Santa Fe Institute Events Wiki - User contributions [en]

Presentations 2012

2012-06-27T18:54:02Z

GeGoerg:

{{Complex Systems Summer School 2012}}

9:00 - 9:15: Christa Brelsford and Xin Lu: Changes in Social Network Structure in Response to Crisis: Using Twitter data to Explore the Effect of the Tōhoku Earthquake.

9:15 - 9:30: Piotr Milanowski and Georg F. Weber: Enzyme kinetics and the outcome of chemical reactions.

9:30 - 9:45: Fabio, Elena, Tom and Friederike: Collaboration in times of stress: an Agent Based Modelling approach

9:45 - 10:00: Joanne, Vikram, Matteo, Sanith: Price-time Dynamics of Contracts Traded on Prediction Markets

10:15 - 10:45: BREAK

10:45 - 11:00: Katrien, Jasmeen, Sandro, Cameron, Vanessa

11:15 - 11:30: Xue, Χλοε, Xiaoli

12:00 - 1:15: LUNCH

1:15 - 1:30: Andres, Charlie, Gareth, and Nic G: We Got the Skills to Pay the Bills - Exploring the Link Between Occupation Diversity and Innovation

1:30 - 1:45: Xin and Abby

1:45 - 2:00: Sepehr

2:00 - 2:15: Ben, Laurent, Oscar, Georg: The Targeting and Timing of Treatment Influences the Emergence of Influenza Resistance in Structured Populations

2:15 - 2:30: Georg, Ben, Laurent, Oscar: Escaping the Poverty Trap: Modeling the Interplay Between Economic Growth and the Ecology of Infectious Disease

2:45 - 3:00: BREAK

5:00: Final Remarks & Farewell Dinner

Presentations 2012

2012-06-27T18:52:43Z

GeGoerg:

Complex Systems Summer School 2012-After Hours

2012-06-21T16:11:03Z

GeGoerg:

{{Complex Systems Summer School 2012}}

Use this space to organize your own after hours activities.

==Rodeo de Santa Fe==

We are planning to head to the [http://rodeodesantafe.org/ Rodeo] today (June 21) at 6:00p.m. please meet drivers in the parking circle and post your car if you can drive:

Car 1: Juniper's Car 

1. Katrien

2. Nick A.

3. Xue

4. Mikkel

Car 1: JP's Car 

1. Matteo

2. Ben

3. Laurent

4. Marco

'''People who still need rides'''

1. Georg G.

2.

==Trip to Taos and alpaca farm==
I am planning to rent a car to visit Taos and a alpaca farm this saturday June 23.
The alpaca farm in my plan is Victory Ranch. http://victoryranch.com/
The car should be able to carry 5 people and let's share the cost.
1. Jianfeng Xu
2. Xin lu
3. Si Tang
4. Xiaoli Dong

==Trip to a museum==
On weekend (Saturday?) I'm planning to visit the Georgia O'Keeffe Museum; after that I'm going to see some more of Santa Fe (no plan yet -- waiting for proposals). Anyone interested?
Cheers,
Piotr

==Road Trip to Boulder, Colorado==

From Gareth: Hi all, I'm planning on making a trip up to Boulder, CO for the weekend. It's about a 6 1/2 hr drive from Santa Fe and I'll be renting a car. My main reason for the trip is to see a friend of mine so you might have to sort your own accommodation (camping/youth hostel/hotel). We're planning on a bit of hiking nearby. The plan is to leave straight from class on Friday evening and arrive back in Santa Fe on Sunday eve. If you're interested in splitting petrol and rental fee and joining me for some Springsteen singalongs then sign up:

1.

2.

3.

4.

==Some Banjo fun out on the town==

My brother will be having a concert this Saturday June 16 at the Second Street Brewery (original location) from 6-9p.m. I will be at the parking circle at 6p.m. For those who do not sign up for a car don't forget Friday and Saturday $5 cabs.

[http://www.secondstreetbrewery.com/2012/05/todd-the-fox-9/ Todd and the Fox Venue Details]

[http://www.toddandthefox.com/fr_home.cfm To hear their music]

If anyone would like to join:

Car 1: Juniper's Car 

1. Katrien (not sure if we'll be back from the lake trip by 6pm. Somebody can take my place if they want.) back up: Georg Weber

2. Marque

3. Sarah

4. Marco

==Dancing==

Swing dancing on Monday 18th June.
Lessons from 7 to 8 P.M.
Dancing from 8 on wards.
The cost is $8 including the lesson and dancing (or $3 for the dancing). Venue: Odd Fellows Hall, 1125 Cerrillos Road. We have not yet decided on transportation. We could either take a cab or walk -- Let's try to decide during dinner.
Sign up below if you are interested:

1.Vikram -- Slightly biased towards taking the lesson. 
2. Xue -- dancing, though not a strong preference. 
3. Mark - I could use a lesson, or twelve. Do we have transportation? 
4. Chloe -- would rather walk down with everyone than skip the lesson. 
5. Aleksandra -- would try lesson, may be stay for dancing. 

PS: If you are on the edge because you want to attend the session on "Introduction to Python". I would be happy to walk you through the basics of Python at a later time. -- Vikram

PPS: how about a Dancing Python lunch tomorrow? I can do intro tutoring too. --Chloe

Other varieties --

There's a contra on the 23rd; swing dancing most Mondays; this is supposed to be a great tango town, and the drop-in-friendly beginner class on Thursday PM was good ($20, though).

[http://www.santafenewmexican.com/Sidebar/Dance_fever_in_Santa_Fe swing, salsa, tango]

[http://www.folkmads.org/may_jun_calendar12.html contras, here and ABQ]

We've heard great Appalachian-style folk musicians here already, but I haven't found a ceili or hoedown locally.

--Chloe

==Trip to Taos==

JP and Tom are going to go to Taos on Saturday 6/16. Sights to see will include the High Road to Taos, Taos Pueblo, the Taos Gorge, Taos Earthships, and the plenty of Taos Hippies.
 
Car 1: JP's Camry 

1.[[JP]] 
2. 
3.Piotr 
4.Matteo 
5.Vikram 
 

Car 2: Tom's Car 
1 
2 Miguel 
3 Riccardo 
4 Priya 

From Andres: I'm sorry... I decided to stay tomorrow at St. John's. I'm very sorry to letting you know so late...! I want to rest, and there is some work I'd like to do...
From Nick: Same for me guys. I feel exhausted. Sorry for telling you so late. Enjoy!

==Trip to Abiquiu==

We are organizing a trip to lake Abiquiu this weekend. ATTENTION ATTENTION! Drivers (Christa, Fabio, John, Tom and David) will meet at 8:30 tomorrow morning (Saturday), and will go with Christa to town to rent 4 cars. We'll pick the others up at 9:30. Those in Christas car, meet at 9am. See you tomorrow!

Fabio

Friederike

Elena

Mikkel

John

Nona

Abby

Marco

Aleksandra

Jasmeen

Dan

Sandro

Ian (if there is any room)

Vanessa (ditto)

Christa (my car seats 5 including me, but I want to stop by Los Alamos to pick up my dog on the way. That adds ~30 min to the drive. "Christa's Honda has manual transmission. do we need a second driver on the car who can drive a stick shift car?" "Yes"- Christa

1. Christa

2. Xue (though I'm also willing to be a driver if necessary)

3. Katrien

4. Jianfeng Xu

5. Xin

Tom

Nick A

==Bandelier Field Trip==

Bandelier Field Trip
Trip to Bandelier National Monument on Sat. June 9.
We might string a visit to the Valles Caldera and Bradbury Science Museum/Los Alamos in as well. If another group would like to stay around Bandelier, we can split up.

Head over to the [[Bandelier Trip 2012 | Bandelier Trip]] Page to sign up!

==Mafia==

[[JP]] is a huge fan of Mafia/Werewolf. Let's play a game sometime in the lower commons.

Let's meet Saturday evening at 8:00 in the lower commons for our first game.

- [[Ryan_James|Ryan]] is down for this.

- Jasmeen is also a big fan of Mafia.

- Ian has never played, but is interested

- Vikram is interested in learning the game.

- Tom F. would like to join and can also teach "The Resistance" a very similar game

- Katrien wants to play too

==FOOTBALL!==

Anyone up for a friendly game of soccer? We can check out equipment from the gym.

[Team: Continuous!] 
1. [[Piotr Milanowski|Piotr]] 
2. [[Marco Duenas|Marco]] 
3.[[Oleksandr Ivanov|Alex]] 
4. 
5. 
[Team: Discrete!] 
1. [[Fabio Cresto Aleina|Fabio]] 
2. [[Matteo Chinazzi|Matteo]] 
3.[[JP]] 
4. 
5.

Complex Systems Summer School 2012-Tutorials

2012-06-16T17:30:04Z

GeGoerg:

{{Complex Systems Summer School 2012}}

CSSS participants come from a wide range of disciplines. Participants are encouraged to share their knowledge by organizing their own tutorials.

Also, please post requests for tutorials here.

==Intro to Philosophy of Science==

Hi All, I've heard some interest in having a more structured/rigorous intro to ideas in the philosophy of science. I'd be happy to briefly explain some of the classic theories as well as some more recent views due to, e.g. Bas Van Fraassen, James Ladyman, Nancy Cartwright, and we could have a small (moderated!) group discussion. Sign up if interested... -Jasmeen

==Markov Chain Monte Carlo==
If anyone is interested in talking more about Bayesian methods and MCMC implementation, I'd be happy to put something together. - Keegan

-Hide is interested in this!

-Abby

-Joanne

-Oscar

-Vanessa

-Jasmeen

-Georg

-Nona

==Python, Computational Mechanics, and Information Theory==

There has been interest in more discussion on a number of topics, and so i'm offering to have evening discussions on them. please sign up below so that i can get a feel for the number of people who would be attending. also, please put a preference for what day it should be.

===Python===
Sign up below if you'd like an introduction to basic python programming. Python is a general purpose, very flexible and useful programming language. It is used pretty extensively in scientific computing.

dan wu: wu12345@gmail.com Let me know when we're meeting

- Benji: bzusman@gmail.com 

-Keegan keegan.hines@gmail.com

-Christa (maybe- I'm competent at the very basics, but could still use some help)

- Riccardo: fusaroli@gmail.com

- Oleksandr: krystoferivanov@gmail.com

- Nona: nona.karalashvili@gmail.com

- Xiaoli: xiaolidong2008@gmail.com

- Abby: Abbyhorn@Mit.edu

- Marco: maduenase@gmail.com

- Nick: goudemand at pim dot uzh dot ch

- Fabio: fabio.cresto-aleina@zmaw.de

- Joanne: joannerodrigues@berkeley.edu

- Hide: hi55 at cornell dot edu

- Priya: iitm.priya@gmail.com

- Miguel: miguel.lurgi@icm.csic.es

- Oscar: opatters@asu.edu

- Vanessa: vanferdi at gmail dot com

- Georg G: gmg at stat dot cmu dot edu dot notthelastpart

===Information Theory===
Interested in turning your data into bits, or seeing how the bits over there are related to the bits over here? If so, sign up below.

- Matteo

- Benji: bzusman@gmail.com

-Keegan keegan.hines@gmail.com

-Riccardo

-Christa

- Katrien

- Xiaoli

- Abby

- Jasmeen

- Hide

- Sanith

- Priya

- Georg W.

- Piotr

- Miguel

- Oscar

- Vanessa

- Nona

- Georg G.

===Computational Mechanics===
If you'd like to know more about epsilon machines, measures of complexity, how to go from a map to a machine, i'm happy to discuss it all. 
- Matteo

Yes please! --Chloe

-Keegan keegan.hines@gmail.com

-Christa

-Xiaoli

-Jasmeen

-Hide

-Sanith

- Priya

- Georg W.

-Piotr

-Ian

- Miguel

- Oscar

- Abby

- Georg G.

==Order Book Dynamics: Learn how to trade in 15min==

--I'm happy to repeat this -just get in touch with me.--

If you are curious about how stocks trade and want to try your luck, I'll
be going over some of the basics with a hands-on example.

Meet at 7.30pm in main lecture hall Thursday June 7th. Please make sure
to bring your laptop.

Sanith

==Update==

Hi guys

I guess the mail lecture hall is free at 6.30 pm and so we can meet there. I have a small talk through the idea and then maybe we can try out getting bifurcation plots for one or two systems. I use software written in MATLAB for the demo but the idea can be implemented in any software.

Here's a link to the [[software for numerical continuation|http://twr.cs.kuleuven.be//research/software/delay/notice.shtml]]. This software can handle systems with time delays and can be used to obtain the bifurcation behaviour.

(1) Please download the software DDE-BIFTOOL and unzip.

(2) Within the folder, you will find another zipped folder in small case ‘ddebiftool’. Please unzip this into a folder with the same name

(3) Add the location of the folder to the path of MATLAB.
I have also sent out a mail with a zipped folder containing files to get a bifurcation plot for a Rijke tube system.
Do let me know if anyone did not get this.
See you there!

Priya

==An easier way to get a bifurcation plot==

Hey guys
I have been working in the past in obtaining bifurcation plots for different physical systems. There's a better way to get these pictures instead of getting the evolution at every parameter value of interest. This is called ''numerical continuation'' and basically involves tracking a curve. I plan to give a informal talk on how to do this and maybe even a demo on applying this technique on 14th June at 6.30 pm. Do mail me if you are interested at '''iitm.priya@gmail.com''' or sign up below.
[[User:Priya|Priya]]

- [[Ryan James|Ryan]] is interested in this. 
- Riccardo is interested in this. 
- Vikram is interested in this. 
- Matteo is interested in this. 
- Hide is interested in this. 
- Katrien wants to hear more about this. 
- Cameron is interested in this and would like to see [http://itunes.apple.com/us/app/xpp/id433859546?mt=8 this] in action if anyone has an iPad. 
- Oscar is interested in this. 
- Charlie is interested in this. (I've done this once for a paper.) 
- Georg W. is interested in this 

==Using Tisean on R on OS X==
first install TISEAN, like you've done already. Make sure R is up to date (v 2.15).
you can try to install RTisean from CRAN
install.packages("RTisean"); library(RTisean); henon()
But me and Dave both got an error about some bad file name, something about con and something about a long path involving -Tmp-
Solution:
remove.packages("RTisean")
restart R
download this: http://cl.ly/0I0b2P2L311y1q0q0y0n
install it, it changes line 74 of nativeTISEAN.R which has some problems with file handling
If you are golden, then:
> henon()
V1 V2
[1,] -0.1232481 -1.030383

Complex Systems Summer School 2012-After Hours

2012-06-08T01:52:04Z

GeGoerg:

{{Complex Systems Summer School 2012}}

Use this space to organize your own after hours activities.

==Santa Fe Brewing Company==

Hi all some of us will be heading to the [http://www.santafebrewing.com/ Santa Fe Brewing Company] tonight at 8:00p.m. Meet in the coffee shop.

- count [[Ryan James|ryan]] in.

Woo! I'll bring my car [[jp]]

JP's Camry

1.JP

2. Ryan James

3. Georg Goerg

4.

5.

Juniper's Car

1.Juniper

2.

3.

4.

5.

==Bandelier Field Trip==

Bandelier Field Trip
Trip to Bandelier National Monument on Sat. June 9.
We might string a visit to the Valles Caldera and Bradbury Science Museum/Los Alamos in as well. If another group would like to stay around Bandelier, we can split up.

Head over to the [[Bandelier Trip 2012 | Bandelier Trip]] Page to sign up!

==Mafia==

[[JP]] is a huge fan of Mafia/Werewolf. Let's play a game sometime in the lower commons.

Let's meet Saturday evening at 8:00 in the lower commons for our first game.

- [[Ryan_James|Ryan]] is down for this.

- Jasmeen is also a big fan of Mafia.

- Ian has never played, but is interested

- Vikram is interested in learning the game.

- Tom F. would like to join and can also teach "The Resistance" a very similar game

==FOOTBALL!==

Anyone up for a friendly game of soccer? We can check out equipment from the gym.

[Team: Continuous!] 
1. [[Piotr Milanowski|Piotr]] 
2. [[Marco Duenas|Marco]] 
3.[[Oleksandr Ivanov|Alex]] 
4. 
5. 
[Team: Discrete!] 
1. [[Fabio Cresto Aleina|Fabio]] 
2. [[Matteo Chinazzi|Matteo]] 
3.[[JP]] 
4. 
5.

Bandelier Trip 2012

2012-06-07T01:44:08Z

GeGoerg:

{{Complex Systems Summer School 2012}}

Please sign up here so we know who's going. 
Also: If you have a car, please let us know. The more cars, the more people. 

We'll meet Saturday at 10:00am in the parking circle.

Please remember to bring a hat, sunscreen, water, hiking shoes, and anything else you'll need for a day out in the field.

==Cars:==

===Tom's Sedan: 4 seats===
1. [[Nicholas Allgaier]] 

2. Vikram Vijayaraghavan 

3. Katrien Beuls 

4. Riccardo Fusaroli 

===John Paul's Camry: 4 (maybe 5) seats===

1. [[John Paul]] 

2. [[Matteo Chinazzi]] 

3. [[Chloe Lewis]] 

4. [[Xue Feng]] 

5. [[Joanne Rodrigues]] 

===Juniper's Car: 4 seats===

1. Jasmeen Kanwal

2. Sarah Tweedt

3. Mark Longo

4. Hide Inamine

===[http://tuvalu.santafe.edu/events/workshops/index.php/Christa_Brelsford Christa]'s Car: 4 (maybe 5) seats===

1. Christa

2. Nicolas Goudemand

3. Marco

4. [http://tuvalu.santafe.edu/events/workshops/index.php/Xin_Lu Xin]

5(middle seat in a 2 door civic). [[Miguel Lurgi]]

===STILL NEEDS A SEAT!===
1. Priya Subramanian

2. [[Piotr Milanowski | Piotr]]

3. Georg M Goerg

Georg M Goerg

2012-06-06T04:44:52Z

GeGoerg: /* SFI Project: Traffic pattern analysis - Can we estimate car velocity by only observing car counts? */

[[File:RapaNui2.jpg|thumb|left|alt=Me in Rapa Nui.|Me (left).]]

== My path to SFI ==
I am a PhD candidate (starting 4th year) in [http://www.stat.cmu.edu/ Statistics at Carnegie Mellon]. I received my masters in mathematics (applied / econometrics / time series) from the Vienna University of Technology, Austria and before coming to the US, I spent a year in Chile teaching statistics (mainly time series) at PUC. For more details you can visit [http://www.stat.cmu.edu/~gmg/ my website]. You can email me at "my_3_initials_in_lowercase"@stat.cmu.edu.

I am very eager to participate in the CSSS; especially because of the inter-disciplinary research / collaborations on real world problems with people from many backgrounds - that's what statistics is all about (at least for me). So I am looking forward to meeting all of you and I am sure we'll have a great month ahead of us.

== Research Interests ==

In my thesis I work on local statistical complexity (LSC) - a measure of
''interestingness'' for spatio-temporal fields. We develop the
statistical methods and algorithms to i) forecast a spatio-temporal
system, and ii) discover patterns automatically solely from the data. We
do this using modern non-parametric statistical / machine learning
techniques with good properties for any kind of (complex)
spatio-temporal system.

One reason why I work on spatio-temporal systems is that I have always been drawn to time series (a la "My interest lies in the future because I am going to spend the rest of my life there. ” - Charles F. Kettering) and methods that try to solve real-world problems. These include time series clustering, forecasting, blind source separation techniques for forecastable time series, time-varying parameter models. Another side-project are skewed and heavy-tailed distributions, in particular how we can transform random variables to introduce skewness and heavy tails. And as a statistician what's even more relevant to me is how can I reverse this transformation so I can take data and remove skewness, remove power laws, remove heavy tails.

I do all my statistical computing in R -- for user-friendly code and R packages (two so far), and Python -- for huge data tasks.

In my spare time I like to play soccer, volleyball, salsa dancing, traveling, ...

== SFI Project: Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ==

'''Disclaimer:''' The model/framework I am thinking about can be applied to many systems where one observes only an overall ensemble average (number of ``particles`` in a system) at each time point t, which is the sum of all particles/entities that have ``survived`` until time t (see also [[#Conceptual View|Conceptual View]] below). However, the really interesting stuff goes on in the individual entities / particles (how long are they alive typically?). So the traffic example is just one of many where these ideas could be applied; if you have a similar situation and you have real-world data then I would have no problem to change the focus on your particular problem if it fits this framework.

==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how I would approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

==== Existing approaches ====

First of all I am not a civil engineer or working in public policy, so I am not aware of the current state of technology / ``art``. So if you happen to know of reference that exactly approach it this way please let me know.

# [http://www.jds-online.com/file_download/49/JDS-159.pdf Hazelton] tries to do something similar, but their methods uses more observables than just the counts (they also use occupancy rates). Nevertheless this would be I guess a starting point for the project.
# [http://www.webs1.uidaho.edu/niatt_labmanual/Chapters/trafficflowtheory/professionalpractice/TrafficFlowParameters.htm A glossary of traffic analysis terms]
# [http://faculty.washington.edu/yinhai/wangpublication_files/TRB_00_SP.pdf Freeway Traffic Speed Estimation Using Single Loop Outputs]
# [http://ftp.jrc.es/EURdoc/JRC47967.TN.pdf Road Traffic Data: Collection Methods and Applications]: contains many sources of information and existing real-world approaches / technologies. Includes references to online data-sources.

=== Math / Statistics ===

==== Conceptual view ====

[http://www.jstor.org/stable/10.2307/2646712 Parke] proposes an error duration model (EDM) for how time series observed in a system happen to form, which is very different to the typical [http://en.wikipedia.org/wiki/Autoregressive_model auto-regressive] (moving-average) explanation of stochastic phenomena:

''The basic mechanism for an error duration model is a sequence of shocks of stochastic magnitude and stochastic duration. The variable observed in a given period is the sum of those shocks that survive to that point.''

The point of this formulation is that the distribution of the (unobserved) survival times determines the correlation structure of the observed series. Thus vice-versa we should be able to infer the lifetime distribution of the shocks from the correlation structure. The point of this is that in practice we don't observe neither the individual shocks nor their lifetime, but we can estimate the correlations of the observations.
Thus in principle it should be possible to infer/estimate the lifetime distribution only from the counts.

==== Formal details ====

Follows later or link to external pdf.

Georg M Goerg

2012-06-06T04:44:17Z

GeGoerg: /* SFI Project: Traffic pattern analysis - Can we estimate car velocity by only observing car counts? */

[[File:RapaNui2.jpg|thumb|left|alt=Me in Rapa Nui.|Me (left).]]

== My path to SFI ==
I am a PhD candidate (starting 4th year) in [http://www.stat.cmu.edu/ Statistics at Carnegie Mellon]. I received my masters in mathematics (applied / econometrics / time series) from the Vienna University of Technology, Austria and before coming to the US, I spent a year in Chile teaching statistics (mainly time series) at PUC. For more details you can visit [http://www.stat.cmu.edu/~gmg/ my website]. You can email me at "my_3_initials_in_lowercase"@stat.cmu.edu.

I am very eager to participate in the CSSS; especially because of the inter-disciplinary research / collaborations on real world problems with people from many backgrounds - that's what statistics is all about (at least for me). So I am looking forward to meeting all of you and I am sure we'll have a great month ahead of us.

== Research Interests ==

In my thesis I work on local statistical complexity (LSC) - a measure of
''interestingness'' for spatio-temporal fields. We develop the
statistical methods and algorithms to i) forecast a spatio-temporal
system, and ii) discover patterns automatically solely from the data. We
do this using modern non-parametric statistical / machine learning
techniques with good properties for any kind of (complex)
spatio-temporal system.

One reason why I work on spatio-temporal systems is that I have always been drawn to time series (a la "My interest lies in the future because I am going to spend the rest of my life there. ” - Charles F. Kettering) and methods that try to solve real-world problems. These include time series clustering, forecasting, blind source separation techniques for forecastable time series, time-varying parameter models. Another side-project are skewed and heavy-tailed distributions, in particular how we can transform random variables to introduce skewness and heavy tails. And as a statistician what's even more relevant to me is how can I reverse this transformation so I can take data and remove skewness, remove power laws, remove heavy tails.

I do all my statistical computing in R -- for user-friendly code and R packages (two so far), and Python -- for huge data tasks.

In my spare time I like to play soccer, volleyball, salsa dancing, traveling, ...

== SFI Project: Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ==

'''Disclaimer:''' The model I am thinking about to use can be applied to many systems where one observes only an overall ensemble average (number of ``particles`` in a system) at each time point t, which is the sum of all particles/entities that have ``survived`` until time t (see also [[#Conceptual View|Conceptual View]] below). However, the really interesting stuff goes on in the individual entities / particles (how long are they alive typically?). So the traffic example is just one of many where these ideas could be applied; if you have a similar situation and you have real-world data then I would have no problem to change the focus on your particular problem if it fits this framework.

==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how I would approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

==== Existing approaches ====

First of all I am not a civil engineer or working in public policy, so I am not aware of the current state of technology / ``art``. So if you happen to know of reference that exactly approach it this way please let me know.

# [http://www.jds-online.com/file_download/49/JDS-159.pdf Hazelton] tries to do something similar, but their methods uses more observables than just the counts (they also use occupancy rates). Nevertheless this would be I guess a starting point for the project.
# [http://www.webs1.uidaho.edu/niatt_labmanual/Chapters/trafficflowtheory/professionalpractice/TrafficFlowParameters.htm A glossary of traffic analysis terms]
# [http://faculty.washington.edu/yinhai/wangpublication_files/TRB_00_SP.pdf Freeway Traffic Speed Estimation Using Single Loop Outputs]
# [http://ftp.jrc.es/EURdoc/JRC47967.TN.pdf Road Traffic Data: Collection Methods and Applications]: contains many sources of information and existing real-world approaches / technologies. Includes references to online data-sources.

=== Math / Statistics ===

==== Conceptual view ====

[http://www.jstor.org/stable/10.2307/2646712 Parke] proposes an error duration model (EDM) for how time series observed in a system happen to form, which is very different to the typical [http://en.wikipedia.org/wiki/Autoregressive_model auto-regressive] (moving-average) explanation of stochastic phenomena:

''The basic mechanism for an error duration model is a sequence of shocks of stochastic magnitude and stochastic duration. The variable observed in a given period is the sum of those shocks that survive to that point.''

The point of this formulation is that the distribution of the (unobserved) survival times determines the correlation structure of the observed series. Thus vice-versa we should be able to infer the lifetime distribution of the shocks from the correlation structure. The point of this is that in practice we don't observe neither the individual shocks nor their lifetime, but we can estimate the correlations of the observations.
Thus in principle it should be possible to infer/estimate the lifetime distribution only from the counts.

==== Formal details ====

Follows later or link to external pdf.

Georg M Goerg

2012-06-06T04:42:35Z

GeGoerg: /* SFI Project: Traffic pattern analysis - Can we estimate car velocity by only observing car counts? */

[[File:RapaNui2.jpg|thumb|left|alt=Me in Rapa Nui.|Me (left).]]

== My path to SFI ==
I am a PhD candidate (starting 4th year) in [http://www.stat.cmu.edu/ Statistics at Carnegie Mellon]. I received my masters in mathematics (applied / econometrics / time series) from the Vienna University of Technology, Austria and before coming to the US, I spent a year in Chile teaching statistics (mainly time series) at PUC. For more details you can visit [http://www.stat.cmu.edu/~gmg/ my website]. You can email me at "my_3_initials_in_lowercase"@stat.cmu.edu.

I am very eager to participate in the CSSS; especially because of the inter-disciplinary research / collaborations on real world problems with people from many backgrounds - that's what statistics is all about (at least for me). So I am looking forward to meeting all of you and I am sure we'll have a great month ahead of us.

== Research Interests ==

In my thesis I work on local statistical complexity (LSC) - a measure of
''interestingness'' for spatio-temporal fields. We develop the
statistical methods and algorithms to i) forecast a spatio-temporal
system, and ii) discover patterns automatically solely from the data. We
do this using modern non-parametric statistical / machine learning
techniques with good properties for any kind of (complex)
spatio-temporal system.

One reason why I work on spatio-temporal systems is that I have always been drawn to time series (a la "My interest lies in the future because I am going to spend the rest of my life there. ” - Charles F. Kettering) and methods that try to solve real-world problems. These include time series clustering, forecasting, blind source separation techniques for forecastable time series, time-varying parameter models. Another side-project are skewed and heavy-tailed distributions, in particular how we can transform random variables to introduce skewness and heavy tails. And as a statistician what's even more relevant to me is how can I reverse this transformation so I can take data and remove skewness, remove power laws, remove heavy tails.

I do all my statistical computing in R -- for user-friendly code and R packages (two so far), and Python -- for huge data tasks.

In my spare time I like to play soccer, volleyball, salsa dancing, traveling, ...

== SFI Project: Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ==

'''Disclaimer:''' The model I am thinking about to use can be applied to many systems where one observes only an overall ensemble average (number of ``particles`` in a system) at each time point t, which is the sum of all particles/entities that have ``survived`` until time t (see also [Conceptual View|Conceptual View] below). However, the really interesting stuff goes on in the individual entities / particles (how long are they alive typically?). So the traffic example is just one of many where these ideas could be applied; if you have a similar situation and you have real-world data then I would have no problem to change the focus on your particular problem if it fits this framework.

==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how I would approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

==== Existing approaches ====

First of all I am not a civil engineer or working in public policy, so I am not aware of the current state of technology / ``art``. So if you happen to know of reference that exactly approach it this way please let me know.

# [http://www.jds-online.com/file_download/49/JDS-159.pdf Hazelton] tries to do something similar, but their methods uses more observables than just the counts (they also use occupancy rates). Nevertheless this would be I guess a starting point for the project.
# [http://www.webs1.uidaho.edu/niatt_labmanual/Chapters/trafficflowtheory/professionalpractice/TrafficFlowParameters.htm A glossary of traffic analysis terms]
# [http://faculty.washington.edu/yinhai/wangpublication_files/TRB_00_SP.pdf Freeway Traffic Speed Estimation Using Single Loop Outputs]
# [http://ftp.jrc.es/EURdoc/JRC47967.TN.pdf Road Traffic Data: Collection Methods and Applications]: contains many sources of information and existing real-world approaches / technologies. Includes references to online data-sources.

=== Math / Statistics ===

==== Conceptual view ====

[http://www.jstor.org/stable/10.2307/2646712 Parke] proposes an error duration model (EDM) for how time series observed in a system happen to form, which is very different to the typical [http://en.wikipedia.org/wiki/Autoregressive_model auto-regressive] (moving-average) explanation of stochastic phenomena:

''The basic mechanism for an error duration model is a sequence of shocks of stochastic magnitude and stochastic duration. The variable observed in a given period is the sum of those shocks that survive to that point.''

The point of this formulation is that the distribution of the (unobserved) survival times determines the correlation structure of the observed series. Thus vice-versa we should be able to infer the lifetime distribution of the shocks from the correlation structure. The point of this is that in practice we don't observe neither the individual shocks nor their lifetime, but we can estimate the correlations of the observations.
Thus in principle it should be possible to infer/estimate the lifetime distribution only from the counts.

==== Formal details ====

Follows later or link to external pdf.

Georg M Goerg

2012-06-06T04:41:53Z

GeGoerg: /* SFI Project: Traffic pattern analysis - Can we estimate car velocity by only observing car counts? = */

[[File:RapaNui2.jpg|thumb|left|alt=Me in Rapa Nui.|Me (left).]]

== My path to SFI ==
I am a PhD candidate (starting 4th year) in [http://www.stat.cmu.edu/ Statistics at Carnegie Mellon]. I received my masters in mathematics (applied / econometrics / time series) from the Vienna University of Technology, Austria and before coming to the US, I spent a year in Chile teaching statistics (mainly time series) at PUC. For more details you can visit [http://www.stat.cmu.edu/~gmg/ my website]. You can email me at "my_3_initials_in_lowercase"@stat.cmu.edu.

I am very eager to participate in the CSSS; especially because of the inter-disciplinary research / collaborations on real world problems with people from many backgrounds - that's what statistics is all about (at least for me). So I am looking forward to meeting all of you and I am sure we'll have a great month ahead of us.

== Research Interests ==

In my thesis I work on local statistical complexity (LSC) - a measure of
''interestingness'' for spatio-temporal fields. We develop the
statistical methods and algorithms to i) forecast a spatio-temporal
system, and ii) discover patterns automatically solely from the data. We
do this using modern non-parametric statistical / machine learning
techniques with good properties for any kind of (complex)
spatio-temporal system.

One reason why I work on spatio-temporal systems is that I have always been drawn to time series (a la "My interest lies in the future because I am going to spend the rest of my life there. ” - Charles F. Kettering) and methods that try to solve real-world problems. These include time series clustering, forecasting, blind source separation techniques for forecastable time series, time-varying parameter models. Another side-project are skewed and heavy-tailed distributions, in particular how we can transform random variables to introduce skewness and heavy tails. And as a statistician what's even more relevant to me is how can I reverse this transformation so I can take data and remove skewness, remove power laws, remove heavy tails.

I do all my statistical computing in R -- for user-friendly code and R packages (two so far), and Python -- for huge data tasks.

In my spare time I like to play soccer, volleyball, salsa dancing, traveling, ...

== SFI Project: Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ==

'''Disclaimer:''' The model I am thinking about to use can be applied to many systems where one observes only an overall ensemble average (number of ``particles`` in a system) at each time point t, which is the sum of all particles/entities that have ``survived`` until time t (see also [#Conceptual View] below). However, the really interesting stuff goes on in the individual entities / particles (how long are they alive typically?). So the traffic example is just one of many where these ideas could be applied; if you have a similar situation and you have real-world data then I would have no problem to change the focus on your particular problem if it fits this framework.

==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how I would approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

==== Existing approaches ====

First of all I am not a civil engineer or working in public policy, so I am not aware of the current state of technology / ``art``. So if you happen to know of reference that exactly approach it this way please let me know.

# [http://www.jds-online.com/file_download/49/JDS-159.pdf Hazelton] tries to do something similar, but their methods uses more observables than just the counts (they also use occupancy rates). Nevertheless this would be I guess a starting point for the project.
# [http://www.webs1.uidaho.edu/niatt_labmanual/Chapters/trafficflowtheory/professionalpractice/TrafficFlowParameters.htm A glossary of traffic analysis terms]
# [http://faculty.washington.edu/yinhai/wangpublication_files/TRB_00_SP.pdf Freeway Traffic Speed Estimation Using Single Loop Outputs]
# [http://ftp.jrc.es/EURdoc/JRC47967.TN.pdf Road Traffic Data: Collection Methods and Applications]: contains many sources of information and existing real-world approaches / technologies. Includes references to online data-sources.

=== Math / Statistics ===

==== Conceptual view ====

[http://www.jstor.org/stable/10.2307/2646712 Parke] proposes an error duration model (EDM) for how time series observed in a system happen to form, which is very different to the typical [http://en.wikipedia.org/wiki/Autoregressive_model auto-regressive] (moving-average) explanation of stochastic phenomena:

''The basic mechanism for an error duration model is a sequence of shocks of stochastic magnitude and stochastic duration. The variable observed in a given period is the sum of those shocks that survive to that point.''

The point of this formulation is that the distribution of the (unobserved) survival times determines the correlation structure of the observed series. Thus vice-versa we should be able to infer the lifetime distribution of the shocks from the correlation structure. The point of this is that in practice we don't observe neither the individual shocks nor their lifetime, but we can estimate the correlations of the observations.
Thus in principle it should be possible to infer/estimate the lifetime distribution only from the counts.

==== Formal details ====

Follows later or link to external pdf.

Georg M Goerg

2012-06-06T04:01:04Z

GeGoerg: /* Existing approaches */

[[File:RapaNui2.jpg|thumb|left|alt=Me in Rapa Nui.|Me (left).]]

== My path to SFI ==
I am a PhD candidate (starting 4th year) in [http://www.stat.cmu.edu/ Statistics at Carnegie Mellon]. I received my masters in mathematics (applied / econometrics / time series) from the Vienna University of Technology, Austria and before coming to the US, I spent a year in Chile teaching statistics (mainly time series) at PUC. For more details you can visit [http://www.stat.cmu.edu/~gmg/ my website]. You can email me at "my_3_initials_in_lowercase"@stat.cmu.edu.

I am very eager to participate in the CSSS; especially because of the inter-disciplinary research / collaborations on real world problems with people from many backgrounds - that's what statistics is all about (at least for me). So I am looking forward to meeting all of you and I am sure we'll have a great month ahead of us.

== Research Interests ==

In my thesis I work on local statistical complexity (LSC) - a measure of
''interestingness'' for spatio-temporal fields. We develop the
statistical methods and algorithms to i) forecast a spatio-temporal
system, and ii) discover patterns automatically solely from the data. We
do this using modern non-parametric statistical / machine learning
techniques with good properties for any kind of (complex)
spatio-temporal system.

One reason why I work on spatio-temporal systems is that I have always been drawn to time series (a la "My interest lies in the future because I am going to spend the rest of my life there. ” - Charles F. Kettering) and methods that try to solve real-world problems. These include time series clustering, forecasting, blind source separation techniques for forecastable time series, time-varying parameter models. Another side-project are skewed and heavy-tailed distributions, in particular how we can transform random variables to introduce skewness and heavy tails. And as a statistician what's even more relevant to me is how can I reverse this transformation so I can take data and remove skewness, remove power laws, remove heavy tails.

I do all my statistical computing in R -- for user-friendly code and R packages (two so far), and Python -- for huge data tasks.

In my spare time I like to play soccer, volleyball, salsa dancing, traveling, ...

== SFI Project: Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how I would approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

==== Existing approaches ====

First of all I am not a civil engineer or working in public policy, so I am not aware of the current state of technology / ``art``. So if you happen to know of reference that exactly approach it this way please let me know.

# [http://www.jds-online.com/file_download/49/JDS-159.pdf Hazelton] tries to do something similar, but their methods uses more observables than just the counts (they also use occupancy rates). Nevertheless this would be I guess a starting point for the project.
# [http://www.webs1.uidaho.edu/niatt_labmanual/Chapters/trafficflowtheory/professionalpractice/TrafficFlowParameters.htm A glossary of traffic analysis terms]
# [http://faculty.washington.edu/yinhai/wangpublication_files/TRB_00_SP.pdf Freeway Traffic Speed Estimation Using Single Loop Outputs]
# [http://ftp.jrc.es/EURdoc/JRC47967.TN.pdf Road Traffic Data: Collection Methods and Applications]: contains many sources of information and existing real-world approaches / technologies. Includes references to online data-sources.

=== Math / Statistics ===

==== Conceptual view ====

[http://www.jstor.org/stable/10.2307/2646712 Parke] proposes an error duration model (EDM) for how time series observed in a system happen to form, which is very different to the typical [http://en.wikipedia.org/wiki/Autoregressive_model auto-regressive] (moving-average) explanation of stochastic phenomena:

''The basic mechanism for an error duration model is a sequence of shocks of stochastic magnitude and stochastic duration. The variable observed in a given period is the sum of those shocks that survive to that point.''

The point of this formulation is that the distribution of the (unobserved) survival times determines the correlation structure of the observed series. Thus vice-versa we should be able to infer the lifetime distribution of the shocks from the correlation structure. The point of this is that in practice we don't observe neither the individual shocks nor their lifetime, but we can estimate the correlations of the observations.
Thus in principle it should be possible to infer/estimate the lifetime distribution only from the counts.

==== Formal details ====

Follows later or link to external pdf.

Georg M Goerg

2012-06-06T04:00:44Z

GeGoerg: /* Existing approaches */

[[File:RapaNui2.jpg|thumb|left|alt=Me in Rapa Nui.|Me (left).]]

== My path to SFI ==
I am a PhD candidate (starting 4th year) in [http://www.stat.cmu.edu/ Statistics at Carnegie Mellon]. I received my masters in mathematics (applied / econometrics / time series) from the Vienna University of Technology, Austria and before coming to the US, I spent a year in Chile teaching statistics (mainly time series) at PUC. For more details you can visit [http://www.stat.cmu.edu/~gmg/ my website]. You can email me at "my_3_initials_in_lowercase"@stat.cmu.edu.

I am very eager to participate in the CSSS; especially because of the inter-disciplinary research / collaborations on real world problems with people from many backgrounds - that's what statistics is all about (at least for me). So I am looking forward to meeting all of you and I am sure we'll have a great month ahead of us.

== Research Interests ==

In my thesis I work on local statistical complexity (LSC) - a measure of
''interestingness'' for spatio-temporal fields. We develop the
statistical methods and algorithms to i) forecast a spatio-temporal
system, and ii) discover patterns automatically solely from the data. We
do this using modern non-parametric statistical / machine learning
techniques with good properties for any kind of (complex)
spatio-temporal system.

One reason why I work on spatio-temporal systems is that I have always been drawn to time series (a la "My interest lies in the future because I am going to spend the rest of my life there. ” - Charles F. Kettering) and methods that try to solve real-world problems. These include time series clustering, forecasting, blind source separation techniques for forecastable time series, time-varying parameter models. Another side-project are skewed and heavy-tailed distributions, in particular how we can transform random variables to introduce skewness and heavy tails. And as a statistician what's even more relevant to me is how can I reverse this transformation so I can take data and remove skewness, remove power laws, remove heavy tails.

I do all my statistical computing in R -- for user-friendly code and R packages (two so far), and Python -- for huge data tasks.

In my spare time I like to play soccer, volleyball, salsa dancing, traveling, ...

== SFI Project: Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how I would approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

==== Existing approaches ====

First of all I am not a civil engineer or working in public policy, so I am not aware of the current state of technology / ``art``. So first of all, if you happen to know of reference that exactly approach it this way please let me know.

# [http://www.jds-online.com/file_download/49/JDS-159.pdf Hazelton] tries to do something similar, but their methods uses more observables than just the counts (they also use occupancy rates). Nevertheless this would be I guess a starting point for the project.
# [http://www.webs1.uidaho.edu/niatt_labmanual/Chapters/trafficflowtheory/professionalpractice/TrafficFlowParameters.htm A glossary of traffic analysis terms]
# [http://faculty.washington.edu/yinhai/wangpublication_files/TRB_00_SP.pdf Freeway Traffic Speed Estimation Using Single Loop Outputs]
# [http://ftp.jrc.es/EURdoc/JRC47967.TN.pdf Road Traffic Data: Collection Methods and Applications]: contains many sources of information and existing real-world approaches / technologies. Includes references to online data-sources.

=== Math / Statistics ===

==== Conceptual view ====

[http://www.jstor.org/stable/10.2307/2646712 Parke] proposes an error duration model (EDM) for how time series observed in a system happen to form, which is very different to the typical [http://en.wikipedia.org/wiki/Autoregressive_model auto-regressive] (moving-average) explanation of stochastic phenomena:

''The basic mechanism for an error duration model is a sequence of shocks of stochastic magnitude and stochastic duration. The variable observed in a given period is the sum of those shocks that survive to that point.''

The point of this formulation is that the distribution of the (unobserved) survival times determines the correlation structure of the observed series. Thus vice-versa we should be able to infer the lifetime distribution of the shocks from the correlation structure. The point of this is that in practice we don't observe neither the individual shocks nor their lifetime, but we can estimate the correlations of the observations.
Thus in principle it should be possible to infer/estimate the lifetime distribution only from the counts.

==== Formal details ====

Follows later or link to external pdf.

Georg M Goerg

2012-06-06T03:56:18Z

GeGoerg: /* SFI Project: Traffic pattern analysis - Can we estimate car velocity by only observing car counts? = */

[[File:RapaNui2.jpg|thumb|left|alt=Me in Rapa Nui.|Me (left).]]

== My path to SFI ==
I am a PhD candidate (starting 4th year) in [http://www.stat.cmu.edu/ Statistics at Carnegie Mellon]. I received my masters in mathematics (applied / econometrics / time series) from the Vienna University of Technology, Austria and before coming to the US, I spent a year in Chile teaching statistics (mainly time series) at PUC. For more details you can visit [http://www.stat.cmu.edu/~gmg/ my website]. You can email me at "my_3_initials_in_lowercase"@stat.cmu.edu.

I am very eager to participate in the CSSS; especially because of the inter-disciplinary research / collaborations on real world problems with people from many backgrounds - that's what statistics is all about (at least for me). So I am looking forward to meeting all of you and I am sure we'll have a great month ahead of us.

== Research Interests ==

In my thesis I work on local statistical complexity (LSC) - a measure of
''interestingness'' for spatio-temporal fields. We develop the
statistical methods and algorithms to i) forecast a spatio-temporal
system, and ii) discover patterns automatically solely from the data. We
do this using modern non-parametric statistical / machine learning
techniques with good properties for any kind of (complex)
spatio-temporal system.

One reason why I work on spatio-temporal systems is that I have always been drawn to time series (a la "My interest lies in the future because I am going to spend the rest of my life there. ” - Charles F. Kettering) and methods that try to solve real-world problems. These include time series clustering, forecasting, blind source separation techniques for forecastable time series, time-varying parameter models. Another side-project are skewed and heavy-tailed distributions, in particular how we can transform random variables to introduce skewness and heavy tails. And as a statistician what's even more relevant to me is how can I reverse this transformation so I can take data and remove skewness, remove power laws, remove heavy tails.

I do all my statistical computing in R -- for user-friendly code and R packages (two so far), and Python -- for huge data tasks.

In my spare time I like to play soccer, volleyball, salsa dancing, traveling, ...

== SFI Project: Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how I would approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

==== Existing approaches ====

First of all I am not a civil engineer or working in public policy, so I am not aware of the current state of technology / ``art``. So first of all, if you happen to know of reference that exactly approach it this way please let me know.

* [http://www.jds-online.com/file_download/49/JDS-159.pdf Hazelton] who tries to do something similar, but their methods uses more observables than just the counts (they also use occupancy rates). Nevertheless this would be I guess a starting point for the project.
* [http://www.webs1.uidaho.edu/niatt_labmanual/Chapters/trafficflowtheory/professionalpractice/TrafficFlowParameters.htm Dictionary of traffic analysis terms]

=== Math / Statistics ===

==== Conceptual view ====

[http://www.jstor.org/stable/10.2307/2646712 Parke] proposes an error duration model (EDM) for how time series observed in a system happen to form, which is very different to the typical [http://en.wikipedia.org/wiki/Autoregressive_model auto-regressive] (moving-average) explanation of stochastic phenomena:

''The basic mechanism for an error duration model is a sequence of shocks of stochastic magnitude and stochastic duration. The variable observed in a given period is the sum of those shocks that survive to that point.''

The point of this formulation is that the distribution of the (unobserved) survival times determines the correlation structure of the observed series. Thus vice-versa we should be able to infer the lifetime distribution of the shocks from the correlation structure. The point of this is that in practice we don't observe neither the individual shocks nor their lifetime, but we can estimate the correlations of the observations.
Thus in principle it should be possible to infer/estimate the lifetime distribution only from the counts.

==== Formal details ====

Follows later or link to external pdf.

Georg M Goerg

2012-06-05T22:36:32Z

GeGoerg: /* Existing approaches */

[[File:RapaNui2.jpg|thumb|left|alt=Me in Rapa Nui.|Me (left).]]

== My path to SFI ==
I am a PhD candidate (starting 4th year) in [http://www.stat.cmu.edu/ Statistics at Carnegie Mellon]. I received my masters in mathematics (applied / econometrics / time series) from the Vienna University of Technology, Austria and before coming to the US, I spent a year in Chile teaching statistics (mainly time series) at PUC. For more details you can visit [http://www.stat.cmu.edu/~gmg/ my website]. You can email me at "my_3_initials_in_lowercase"@stat.cmu.edu.

I am very eager to participate in the CSSS; especially because of the inter-disciplinary research / collaborations on real world problems with people from many backgrounds - that's what statistics is all about (at least for me). So I am looking forward to meeting all of you and I am sure we'll have a great month ahead of us.

== Research Interests ==

In my thesis I work on local statistical complexity (LSC) - a measure of
''interestingness'' for spatio-temporal fields. We develop the
statistical methods and algorithms to i) forecast a spatio-temporal
system, and ii) discover patterns automatically solely from the data. We
do this using modern non-parametric statistical / machine learning
techniques with good properties for any kind of (complex)
spatio-temporal system.

One reason why I work on spatio-temporal systems is that I have always been drawn to time series (a la "My interest lies in the future because I am going to spend the rest of my life there. ” - Charles F. Kettering) and methods that try to solve real-world problems. These include time series clustering, forecasting, blind source separation techniques for forecastable time series, time-varying parameter models. Another side-project are skewed and heavy-tailed distributions, in particular how we can transform random variables to introduce skewness and heavy tails. And as a statistician what's even more relevant to me is how can I reverse this transformation so I can take data and remove skewness, remove power laws, remove heavy tails.

I do all my statistical computing in R -- for user-friendly code and R packages (two so far), and Python -- for huge data tasks.

In my spare time I like to play soccer, volleyball, salsa dancing, traveling, ...

== SFI Project: Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how I would approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

==== Existing approaches ====

First of all I am not a civil engineer or working in public policy, so I am not aware of the current state of technology / ``art``. So first of all, if you happen to know of reference that exactly approach it this way please let me know.

I have only found the work of [http://www.jds-online.com/file_download/49/JDS-159.pdf Hazelton] who tries to do something similar, but their methods uses more observables than just the counts. Nevertheless this would be I guess a starting point for the project.

=== Math / Statistics ===

==== Conceptual view ====

[http://www.jstor.org/stable/10.2307/2646712 Parke] proposes an error duration model (EDM) for how time series observed in a system happen to form, which is very different to the typical [http://en.wikipedia.org/wiki/Autoregressive_model auto-regressive] (moving-average) explanation of stochastic phenomena:

''The basic mechanism for an error duration model is a sequence of shocks of stochastic magnitude and stochastic duration. The variable observed in a given period is the sum of those shocks that survive to that point.''

The point of this formulation is that the distribution of the (unobserved) survival times determines the correlation structure of the observed series. Thus vice-versa we should be able to infer the lifetime distribution of the shocks from the correlation structure. The point of this is that in practice we don't observe neither the individual shocks nor their lifetime, but we can estimate the correlations of the observations.
Thus in principle it should be possible to infer/estimate the lifetime distribution only from the counts.

==== Formal details ====

Follows later or link to external pdf.

Georg M Goerg

2012-06-05T22:36:10Z

GeGoerg:

[[File:RapaNui2.jpg|thumb|left|alt=Me in Rapa Nui.|Me (left).]]

== My path to SFI ==
I am a PhD candidate (starting 4th year) in [http://www.stat.cmu.edu/ Statistics at Carnegie Mellon]. I received my masters in mathematics (applied / econometrics / time series) from the Vienna University of Technology, Austria and before coming to the US, I spent a year in Chile teaching statistics (mainly time series) at PUC. For more details you can visit [http://www.stat.cmu.edu/~gmg/ my website]. You can email me at "my_3_initials_in_lowercase"@stat.cmu.edu.

I am very eager to participate in the CSSS; especially because of the inter-disciplinary research / collaborations on real world problems with people from many backgrounds - that's what statistics is all about (at least for me). So I am looking forward to meeting all of you and I am sure we'll have a great month ahead of us.

== Research Interests ==

In my thesis I work on local statistical complexity (LSC) - a measure of
''interestingness'' for spatio-temporal fields. We develop the
statistical methods and algorithms to i) forecast a spatio-temporal
system, and ii) discover patterns automatically solely from the data. We
do this using modern non-parametric statistical / machine learning
techniques with good properties for any kind of (complex)
spatio-temporal system.

One reason why I work on spatio-temporal systems is that I have always been drawn to time series (a la "My interest lies in the future because I am going to spend the rest of my life there. ” - Charles F. Kettering) and methods that try to solve real-world problems. These include time series clustering, forecasting, blind source separation techniques for forecastable time series, time-varying parameter models. Another side-project are skewed and heavy-tailed distributions, in particular how we can transform random variables to introduce skewness and heavy tails. And as a statistician what's even more relevant to me is how can I reverse this transformation so I can take data and remove skewness, remove power laws, remove heavy tails.

I do all my statistical computing in R -- for user-friendly code and R packages (two so far), and Python -- for huge data tasks.

In my spare time I like to play soccer, volleyball, salsa dancing, traveling, ...

== SFI Project: Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how I would approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

==== Existing approaches ====

First of all I am not a civil engineer or working in public policy, so I am not aware of the current state of technology / ``art``. So first of all, if you happen to know of reference that exactly approach it this way please let me know.

I have only found the work of [www.jds-online.com/file_download/49/JDS-159.pdf Hazelton] who tries to do something similar, but their methods uses more observables than just the counts. Nevertheless this would be I guess a starting point for the project.

=== Math / Statistics ===

==== Conceptual view ====

[http://www.jstor.org/stable/10.2307/2646712 Parke] proposes an error duration model (EDM) for how time series observed in a system happen to form, which is very different to the typical [http://en.wikipedia.org/wiki/Autoregressive_model auto-regressive] (moving-average) explanation of stochastic phenomena:

''The basic mechanism for an error duration model is a sequence of shocks of stochastic magnitude and stochastic duration. The variable observed in a given period is the sum of those shocks that survive to that point.''

The point of this formulation is that the distribution of the (unobserved) survival times determines the correlation structure of the observed series. Thus vice-versa we should be able to infer the lifetime distribution of the shocks from the correlation structure. The point of this is that in practice we don't observe neither the individual shocks nor their lifetime, but we can estimate the correlations of the observations.
Thus in principle it should be possible to infer/estimate the lifetime distribution only from the counts.

==== Formal details ====

Follows later or link to external pdf.

Georg M Goerg

2012-06-05T22:32:48Z

GeGoerg: /* Ideas on how to approach this */

[[File:RapaNui2.jpg|thumb|left|alt=Me in Rapa Nui.|Me (left).]]

== My path to SFI ==
I am a PhD candidate (starting 4th year) in [http://www.stat.cmu.edu/ Statistics at Carnegie Mellon]. I received my masters in mathematics (applied / econometrics / time series) from the Vienna University of Technology, Austria and before coming to the US, I spent a year in Chile teaching statistics (mainly time series) at PUC. For more details you can visit [http://www.stat.cmu.edu/~gmg/ my website]. You can email me at "my_3_initials_in_lowercase"@stat.cmu.edu.

I am very eager to participate in the CSSS; especially because of the inter-disciplinary research / collaborations on real world problems with people from many backgrounds - that's what statistics is all about (at least for me). So I am looking forward to meeting all of you and I am sure we'll have a great month ahead of us.

== Research Interests ==

In my thesis I work on local statistical complexity (LSC) - a measure of
''interestingness'' for spatio-temporal fields. We develop the
statistical methods and algorithms to i) forecast a spatio-temporal
system, and ii) discover patterns automatically solely from the data. We
do this using modern non-parametric statistical / machine learning
techniques with good properties for any kind of (complex)
spatio-temporal system.

One reason why I work on spatio-temporal systems is that I have always been drawn to time series (a la "My interest lies in the future because I am going to spend the rest of my life there. ” - Charles F. Kettering) and methods that try to solve real-world problems. These include time series clustering, forecasting, blind source separation techniques for forecastable time series, time-varying parameter models. Another side-project are skewed and heavy-tailed distributions, in particular how we can transform random variables to introduce skewness and heavy tails. And as a statistician what's even more relevant to me is how can I reverse this transformation so I can take data and remove skewness, remove power laws, remove heavy tails.

I do all my statistical computing in R -- for user-friendly code and R packages (two so far), and Python -- for huge data tasks.

In my spare time I like to play soccer, volleyball, salsa dancing, traveling, ...

== SFI Project: Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how I would approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

=== Math / Statistics ===

==== Conceptual view ====

[http://www.jstor.org/stable/10.2307/2646712 Parke] proposes an error duration model (EDM) for how time series observed in a system happen to form, which is very different to the typical [http://en.wikipedia.org/wiki/Autoregressive_model auto-regressive] (moving-average) explanation of stochastic phenomena:

''The basic mechanism for an error duration model is a sequence of shocks of stochastic magnitude and stochastic duration. The variable observed in a given period is the sum of those shocks that survive to that point.''

The point of this formulation is that the distribution of the (unobserved) survival times determines the correlation structure of the observed series. Thus vice-versa we should be able to infer the lifetime distribution of the shocks from the correlation structure. The point of this is that in practice we don't observe neither the individual shocks nor their lifetime, but we can estimate the correlations of the observations.
Thus in principle it should be possible to infer/estimate the lifetime distribution only from the counts.

==== Formal details ====

Follows later or link to external pdf.

Georg M Goerg

2012-06-05T22:30:27Z

GeGoerg: /* Conceptual view */

[[File:RapaNui2.jpg|thumb|left|alt=Me in Rapa Nui.|Me (left).]]

== My path to SFI ==
I am a PhD candidate (starting 4th year) in [http://www.stat.cmu.edu/ Statistics at Carnegie Mellon]. I received my masters in mathematics (applied / econometrics / time series) from the Vienna University of Technology, Austria and before coming to the US, I spent a year in Chile teaching statistics (mainly time series) at PUC. For more details you can visit [http://www.stat.cmu.edu/~gmg/ my website]. You can email me at "my_3_initials_in_lowercase"@stat.cmu.edu.

I am very eager to participate in the CSSS; especially because of the inter-disciplinary research / collaborations on real world problems with people from many backgrounds - that's what statistics is all about (at least for me). So I am looking forward to meeting all of you and I am sure we'll have a great month ahead of us.

== Research Interests ==

In my thesis I work on local statistical complexity (LSC) - a measure of
''interestingness'' for spatio-temporal fields. We develop the
statistical methods and algorithms to i) forecast a spatio-temporal
system, and ii) discover patterns automatically solely from the data. We
do this using modern non-parametric statistical / machine learning
techniques with good properties for any kind of (complex)
spatio-temporal system.

One reason why I work on spatio-temporal systems is that I have always been drawn to time series (a la "My interest lies in the future because I am going to spend the rest of my life there. ” - Charles F. Kettering) and methods that try to solve real-world problems. These include time series clustering, forecasting, blind source separation techniques for forecastable time series, time-varying parameter models. Another side-project are skewed and heavy-tailed distributions, in particular how we can transform random variables to introduce skewness and heavy tails. And as a statistician what's even more relevant to me is how can I reverse this transformation so I can take data and remove skewness, remove power laws, remove heavy tails.

I do all my statistical computing in R -- for user-friendly code and R packages (two so far), and Python -- for huge data tasks.

In my spare time I like to play soccer, volleyball, salsa dancing, traveling, ...

== SFI Project: Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how to approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

=== Math / Statistics ===

==== Conceptual view ====

[http://www.jstor.org/stable/10.2307/2646712 Parke] proposes an error duration model (EDM) for how time series observed in a system happen to form, which is very different to the typical [http://en.wikipedia.org/wiki/Autoregressive_model auto-regressive] (moving-average) explanation of stochastic phenomena:

''The basic mechanism for an error duration model is a sequence of shocks of stochastic magnitude and stochastic duration. The variable observed in a given period is the sum of those shocks that survive to that point.''

The point of this formulation is that the distribution of the (unobserved) survival times determines the correlation structure of the observed series. Thus vice-versa we should be able to infer the lifetime distribution of the shocks from the correlation structure. The point of this is that in practice we don't observe neither the individual shocks nor their lifetime, but we can estimate the correlations of the observations.
Thus in principle it should be possible to infer/estimate the lifetime distribution only from the counts.

==== Formal details ====

Follows later or link to external pdf.

Complex Systems Summer School 2012-Projects & Working Groups

2012-06-05T21:01:22Z

GeGoerg: /* Ideas on how to approach this */

{{Complex Systems Summer School 2012}}

==Project proposals==

=== Nonequilibrium game theory ===
My hope is to adapt some SFI-based models, by people like Crutchfield and Farmer, so that they will quantitatively or qualitatively reproduce features of real human data. Of course, that is very specific, and I'm up for all kinds of ideas in the areas of game learning, game dynamics, small group collective behavior, cognitive science, nonlinear time series, non-eq time series, etc., etc.

Meet me, Seth Frey, at dinner on Thursday and Friday.

=== Enzyme kinetics – Do enzymes just accelerate equilibrium or play an active role in chemical reactions? ===
Enzyme networks (e.g. glycolysis) and catalysts in complex mixtures (e.g. Belusov-Zhabotinski reaction) can profoundly influence the outcome of a chemical reaction system. What about a single enzyme? Biochemistry textbooks uniformly say that an enzyme accelerates a reaction without altering its outcome. Yet, the set of differential equations that generically describes enzyme catalysis has remarkable resemblance to the Roessler equations (a textbook example of a non-linear, complex system). With a fixed substrate input or a steady substrate flow, a single enzyme probably cannot affect the reaction outcome. However, sinusoidal or pulsating substrate input, substrate activation or product inhibition, coupling of two enzymes could turn the reaction pattern non-linear. For this project, the sets of equations to study are quite well established – they need to be analyzed. In contrast to some of the more ambitious ideas circulated, this task is exhaustively doable in less than four weeks.

I am Georg Weber. If you are interested in studying this problem, please find me on Tuesday over lunch or dinner (or talk to me at any other unstructured time).
=== Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how to approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

If you are interested to see more about this check out the [[Georg_M_Goerg#SFI_Project:_Traffic_pattern_analysis_-_Can_we_estimate_car_velocity_by_only_observing_car_counts.3F_.3D|SFI Project]] subsection on [[Georg_M_Goerg|Georg M. Goerg]] or email me to my_3_initials_in_lowercase@stat.cmu.edu. Let's say we meet on Wednesday for lunch (or just ask me any other time you see me around).

=== Cultural Evolution - General Meet-up ===
Attention anyone who is interested in cultural evolution or applying your models/methodologies to this fabulous topic!

Let's meet at 4:15 (June 5th) in the cafe during the first "Time to work on Projects" slot. A bunch of us coalesced there tonight and figured we should all properly meet up and then bud off into different projects. Please post your potential buds below:

=== Cultural Evolution - things that look like drift but aren't ===
Lots of cultural evolution looks like drift (Bently et al 2004 'Random drift and culture change"). But what social transmission or cognitive learning mechanisms are isomorphic to random sampling with replacement from cultural inputs? In biological evolution, drift serves as a null model of sorts - one that should be ruled out before you can claim that anything more interesting is happening. However, it's not clear what the "uninteresting" type of change is for things that replicate by passing through human cognition and human social systems - like culture does. Is there even a reasonable equivalent of drift in cultural transmission? How should we go about conceptualizing and modeling the evolutionary forces at play in culture?

One candidate for a drifty-lookin' human behavior is probability matching: when people reproduce similar distributions of variation to that which they've learned from. And probability matching is rampant in human behavior (from language learning, to decision making, and even at the neural level). But I think this is a clearly different process than drift, however it still may qualify under Bentley's vague criteria - we can test that out. And there have got to be more drift-esque processes, anyone have any ideas?

If you're interested in these issues or modeling evolution (of any type of system), please give me a shout!

Vanessa

vanferdi [at] gmail.com

==="Small Steps and Big Ideas" Group===

[http://tuvalu.santafe.edu/events/workshops/index.php/Christa_Brelsford Christa] [http://tuvalu.santafe.edu/events/workshops/index.php/Daniel_Wu Dan] [http://tuvalu.santafe.edu/events/workshops/index.php/Xin_Lu Xin] and Tom spent a while talking after dinner about a bunch of big ideas. Some things we thought about were *big data type network problems, *integrating qualitative social information with models of physical systems, *using games to understand cooperation and decision making.

We'll meet at dinner at 5:30 today (Tuesday, June 5th) in the cafeteria.

=== 107 Proteins in 10-15 cubic meters ===
Cells rely on proteins to perform vital metabolic and signaling functions; however, it is unclear how proteins are successfully directed to their necessary cellular location(s) in a densely-packed macromolecular environment within the cytoplasm and on the cellular membrane in a short timescale (see for example [http://www.pnas.org/content/108/16/6438.full Weigel et al., PNAS 2011]). Using the cell as a manipulatable model of complexity, one could begin to define the parameters and questions, as they pertain to prokaryotic and eukaryotic cells. If interested, please drop me a line: Sepehr Ehsani; sepehr.ehsani[at]utoronto.ca.

=== Innovation and Technological Progress ===

I noticed that a number of people mentioned that they were interested in some way in relation to innovation. I was wondering if anyone was interested in a project looking at how particular technologies progress over time and whether charting the form of successful (and/or unsuccessful) previous technologies such as the transistor, fission reactor, mobile phone, etc. in terms of either price, efficiency, or some other variable may be useful in predicting whether a current technology such as solar PV, fuel cell, or something else is following a similar trajectory. Other possible ideas might be to look at using patent, publication, or collaboration network data to reveal certain features of innovation that are not captured by other statistics, particularly for technologies that have yet to reach the market. SFI Professor Doyne Farmer has looked at some of this already in 'The Role of Design Complexity in Technology Improvement', see link: http://adsabs.harvard.edu/abs/2009arXiv0907.0036M

This could be a jumping off point for some ideas on data, methods, models etc. Just throwing the idea out there and it's welcome to completely change but if you're interested, message me (Gareth Haslam) haslam@ias.unu.edu or find me in class.

=== Space, Stochasticity, Stability; Speciation? ===

[http://tuvalu.santafe.edu/events/workshops/index.php/Xue_Feng Xue], [http://tuvalu.santafe.edu/events/workshops/index.php/Chloe_Lewis Chloe] and [http://tuvalu.santafe.edu/events/workshops/index.php/Xiaoli_Dong Xiaoli]are all working in ecosystems that experience_ a lot of unpredictability in a limiting ecosystem variables (water and/or nutrients); we see patchiness in space and time in how organisms are arranged; and we have some ideas about how the stochasticity may cause the spatial arrangements. [http://tuvalu.santafe.edu/events/workshops/index.php/Si_Tang Si] is working on the stability and robustness of ecosystems.

With enough time, this is likely to involve speciation either to express different strategies, or as a result of spatial separation.

Find any of us walking-around, or meet in the cafeteria at 4:15 June 5th.

=== Plasticity in Neural Networks ===
I've done some modeling which shows that the amount of genetic variation that accumulates at any particular metabolic gene (enzyme) in a population at any given time is a function of the network topology in which the gene is embedded, as well as the distance of the network output from an optimum. So, for instance, in a linear metabolic network, enzymes at the beginning of a pathway will tend to be more constrained (show less variation in the population) than at the end of the pathway. This makes sense given that any changes in those first genes would ripple through the system and have a greater relative effect than mutations in later genes. However, this is only true when a population is already close to an optimum. When far from an optimum, we see the exact opposite trend with more variation in the front of the pathway. This also makes sense - when far from a goal, taking bigger steps gives individuals a better chance of achieving higher fitness. The system as a whole then uses the different relative step sizes according to pathway position to "fine tune" its output.
I think these findings are quite general - at least the model we used was simple enough that it could apply to many different types of directional developmental processes. We can conceptualize these "genes" more generally as sequential steps in a developmental process with some arbitrary goal. These could be steps in a factory assembly line, major product revisions versus minor releases, or (and this is my favorite), neurons learning about their environment. I'm curious what would happen if we took a similar approach to model neural networks. Genetic variation is the raw material for evolution while neural plasticity is the raw material for learning. The question we would be trying to answer is where, within a neural network, would we expect the most plasticity given a particular network topology and distance form a learning goal.
Please contact me (Mark Longo) if this sounds interesting. I'll be available during unstructured time, or you can email mlongo@stanford.edu.
[http://tuvalu.santafe.edu/events/workshops/index.php/Mark_D._Longo]

=== Robustness of complex networks ===
[[File:Zoo.png|thumb|Fig. 1. Zoo of complex networks (an example). Taken from Sol´e and Valverde, 2001.]]
==== Problem statement ====
Complex networks have various properties which can be measured in real networks (WWW, social networks, biological networks), e.g. degree distribution, modularity, hierarchy, assortativity etc. Robustness of complex networks is a big question, however only some progress have been done in this direction. For example, it was shown that the scale-free networks are much more topologically robust to random attacks than random networks. Many people claim that various characteristics of complex networks will influence the robustness interdependently. The question I am interested in is how?

==== Approach ====
The idea is to generate continuous topology space of various complex networks (networks with different modularity, degree distribution, hierarchy etc) and use it to measure their robustness (see Fig. 1). There are many approaches to measure the robustness of complex networks. For example we can remove edges of vertices of a complex network graph and look at the size of a giant cluster. We can discuss other possibilities.

If you are interested you can contact me directly or via my E-mail: krystoferivanov@gmail.com or via my [[Oleksandr Ivanov|discussion page in CSSS 2012 wiki]].

Complex Systems Summer School 2012-Projects & Working Groups

2012-06-05T21:00:58Z

GeGoerg: /* Ideas on how to approach this */

{{Complex Systems Summer School 2012}}

==Project proposals==

=== Nonequilibrium game theory ===
My hope is to adapt some SFI-based models, by people like Crutchfield and Farmer, so that they will quantitatively or qualitatively reproduce features of real human data. Of course, that is very specific, and I'm up for all kinds of ideas in the areas of game learning, game dynamics, small group collective behavior, cognitive science, nonlinear time series, non-eq time series, etc., etc.

Meet me, Seth Frey, at dinner on Thursday and Friday.

=== Enzyme kinetics – Do enzymes just accelerate equilibrium or play an active role in chemical reactions? ===
Enzyme networks (e.g. glycolysis) and catalysts in complex mixtures (e.g. Belusov-Zhabotinski reaction) can profoundly influence the outcome of a chemical reaction system. What about a single enzyme? Biochemistry textbooks uniformly say that an enzyme accelerates a reaction without altering its outcome. Yet, the set of differential equations that generically describes enzyme catalysis has remarkable resemblance to the Roessler equations (a textbook example of a non-linear, complex system). With a fixed substrate input or a steady substrate flow, a single enzyme probably cannot affect the reaction outcome. However, sinusoidal or pulsating substrate input, substrate activation or product inhibition, coupling of two enzymes could turn the reaction pattern non-linear. For this project, the sets of equations to study are quite well established – they need to be analyzed. In contrast to some of the more ambitious ideas circulated, this task is exhaustively doable in less than four weeks.

I am Georg Weber. If you are interested in studying this problem, please find me on Tuesday over lunch or dinner (or talk to me at any other unstructured time).
=== Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how to approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

If you are interested to see more about this check out the details [[Georg_M_Goerg#SFI_Project:_Traffic_pattern_analysis_-_Can_we_estimate_car_velocity_by_only_observing_car_counts.3F_.3D|SFI Project]] subsection on [[Georg_M_Goerg|Georg M. Goerg]] or email me to my_3_initials_in_lowercase@stat.cmu.edu. Let's say we meet on Wednesday for lunch (or just ask me any other time you see me around).

=== Cultural Evolution - General Meet-up ===
Attention anyone who is interested in cultural evolution or applying your models/methodologies to this fabulous topic!

Let's meet at 4:15 (June 5th) in the cafe during the first "Time to work on Projects" slot. A bunch of us coalesced there tonight and figured we should all properly meet up and then bud off into different projects. Please post your potential buds below:

=== Cultural Evolution - things that look like drift but aren't ===
Lots of cultural evolution looks like drift (Bently et al 2004 'Random drift and culture change"). But what social transmission or cognitive learning mechanisms are isomorphic to random sampling with replacement from cultural inputs? In biological evolution, drift serves as a null model of sorts - one that should be ruled out before you can claim that anything more interesting is happening. However, it's not clear what the "uninteresting" type of change is for things that replicate by passing through human cognition and human social systems - like culture does. Is there even a reasonable equivalent of drift in cultural transmission? How should we go about conceptualizing and modeling the evolutionary forces at play in culture?

One candidate for a drifty-lookin' human behavior is probability matching: when people reproduce similar distributions of variation to that which they've learned from. And probability matching is rampant in human behavior (from language learning, to decision making, and even at the neural level). But I think this is a clearly different process than drift, however it still may qualify under Bentley's vague criteria - we can test that out. And there have got to be more drift-esque processes, anyone have any ideas?

If you're interested in these issues or modeling evolution (of any type of system), please give me a shout!

Vanessa

vanferdi [at] gmail.com

==="Small Steps and Big Ideas" Group===

[http://tuvalu.santafe.edu/events/workshops/index.php/Christa_Brelsford Christa] [http://tuvalu.santafe.edu/events/workshops/index.php/Daniel_Wu Dan] [http://tuvalu.santafe.edu/events/workshops/index.php/Xin_Lu Xin] and Tom spent a while talking after dinner about a bunch of big ideas. Some things we thought about were *big data type network problems, *integrating qualitative social information with models of physical systems, *using games to understand cooperation and decision making.

We'll meet at dinner at 5:30 today (Tuesday, June 5th) in the cafeteria.

=== 107 Proteins in 10-15 cubic meters ===
Cells rely on proteins to perform vital metabolic and signaling functions; however, it is unclear how proteins are successfully directed to their necessary cellular location(s) in a densely-packed macromolecular environment within the cytoplasm and on the cellular membrane in a short timescale (see for example [http://www.pnas.org/content/108/16/6438.full Weigel et al., PNAS 2011]). Using the cell as a manipulatable model of complexity, one could begin to define the parameters and questions, as they pertain to prokaryotic and eukaryotic cells. If interested, please drop me a line: Sepehr Ehsani; sepehr.ehsani[at]utoronto.ca.

=== Innovation and Technological Progress ===

I noticed that a number of people mentioned that they were interested in some way in relation to innovation. I was wondering if anyone was interested in a project looking at how particular technologies progress over time and whether charting the form of successful (and/or unsuccessful) previous technologies such as the transistor, fission reactor, mobile phone, etc. in terms of either price, efficiency, or some other variable may be useful in predicting whether a current technology such as solar PV, fuel cell, or something else is following a similar trajectory. Other possible ideas might be to look at using patent, publication, or collaboration network data to reveal certain features of innovation that are not captured by other statistics, particularly for technologies that have yet to reach the market. SFI Professor Doyne Farmer has looked at some of this already in 'The Role of Design Complexity in Technology Improvement', see link: http://adsabs.harvard.edu/abs/2009arXiv0907.0036M

This could be a jumping off point for some ideas on data, methods, models etc. Just throwing the idea out there and it's welcome to completely change but if you're interested, message me (Gareth Haslam) haslam@ias.unu.edu or find me in class.

=== Space, Stochasticity, Stability; Speciation? ===

[http://tuvalu.santafe.edu/events/workshops/index.php/Xue_Feng Xue], [http://tuvalu.santafe.edu/events/workshops/index.php/Chloe_Lewis Chloe] and [http://tuvalu.santafe.edu/events/workshops/index.php/Xiaoli_Dong Xiaoli]are all working in ecosystems that experience_ a lot of unpredictability in a limiting ecosystem variables (water and/or nutrients); we see patchiness in space and time in how organisms are arranged; and we have some ideas about how the stochasticity may cause the spatial arrangements. [http://tuvalu.santafe.edu/events/workshops/index.php/Si_Tang Si] is working on the stability and robustness of ecosystems.

With enough time, this is likely to involve speciation either to express different strategies, or as a result of spatial separation.

Find any of us walking-around, or meet in the cafeteria at 4:15 June 5th.

=== Plasticity in Neural Networks ===
I've done some modeling which shows that the amount of genetic variation that accumulates at any particular metabolic gene (enzyme) in a population at any given time is a function of the network topology in which the gene is embedded, as well as the distance of the network output from an optimum. So, for instance, in a linear metabolic network, enzymes at the beginning of a pathway will tend to be more constrained (show less variation in the population) than at the end of the pathway. This makes sense given that any changes in those first genes would ripple through the system and have a greater relative effect than mutations in later genes. However, this is only true when a population is already close to an optimum. When far from an optimum, we see the exact opposite trend with more variation in the front of the pathway. This also makes sense - when far from a goal, taking bigger steps gives individuals a better chance of achieving higher fitness. The system as a whole then uses the different relative step sizes according to pathway position to "fine tune" its output.
I think these findings are quite general - at least the model we used was simple enough that it could apply to many different types of directional developmental processes. We can conceptualize these "genes" more generally as sequential steps in a developmental process with some arbitrary goal. These could be steps in a factory assembly line, major product revisions versus minor releases, or (and this is my favorite), neurons learning about their environment. I'm curious what would happen if we took a similar approach to model neural networks. Genetic variation is the raw material for evolution while neural plasticity is the raw material for learning. The question we would be trying to answer is where, within a neural network, would we expect the most plasticity given a particular network topology and distance form a learning goal.
Please contact me (Mark Longo) if this sounds interesting. I'll be available during unstructured time, or you can email mlongo@stanford.edu.
[http://tuvalu.santafe.edu/events/workshops/index.php/Mark_D._Longo]

=== Robustness of complex networks ===
[[File:Zoo.png|thumb|Fig. 1. Zoo of complex networks (an example). Taken from Sol´e and Valverde, 2001.]]
==== Problem statement ====
Complex networks have various properties which can be measured in real networks (WWW, social networks, biological networks), e.g. degree distribution, modularity, hierarchy, assortativity etc. Robustness of complex networks is a big question, however only some progress have been done in this direction. For example, it was shown that the scale-free networks are much more topologically robust to random attacks than random networks. Many people claim that various characteristics of complex networks will influence the robustness interdependently. The question I am interested in is how?

==== Approach ====
The idea is to generate continuous topology space of various complex networks (networks with different modularity, degree distribution, hierarchy etc) and use it to measure their robustness (see Fig. 1). There are many approaches to measure the robustness of complex networks. For example we can remove edges of vertices of a complex network graph and look at the size of a giant cluster. We can discuss other possibilities.

If you are interested you can contact me directly or via my E-mail: krystoferivanov@gmail.com or via my [[Oleksandr Ivanov|discussion page in CSSS 2012 wiki]].

Complex Systems Summer School 2012-Projects & Working Groups

2012-06-05T21:00:41Z

GeGoerg: /* Ideas on how to approach this */

{{Complex Systems Summer School 2012}}

==Project proposals==

=== Nonequilibrium game theory ===
My hope is to adapt some SFI-based models, by people like Crutchfield and Farmer, so that they will quantitatively or qualitatively reproduce features of real human data. Of course, that is very specific, and I'm up for all kinds of ideas in the areas of game learning, game dynamics, small group collective behavior, cognitive science, nonlinear time series, non-eq time series, etc., etc.

Meet me, Seth Frey, at dinner on Thursday and Friday.

=== Enzyme kinetics – Do enzymes just accelerate equilibrium or play an active role in chemical reactions? ===
Enzyme networks (e.g. glycolysis) and catalysts in complex mixtures (e.g. Belusov-Zhabotinski reaction) can profoundly influence the outcome of a chemical reaction system. What about a single enzyme? Biochemistry textbooks uniformly say that an enzyme accelerates a reaction without altering its outcome. Yet, the set of differential equations that generically describes enzyme catalysis has remarkable resemblance to the Roessler equations (a textbook example of a non-linear, complex system). With a fixed substrate input or a steady substrate flow, a single enzyme probably cannot affect the reaction outcome. However, sinusoidal or pulsating substrate input, substrate activation or product inhibition, coupling of two enzymes could turn the reaction pattern non-linear. For this project, the sets of equations to study are quite well established – they need to be analyzed. In contrast to some of the more ambitious ideas circulated, this task is exhaustively doable in less than four weeks.

I am Georg Weber. If you are interested in studying this problem, please find me on Tuesday over lunch or dinner (or talk to me at any other unstructured time).
=== Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how to approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

If you are interested to see more about this check out the details [[Georg_M_Goerg#SFI_Project:_Traffic_pattern_analysis_-_Can_we_estimate_car_velocity_by_only_observing_car_counts.3F_.3D|SFI Project]] subsection on [Georg_M_Goerg|Georg M. Goerg] or email me to my_3_initials_in_lowercase@stat.cmu.edu. Let's say we meet on Wednesday for lunch (or just ask me any other time you see me around).

=== Cultural Evolution - General Meet-up ===
Attention anyone who is interested in cultural evolution or applying your models/methodologies to this fabulous topic!

Let's meet at 4:15 (June 5th) in the cafe during the first "Time to work on Projects" slot. A bunch of us coalesced there tonight and figured we should all properly meet up and then bud off into different projects. Please post your potential buds below:

=== Cultural Evolution - things that look like drift but aren't ===
Lots of cultural evolution looks like drift (Bently et al 2004 'Random drift and culture change"). But what social transmission or cognitive learning mechanisms are isomorphic to random sampling with replacement from cultural inputs? In biological evolution, drift serves as a null model of sorts - one that should be ruled out before you can claim that anything more interesting is happening. However, it's not clear what the "uninteresting" type of change is for things that replicate by passing through human cognition and human social systems - like culture does. Is there even a reasonable equivalent of drift in cultural transmission? How should we go about conceptualizing and modeling the evolutionary forces at play in culture?

One candidate for a drifty-lookin' human behavior is probability matching: when people reproduce similar distributions of variation to that which they've learned from. And probability matching is rampant in human behavior (from language learning, to decision making, and even at the neural level). But I think this is a clearly different process than drift, however it still may qualify under Bentley's vague criteria - we can test that out. And there have got to be more drift-esque processes, anyone have any ideas?

If you're interested in these issues or modeling evolution (of any type of system), please give me a shout!

Vanessa

vanferdi [at] gmail.com

==="Small Steps and Big Ideas" Group===

[http://tuvalu.santafe.edu/events/workshops/index.php/Christa_Brelsford Christa] [http://tuvalu.santafe.edu/events/workshops/index.php/Daniel_Wu Dan] [http://tuvalu.santafe.edu/events/workshops/index.php/Xin_Lu Xin] and Tom spent a while talking after dinner about a bunch of big ideas. Some things we thought about were *big data type network problems, *integrating qualitative social information with models of physical systems, *using games to understand cooperation and decision making.

We'll meet at dinner at 5:30 today (Tuesday, June 5th) in the cafeteria.

=== 107 Proteins in 10-15 cubic meters ===
Cells rely on proteins to perform vital metabolic and signaling functions; however, it is unclear how proteins are successfully directed to their necessary cellular location(s) in a densely-packed macromolecular environment within the cytoplasm and on the cellular membrane in a short timescale (see for example [http://www.pnas.org/content/108/16/6438.full Weigel et al., PNAS 2011]). Using the cell as a manipulatable model of complexity, one could begin to define the parameters and questions, as they pertain to prokaryotic and eukaryotic cells. If interested, please drop me a line: Sepehr Ehsani; sepehr.ehsani[at]utoronto.ca.

=== Innovation and Technological Progress ===

I noticed that a number of people mentioned that they were interested in some way in relation to innovation. I was wondering if anyone was interested in a project looking at how particular technologies progress over time and whether charting the form of successful (and/or unsuccessful) previous technologies such as the transistor, fission reactor, mobile phone, etc. in terms of either price, efficiency, or some other variable may be useful in predicting whether a current technology such as solar PV, fuel cell, or something else is following a similar trajectory. Other possible ideas might be to look at using patent, publication, or collaboration network data to reveal certain features of innovation that are not captured by other statistics, particularly for technologies that have yet to reach the market. SFI Professor Doyne Farmer has looked at some of this already in 'The Role of Design Complexity in Technology Improvement', see link: http://adsabs.harvard.edu/abs/2009arXiv0907.0036M

This could be a jumping off point for some ideas on data, methods, models etc. Just throwing the idea out there and it's welcome to completely change but if you're interested, message me (Gareth Haslam) haslam@ias.unu.edu or find me in class.

=== Space, Stochasticity, Stability; Speciation? ===

[http://tuvalu.santafe.edu/events/workshops/index.php/Xue_Feng Xue], [http://tuvalu.santafe.edu/events/workshops/index.php/Chloe_Lewis Chloe] and [http://tuvalu.santafe.edu/events/workshops/index.php/Xiaoli_Dong Xiaoli]are all working in ecosystems that experience_ a lot of unpredictability in a limiting ecosystem variables (water and/or nutrients); we see patchiness in space and time in how organisms are arranged; and we have some ideas about how the stochasticity may cause the spatial arrangements. [http://tuvalu.santafe.edu/events/workshops/index.php/Si_Tang Si] is working on the stability and robustness of ecosystems.

With enough time, this is likely to involve speciation either to express different strategies, or as a result of spatial separation.

Find any of us walking-around, or meet in the cafeteria at 4:15 June 5th.

=== Plasticity in Neural Networks ===
I've done some modeling which shows that the amount of genetic variation that accumulates at any particular metabolic gene (enzyme) in a population at any given time is a function of the network topology in which the gene is embedded, as well as the distance of the network output from an optimum. So, for instance, in a linear metabolic network, enzymes at the beginning of a pathway will tend to be more constrained (show less variation in the population) than at the end of the pathway. This makes sense given that any changes in those first genes would ripple through the system and have a greater relative effect than mutations in later genes. However, this is only true when a population is already close to an optimum. When far from an optimum, we see the exact opposite trend with more variation in the front of the pathway. This also makes sense - when far from a goal, taking bigger steps gives individuals a better chance of achieving higher fitness. The system as a whole then uses the different relative step sizes according to pathway position to "fine tune" its output.
I think these findings are quite general - at least the model we used was simple enough that it could apply to many different types of directional developmental processes. We can conceptualize these "genes" more generally as sequential steps in a developmental process with some arbitrary goal. These could be steps in a factory assembly line, major product revisions versus minor releases, or (and this is my favorite), neurons learning about their environment. I'm curious what would happen if we took a similar approach to model neural networks. Genetic variation is the raw material for evolution while neural plasticity is the raw material for learning. The question we would be trying to answer is where, within a neural network, would we expect the most plasticity given a particular network topology and distance form a learning goal.
Please contact me (Mark Longo) if this sounds interesting. I'll be available during unstructured time, or you can email mlongo@stanford.edu.
[http://tuvalu.santafe.edu/events/workshops/index.php/Mark_D._Longo]

=== Robustness of complex networks ===
[[File:Zoo.png|thumb|Fig. 1. Zoo of complex networks (an example). Taken from Sol´e and Valverde, 2001.]]
==== Problem statement ====
Complex networks have various properties which can be measured in real networks (WWW, social networks, biological networks), e.g. degree distribution, modularity, hierarchy, assortativity etc. Robustness of complex networks is a big question, however only some progress have been done in this direction. For example, it was shown that the scale-free networks are much more topologically robust to random attacks than random networks. Many people claim that various characteristics of complex networks will influence the robustness interdependently. The question I am interested in is how?

==== Approach ====
The idea is to generate continuous topology space of various complex networks (networks with different modularity, degree distribution, hierarchy etc) and use it to measure their robustness (see Fig. 1). There are many approaches to measure the robustness of complex networks. For example we can remove edges of vertices of a complex network graph and look at the size of a giant cluster. We can discuss other possibilities.

If you are interested you can contact me directly or via my E-mail: krystoferivanov@gmail.com or via my [[Oleksandr Ivanov|discussion page in CSSS 2012 wiki]].

Complex Systems Summer School 2012-Projects & Working Groups

2012-06-05T20:59:52Z

GeGoerg: /* Ideas on how to approach this */

{{Complex Systems Summer School 2012}}

==Project proposals==

=== Nonequilibrium game theory ===
My hope is to adapt some SFI-based models, by people like Crutchfield and Farmer, so that they will quantitatively or qualitatively reproduce features of real human data. Of course, that is very specific, and I'm up for all kinds of ideas in the areas of game learning, game dynamics, small group collective behavior, cognitive science, nonlinear time series, non-eq time series, etc., etc.

Meet me, Seth Frey, at dinner on Thursday and Friday.

=== Enzyme kinetics – Do enzymes just accelerate equilibrium or play an active role in chemical reactions? ===
Enzyme networks (e.g. glycolysis) and catalysts in complex mixtures (e.g. Belusov-Zhabotinski reaction) can profoundly influence the outcome of a chemical reaction system. What about a single enzyme? Biochemistry textbooks uniformly say that an enzyme accelerates a reaction without altering its outcome. Yet, the set of differential equations that generically describes enzyme catalysis has remarkable resemblance to the Roessler equations (a textbook example of a non-linear, complex system). With a fixed substrate input or a steady substrate flow, a single enzyme probably cannot affect the reaction outcome. However, sinusoidal or pulsating substrate input, substrate activation or product inhibition, coupling of two enzymes could turn the reaction pattern non-linear. For this project, the sets of equations to study are quite well established – they need to be analyzed. In contrast to some of the more ambitious ideas circulated, this task is exhaustively doable in less than four weeks.

I am Georg Weber. If you are interested in studying this problem, please find me on Tuesday over lunch or dinner (or talk to me at any other unstructured time).
=== Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how to approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

If you are interested to see more about this check out the details on the [[Georg_M_Goerg#SFI_Project:_Traffic_pattern_analysis_-_Can_we_estimate_car_velocity_by_only_observing_car_counts.3F_.3D|SFI Project]] subsection on [http://tuvalu.santafe.edu/events/workshops/index.php/Georg_M_Goerg Georg M. Goerg] or email me to my_3_initials_in_lowercase@stat.cmu.edu. Let's say we meet on Wednesday for lunch (or just ask me any other time you see me around).

=== Cultural Evolution - General Meet-up ===
Attention anyone who is interested in cultural evolution or applying your models/methodologies to this fabulous topic!

Let's meet at 4:15 (June 5th) in the cafe during the first "Time to work on Projects" slot. A bunch of us coalesced there tonight and figured we should all properly meet up and then bud off into different projects. Please post your potential buds below:

=== Cultural Evolution - things that look like drift but aren't ===
Lots of cultural evolution looks like drift (Bently et al 2004 'Random drift and culture change"). But what social transmission or cognitive learning mechanisms are isomorphic to random sampling with replacement from cultural inputs? In biological evolution, drift serves as a null model of sorts - one that should be ruled out before you can claim that anything more interesting is happening. However, it's not clear what the "uninteresting" type of change is for things that replicate by passing through human cognition and human social systems - like culture does. Is there even a reasonable equivalent of drift in cultural transmission? How should we go about conceptualizing and modeling the evolutionary forces at play in culture?

One candidate for a drifty-lookin' human behavior is probability matching: when people reproduce similar distributions of variation to that which they've learned from. And probability matching is rampant in human behavior (from language learning, to decision making, and even at the neural level). But I think this is a clearly different process than drift, however it still may qualify under Bentley's vague criteria - we can test that out. And there have got to be more drift-esque processes, anyone have any ideas?

If you're interested in these issues or modeling evolution (of any type of system), please give me a shout!

Vanessa

vanferdi [at] gmail.com

==="Small Steps and Big Ideas" Group===

[http://tuvalu.santafe.edu/events/workshops/index.php/Christa_Brelsford Christa] [http://tuvalu.santafe.edu/events/workshops/index.php/Daniel_Wu Dan] [http://tuvalu.santafe.edu/events/workshops/index.php/Xin_Lu Xin] and Tom spent a while talking after dinner about a bunch of big ideas. Some things we thought about were *big data type network problems, *integrating qualitative social information with models of physical systems, *using games to understand cooperation and decision making.

We'll meet at dinner at 5:30 today (Tuesday, June 5th) in the cafeteria.

=== 107 Proteins in 10-15 cubic meters ===
Cells rely on proteins to perform vital metabolic and signaling functions; however, it is unclear how proteins are successfully directed to their necessary cellular location(s) in a densely-packed macromolecular environment within the cytoplasm and on the cellular membrane in a short timescale (see for example [http://www.pnas.org/content/108/16/6438.full Weigel et al., PNAS 2011]). Using the cell as a manipulatable model of complexity, one could begin to define the parameters and questions, as they pertain to prokaryotic and eukaryotic cells. If interested, please drop me a line: Sepehr Ehsani; sepehr.ehsani[at]utoronto.ca.

=== Innovation and Technological Progress ===

I noticed that a number of people mentioned that they were interested in some way in relation to innovation. I was wondering if anyone was interested in a project looking at how particular technologies progress over time and whether charting the form of successful (and/or unsuccessful) previous technologies such as the transistor, fission reactor, mobile phone, etc. in terms of either price, efficiency, or some other variable may be useful in predicting whether a current technology such as solar PV, fuel cell, or something else is following a similar trajectory. Other possible ideas might be to look at using patent, publication, or collaboration network data to reveal certain features of innovation that are not captured by other statistics, particularly for technologies that have yet to reach the market. SFI Professor Doyne Farmer has looked at some of this already in 'The Role of Design Complexity in Technology Improvement', see link: http://adsabs.harvard.edu/abs/2009arXiv0907.0036M

This could be a jumping off point for some ideas on data, methods, models etc. Just throwing the idea out there and it's welcome to completely change but if you're interested, message me (Gareth Haslam) haslam@ias.unu.edu or find me in class.

=== Space, Stochasticity, Stability; Speciation? ===

[http://tuvalu.santafe.edu/events/workshops/index.php/Xue_Feng Xue], [http://tuvalu.santafe.edu/events/workshops/index.php/Chloe_Lewis Chloe] and [http://tuvalu.santafe.edu/events/workshops/index.php/Xiaoli_Dong Xiaoli]are all working in ecosystems that experience_ a lot of unpredictability in a limiting ecosystem variables (water and/or nutrients); we see patchiness in space and time in how organisms are arranged; and we have some ideas about how the stochasticity may cause the spatial arrangements. [http://tuvalu.santafe.edu/events/workshops/index.php/Si_Tang Si] is working on the stability and robustness of ecosystems.

With enough time, this is likely to involve speciation either to express different strategies, or as a result of spatial separation.

Find any of us walking-around, or meet in the cafeteria at 4:15 June 5th.

=== Plasticity in Neural Networks ===
I've done some modeling which shows that the amount of genetic variation that accumulates at any particular metabolic gene (enzyme) in a population at any given time is a function of the network topology in which the gene is embedded, as well as the distance of the network output from an optimum. So, for instance, in a linear metabolic network, enzymes at the beginning of a pathway will tend to be more constrained (show less variation in the population) than at the end of the pathway. This makes sense given that any changes in those first genes would ripple through the system and have a greater relative effect than mutations in later genes. However, this is only true when a population is already close to an optimum. When far from an optimum, we see the exact opposite trend with more variation in the front of the pathway. This also makes sense - when far from a goal, taking bigger steps gives individuals a better chance of achieving higher fitness. The system as a whole then uses the different relative step sizes according to pathway position to "fine tune" its output.
I think these findings are quite general - at least the model we used was simple enough that it could apply to many different types of directional developmental processes. We can conceptualize these "genes" more generally as sequential steps in a developmental process with some arbitrary goal. These could be steps in a factory assembly line, major product revisions versus minor releases, or (and this is my favorite), neurons learning about their environment. I'm curious what would happen if we took a similar approach to model neural networks. Genetic variation is the raw material for evolution while neural plasticity is the raw material for learning. The question we would be trying to answer is where, within a neural network, would we expect the most plasticity given a particular network topology and distance form a learning goal.
Please contact me (Mark Longo) if this sounds interesting. I'll be available during unstructured time, or you can email mlongo@stanford.edu.
[http://tuvalu.santafe.edu/events/workshops/index.php/Mark_D._Longo]

=== Robustness of complex networks ===
[[File:Zoo.png|thumb|Fig. 1. Zoo of complex networks (an example). Taken from Sol´e and Valverde, 2001.]]
==== Problem statement ====
Complex networks have various properties which can be measured in real networks (WWW, social networks, biological networks), e.g. degree distribution, modularity, hierarchy, assortativity etc. Robustness of complex networks is a big question, however only some progress have been done in this direction. For example, it was shown that the scale-free networks are much more topologically robust to random attacks than random networks. Many people claim that various characteristics of complex networks will influence the robustness interdependently. The question I am interested in is how?

==== Approach ====
The idea is to generate continuous topology space of various complex networks (networks with different modularity, degree distribution, hierarchy etc) and use it to measure their robustness (see Fig. 1). There are many approaches to measure the robustness of complex networks. For example we can remove edges of vertices of a complex network graph and look at the size of a giant cluster. We can discuss other possibilities.

If you are interested you can contact me directly or via my E-mail: krystoferivanov@gmail.com or via my [[Oleksandr Ivanov|discussion page in CSSS 2012 wiki]].

Complex Systems Summer School 2012-Projects & Working Groups

2012-06-05T20:59:14Z

GeGoerg: /* Ideas on how to approach this */

{{Complex Systems Summer School 2012}}

==Project proposals==

=== Nonequilibrium game theory ===
My hope is to adapt some SFI-based models, by people like Crutchfield and Farmer, so that they will quantitatively or qualitatively reproduce features of real human data. Of course, that is very specific, and I'm up for all kinds of ideas in the areas of game learning, game dynamics, small group collective behavior, cognitive science, nonlinear time series, non-eq time series, etc., etc.

Meet me, Seth Frey, at dinner on Thursday and Friday.

=== Enzyme kinetics – Do enzymes just accelerate equilibrium or play an active role in chemical reactions? ===
Enzyme networks (e.g. glycolysis) and catalysts in complex mixtures (e.g. Belusov-Zhabotinski reaction) can profoundly influence the outcome of a chemical reaction system. What about a single enzyme? Biochemistry textbooks uniformly say that an enzyme accelerates a reaction without altering its outcome. Yet, the set of differential equations that generically describes enzyme catalysis has remarkable resemblance to the Roessler equations (a textbook example of a non-linear, complex system). With a fixed substrate input or a steady substrate flow, a single enzyme probably cannot affect the reaction outcome. However, sinusoidal or pulsating substrate input, substrate activation or product inhibition, coupling of two enzymes could turn the reaction pattern non-linear. For this project, the sets of equations to study are quite well established – they need to be analyzed. In contrast to some of the more ambitious ideas circulated, this task is exhaustively doable in less than four weeks.

I am Georg Weber. If you are interested in studying this problem, please find me on Tuesday over lunch or dinner (or talk to me at any other unstructured time).
=== Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how to approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

If you are interested to see more about this check out the details on the [[Georg_M_Goerg#SFI_Project:_Traffic_pattern_analysis_-_Can_we_estimate_car_velocity_by_only_observing_car_counts.3F_.3D SFI Project]] subsection on [http://tuvalu.santafe.edu/events/workshops/index.php/Georg_M_Goerg Georg M. Goerg] or email me to my_3_initials_in_lowercase@stat.cmu.edu. Let's say we meet on Wednesday for lunch (or just ask me any other time you see me around).

=== Cultural Evolution - General Meet-up ===
Attention anyone who is interested in cultural evolution or applying your models/methodologies to this fabulous topic!

Let's meet at 4:15 (June 5th) in the cafe during the first "Time to work on Projects" slot. A bunch of us coalesced there tonight and figured we should all properly meet up and then bud off into different projects. Please post your potential buds below:

=== Cultural Evolution - things that look like drift but aren't ===
Lots of cultural evolution looks like drift (Bently et al 2004 'Random drift and culture change"). But what social transmission or cognitive learning mechanisms are isomorphic to random sampling with replacement from cultural inputs? In biological evolution, drift serves as a null model of sorts - one that should be ruled out before you can claim that anything more interesting is happening. However, it's not clear what the "uninteresting" type of change is for things that replicate by passing through human cognition and human social systems - like culture does. Is there even a reasonable equivalent of drift in cultural transmission? How should we go about conceptualizing and modeling the evolutionary forces at play in culture?

One candidate for a drifty-lookin' human behavior is probability matching: when people reproduce similar distributions of variation to that which they've learned from. And probability matching is rampant in human behavior (from language learning, to decision making, and even at the neural level). But I think this is a clearly different process than drift, however it still may qualify under Bentley's vague criteria - we can test that out. And there have got to be more drift-esque processes, anyone have any ideas?

If you're interested in these issues or modeling evolution (of any type of system), please give me a shout!

Vanessa

vanferdi [at] gmail.com

==="Small Steps and Big Ideas" Group===

[http://tuvalu.santafe.edu/events/workshops/index.php/Christa_Brelsford Christa] [http://tuvalu.santafe.edu/events/workshops/index.php/Daniel_Wu Dan] [http://tuvalu.santafe.edu/events/workshops/index.php/Xin_Lu Xin] and Tom spent a while talking after dinner about a bunch of big ideas. Some things we thought about were *big data type network problems, *integrating qualitative social information with models of physical systems, *using games to understand cooperation and decision making.

We'll meet at dinner at 5:30 today (Tuesday, June 5th) in the cafeteria.

=== 107 Proteins in 10-15 cubic meters ===
Cells rely on proteins to perform vital metabolic and signaling functions; however, it is unclear how proteins are successfully directed to their necessary cellular location(s) in a densely-packed macromolecular environment within the cytoplasm and on the cellular membrane in a short timescale (see for example [http://www.pnas.org/content/108/16/6438.full Weigel et al., PNAS 2011]). Using the cell as a manipulatable model of complexity, one could begin to define the parameters and questions, as they pertain to prokaryotic and eukaryotic cells. If interested, please drop me a line: Sepehr Ehsani; sepehr.ehsani[at]utoronto.ca.

=== Innovation and Technological Progress ===

I noticed that a number of people mentioned that they were interested in some way in relation to innovation. I was wondering if anyone was interested in a project looking at how particular technologies progress over time and whether charting the form of successful (and/or unsuccessful) previous technologies such as the transistor, fission reactor, mobile phone, etc. in terms of either price, efficiency, or some other variable may be useful in predicting whether a current technology such as solar PV, fuel cell, or something else is following a similar trajectory. Other possible ideas might be to look at using patent, publication, or collaboration network data to reveal certain features of innovation that are not captured by other statistics, particularly for technologies that have yet to reach the market. SFI Professor Doyne Farmer has looked at some of this already in 'The Role of Design Complexity in Technology Improvement', see link: http://adsabs.harvard.edu/abs/2009arXiv0907.0036M

This could be a jumping off point for some ideas on data, methods, models etc. Just throwing the idea out there and it's welcome to completely change but if you're interested, message me (Gareth Haslam) haslam@ias.unu.edu or find me in class.

=== Space, Stochasticity, Stability; Speciation? ===

[http://tuvalu.santafe.edu/events/workshops/index.php/Xue_Feng Xue], [http://tuvalu.santafe.edu/events/workshops/index.php/Chloe_Lewis Chloe] and [http://tuvalu.santafe.edu/events/workshops/index.php/Xiaoli_Dong Xiaoli]are all working in ecosystems that experience_ a lot of unpredictability in a limiting ecosystem variables (water and/or nutrients); we see patchiness in space and time in how organisms are arranged; and we have some ideas about how the stochasticity may cause the spatial arrangements. [http://tuvalu.santafe.edu/events/workshops/index.php/Si_Tang Si] is working on the stability and robustness of ecosystems.

With enough time, this is likely to involve speciation either to express different strategies, or as a result of spatial separation.

Find any of us walking-around, or meet in the cafeteria at 4:15 June 5th.

=== Plasticity in Neural Networks ===
I've done some modeling which shows that the amount of genetic variation that accumulates at any particular metabolic gene (enzyme) in a population at any given time is a function of the network topology in which the gene is embedded, as well as the distance of the network output from an optimum. So, for instance, in a linear metabolic network, enzymes at the beginning of a pathway will tend to be more constrained (show less variation in the population) than at the end of the pathway. This makes sense given that any changes in those first genes would ripple through the system and have a greater relative effect than mutations in later genes. However, this is only true when a population is already close to an optimum. When far from an optimum, we see the exact opposite trend with more variation in the front of the pathway. This also makes sense - when far from a goal, taking bigger steps gives individuals a better chance of achieving higher fitness. The system as a whole then uses the different relative step sizes according to pathway position to "fine tune" its output.
I think these findings are quite general - at least the model we used was simple enough that it could apply to many different types of directional developmental processes. We can conceptualize these "genes" more generally as sequential steps in a developmental process with some arbitrary goal. These could be steps in a factory assembly line, major product revisions versus minor releases, or (and this is my favorite), neurons learning about their environment. I'm curious what would happen if we took a similar approach to model neural networks. Genetic variation is the raw material for evolution while neural plasticity is the raw material for learning. The question we would be trying to answer is where, within a neural network, would we expect the most plasticity given a particular network topology and distance form a learning goal.
Please contact me (Mark Longo) if this sounds interesting. I'll be available during unstructured time, or you can email mlongo@stanford.edu.
[http://tuvalu.santafe.edu/events/workshops/index.php/Mark_D._Longo]

=== Robustness of complex networks ===
[[File:Zoo.png|thumb|Fig. 1. Zoo of complex networks (an example). Taken from Sol´e and Valverde, 2001.]]
==== Problem statement ====
Complex networks have various properties which can be measured in real networks (WWW, social networks, biological networks), e.g. degree distribution, modularity, hierarchy, assortativity etc. Robustness of complex networks is a big question, however only some progress have been done in this direction. For example, it was shown that the scale-free networks are much more topologically robust to random attacks than random networks. Many people claim that various characteristics of complex networks will influence the robustness interdependently. The question I am interested in is how?

==== Approach ====
The idea is to generate continuous topology space of various complex networks (networks with different modularity, degree distribution, hierarchy etc) and use it to measure their robustness (see Fig. 1). There are many approaches to measure the robustness of complex networks. For example we can remove edges of vertices of a complex network graph and look at the size of a giant cluster. We can discuss other possibilities.

If you are interested you can contact me directly or via my E-mail: krystoferivanov@gmail.com or via my [[Oleksandr Ivanov|discussion page in CSSS 2012 wiki]].

Complex Systems Summer School 2012-Projects & Working Groups

2012-06-05T20:58:44Z

GeGoerg: /* Ideas on how to approach this */

{{Complex Systems Summer School 2012}}

==Project proposals==

=== Nonequilibrium game theory ===
My hope is to adapt some SFI-based models, by people like Crutchfield and Farmer, so that they will quantitatively or qualitatively reproduce features of real human data. Of course, that is very specific, and I'm up for all kinds of ideas in the areas of game learning, game dynamics, small group collective behavior, cognitive science, nonlinear time series, non-eq time series, etc., etc.

Meet me, Seth Frey, at dinner on Thursday and Friday.

=== Enzyme kinetics – Do enzymes just accelerate equilibrium or play an active role in chemical reactions? ===
Enzyme networks (e.g. glycolysis) and catalysts in complex mixtures (e.g. Belusov-Zhabotinski reaction) can profoundly influence the outcome of a chemical reaction system. What about a single enzyme? Biochemistry textbooks uniformly say that an enzyme accelerates a reaction without altering its outcome. Yet, the set of differential equations that generically describes enzyme catalysis has remarkable resemblance to the Roessler equations (a textbook example of a non-linear, complex system). With a fixed substrate input or a steady substrate flow, a single enzyme probably cannot affect the reaction outcome. However, sinusoidal or pulsating substrate input, substrate activation or product inhibition, coupling of two enzymes could turn the reaction pattern non-linear. For this project, the sets of equations to study are quite well established – they need to be analyzed. In contrast to some of the more ambitious ideas circulated, this task is exhaustively doable in less than four weeks.

I am Georg Weber. If you are interested in studying this problem, please find me on Tuesday over lunch or dinner (or talk to me at any other unstructured time).
=== Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how to approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

If you are interested to see more about this check out the details on the [Georg_M_Goerg#SFI_Project:_Traffic_pattern_analysis_-_Can_we_estimate_car_velocity_by_only_observing_car_counts.3F_.3D SFI Project] subsection on [http://tuvalu.santafe.edu/events/workshops/index.php/Georg_M_Goerg Georg M. Goerg] or email me to my_3_initials_in_lowercase@stat.cmu.edu. Let's say we meet on Wednesday for lunch (or just ask me any other time you see me around).

=== Cultural Evolution - General Meet-up ===
Attention anyone who is interested in cultural evolution or applying your models/methodologies to this fabulous topic!

Let's meet at 4:15 (June 5th) in the cafe during the first "Time to work on Projects" slot. A bunch of us coalesced there tonight and figured we should all properly meet up and then bud off into different projects. Please post your potential buds below:

=== Cultural Evolution - things that look like drift but aren't ===
Lots of cultural evolution looks like drift (Bently et al 2004 'Random drift and culture change"). But what social transmission or cognitive learning mechanisms are isomorphic to random sampling with replacement from cultural inputs? In biological evolution, drift serves as a null model of sorts - one that should be ruled out before you can claim that anything more interesting is happening. However, it's not clear what the "uninteresting" type of change is for things that replicate by passing through human cognition and human social systems - like culture does. Is there even a reasonable equivalent of drift in cultural transmission? How should we go about conceptualizing and modeling the evolutionary forces at play in culture?

One candidate for a drifty-lookin' human behavior is probability matching: when people reproduce similar distributions of variation to that which they've learned from. And probability matching is rampant in human behavior (from language learning, to decision making, and even at the neural level). But I think this is a clearly different process than drift, however it still may qualify under Bentley's vague criteria - we can test that out. And there have got to be more drift-esque processes, anyone have any ideas?

If you're interested in these issues or modeling evolution (of any type of system), please give me a shout!

Vanessa

vanferdi [at] gmail.com

==="Small Steps and Big Ideas" Group===

[http://tuvalu.santafe.edu/events/workshops/index.php/Christa_Brelsford Christa] [http://tuvalu.santafe.edu/events/workshops/index.php/Daniel_Wu Dan] [http://tuvalu.santafe.edu/events/workshops/index.php/Xin_Lu Xin] and Tom spent a while talking after dinner about a bunch of big ideas. Some things we thought about were *big data type network problems, *integrating qualitative social information with models of physical systems, *using games to understand cooperation and decision making.

We'll meet at dinner at 5:30 today (Tuesday, June 5th) in the cafeteria.

=== 107 Proteins in 10-15 cubic meters ===
Cells rely on proteins to perform vital metabolic and signaling functions; however, it is unclear how proteins are successfully directed to their necessary cellular location(s) in a densely-packed macromolecular environment within the cytoplasm and on the cellular membrane in a short timescale (see for example [http://www.pnas.org/content/108/16/6438.full Weigel et al., PNAS 2011]). Using the cell as a manipulatable model of complexity, one could begin to define the parameters and questions, as they pertain to prokaryotic and eukaryotic cells. If interested, please drop me a line: Sepehr Ehsani; sepehr.ehsani[at]utoronto.ca.

=== Innovation and Technological Progress ===

I noticed that a number of people mentioned that they were interested in some way in relation to innovation. I was wondering if anyone was interested in a project looking at how particular technologies progress over time and whether charting the form of successful (and/or unsuccessful) previous technologies such as the transistor, fission reactor, mobile phone, etc. in terms of either price, efficiency, or some other variable may be useful in predicting whether a current technology such as solar PV, fuel cell, or something else is following a similar trajectory. Other possible ideas might be to look at using patent, publication, or collaboration network data to reveal certain features of innovation that are not captured by other statistics, particularly for technologies that have yet to reach the market. SFI Professor Doyne Farmer has looked at some of this already in 'The Role of Design Complexity in Technology Improvement', see link: http://adsabs.harvard.edu/abs/2009arXiv0907.0036M

This could be a jumping off point for some ideas on data, methods, models etc. Just throwing the idea out there and it's welcome to completely change but if you're interested, message me (Gareth Haslam) haslam@ias.unu.edu or find me in class.

=== Space, Stochasticity, Stability; Speciation? ===

[http://tuvalu.santafe.edu/events/workshops/index.php/Xue_Feng Xue], [http://tuvalu.santafe.edu/events/workshops/index.php/Chloe_Lewis Chloe] and [http://tuvalu.santafe.edu/events/workshops/index.php/Xiaoli_Dong Xiaoli]are all working in ecosystems that experience_ a lot of unpredictability in a limiting ecosystem variables (water and/or nutrients); we see patchiness in space and time in how organisms are arranged; and we have some ideas about how the stochasticity may cause the spatial arrangements. [http://tuvalu.santafe.edu/events/workshops/index.php/Si_Tang Si] is working on the stability and robustness of ecosystems.

With enough time, this is likely to involve speciation either to express different strategies, or as a result of spatial separation.

Find any of us walking-around, or meet in the cafeteria at 4:15 June 5th.

=== Plasticity in Neural Networks ===
I've done some modeling which shows that the amount of genetic variation that accumulates at any particular metabolic gene (enzyme) in a population at any given time is a function of the network topology in which the gene is embedded, as well as the distance of the network output from an optimum. So, for instance, in a linear metabolic network, enzymes at the beginning of a pathway will tend to be more constrained (show less variation in the population) than at the end of the pathway. This makes sense given that any changes in those first genes would ripple through the system and have a greater relative effect than mutations in later genes. However, this is only true when a population is already close to an optimum. When far from an optimum, we see the exact opposite trend with more variation in the front of the pathway. This also makes sense - when far from a goal, taking bigger steps gives individuals a better chance of achieving higher fitness. The system as a whole then uses the different relative step sizes according to pathway position to "fine tune" its output.
I think these findings are quite general - at least the model we used was simple enough that it could apply to many different types of directional developmental processes. We can conceptualize these "genes" more generally as sequential steps in a developmental process with some arbitrary goal. These could be steps in a factory assembly line, major product revisions versus minor releases, or (and this is my favorite), neurons learning about their environment. I'm curious what would happen if we took a similar approach to model neural networks. Genetic variation is the raw material for evolution while neural plasticity is the raw material for learning. The question we would be trying to answer is where, within a neural network, would we expect the most plasticity given a particular network topology and distance form a learning goal.
Please contact me (Mark Longo) if this sounds interesting. I'll be available during unstructured time, or you can email mlongo@stanford.edu.
[http://tuvalu.santafe.edu/events/workshops/index.php/Mark_D._Longo]

=== Robustness of complex networks ===
[[File:Zoo.png|thumb|Fig. 1. Zoo of complex networks (an example). Taken from Sol´e and Valverde, 2001.]]
==== Problem statement ====
Complex networks have various properties which can be measured in real networks (WWW, social networks, biological networks), e.g. degree distribution, modularity, hierarchy, assortativity etc. Robustness of complex networks is a big question, however only some progress have been done in this direction. For example, it was shown that the scale-free networks are much more topologically robust to random attacks than random networks. Many people claim that various characteristics of complex networks will influence the robustness interdependently. The question I am interested in is how?

==== Approach ====
The idea is to generate continuous topology space of various complex networks (networks with different modularity, degree distribution, hierarchy etc) and use it to measure their robustness (see Fig. 1). There are many approaches to measure the robustness of complex networks. For example we can remove edges of vertices of a complex network graph and look at the size of a giant cluster. We can discuss other possibilities.

If you are interested you can contact me directly or via my E-mail: krystoferivanov@gmail.com or via my [[Oleksandr Ivanov|discussion page in CSSS 2012 wiki]].

Complex Systems Summer School 2012-Projects & Working Groups

2012-06-05T20:34:54Z

GeGoerg: /* Ideas on how to approach this */

{{Complex Systems Summer School 2012}}

==Project proposals==

=== Nonequilibrium game theory ===
My hope is to adapt some SFI-based models, by people like Crutchfield and Farmer, so that they will quantitatively or qualitatively reproduce features of real human data. Of course, that is very specific, and I'm up for all kinds of ideas in the areas of game learning, game dynamics, small group collective behavior, cognitive science, nonlinear time series, non-eq time series, etc., etc.

Meet me, Seth Frey, at dinner on Thursday and Friday.

=== Enzyme kinetics – Do enzymes just accelerate equilibrium or play an active role in chemical reactions? ===
Enzyme networks (e.g. glycolysis) and catalysts in complex mixtures (e.g. Belusov-Zhabotinski reaction) can profoundly influence the outcome of a chemical reaction system. What about a single enzyme? Biochemistry textbooks uniformly say that an enzyme accelerates a reaction without altering its outcome. Yet, the set of differential equations that generically describes enzyme catalysis has remarkable resemblance to the Roessler equations (a textbook example of a non-linear, complex system). With a fixed substrate input or a steady substrate flow, a single enzyme probably cannot affect the reaction outcome. However, sinusoidal or pulsating substrate input, substrate activation or product inhibition, coupling of two enzymes could turn the reaction pattern non-linear. For this project, the sets of equations to study are quite well established – they need to be analyzed. In contrast to some of the more ambitious ideas circulated, this task is exhaustively doable in less than four weeks.

I am Georg Weber. If you are interested in studying this problem, please find me on Tuesday over lunch or dinner (or talk to me at any other unstructured time).
=== Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how to approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

If you are interested to see more about this check out the details on the `SFI Project` subsection on [http://tuvalu.santafe.edu/events/workshops/index.php/Georg_M_Goerg Georg M. Goerg] or email me to my_3_initials_in_lowercase@stat.cmu.edu. Let's say we meet on Wednesday for lunch (or just ask me any other time you see me around).

=== Cultural Evolution - General Meet-up ===
Attention anyone who is interested in cultural evolution or applying your models/methodologies to this fabulous topic!

Let's meet at 4:15 (June 5th) in the cafe during the first "Time to work on Projects" slot. A bunch of us coalesced there tonight and figured we should all properly meet up and then bud off into different projects. Please post your potential buds below:

=== Cultural Evolution - things that look like drift but aren't ===
Lots of cultural evolution looks like drift (Bently et al 2004 'Random drift and culture change"). But what social transmission or cognitive learning mechanisms are isomorphic to random sampling with replacement from cultural inputs? In biological evolution, drift serves as a null model of sorts - one that should be ruled out before you can claim that anything more interesting is happening. However, it's not clear what the "uninteresting" type of change is for things that replicate by passing through human cognition and human social systems - like culture does. Is there even a reasonable equivalent of drift in cultural transmission? How should we go about conceptualizing and modeling the evolutionary forces at play in culture?

One candidate for a drifty-lookin' human behavior is probability matching: when people reproduce similar distributions of variation to that which they've learned from. And probability matching is rampant in human behavior (from language learning, to decision making, and even at the neural level). But I think this is a clearly different process than drift, however it still may qualify under Bentley's vague criteria - we can test that out. And there have got to be more drift-esque processes, anyone have any ideas?

If you're interested in these issues or modeling evolution (of any type of system), please give me a shout!

Vanessa

vanferdi [at] gmail.com

==="Small Steps and Big Ideas" Group===

[http://tuvalu.santafe.edu/events/workshops/index.php/Christa_Brelsford Christa] [http://tuvalu.santafe.edu/events/workshops/index.php/Daniel_Wu Dan] [http://tuvalu.santafe.edu/events/workshops/index.php/Xin_Lu Xin] and Tom spent a while talking after dinner about a bunch of big ideas. Some things we thought about were *big data type network problems, *integrating qualitative social information with models of physical systems, *using games to understand cooperation and decision making.

We'll meet at dinner at 5:30 today (Tuesday, June 5th) in the cafeteria.

=== 107 Proteins in 10-15 cubic meters ===
Cells rely on proteins to perform vital metabolic and signaling functions; however, it is unclear how proteins are successfully directed to their necessary cellular location(s) in a densely-packed macromolecular environment within the cytoplasm and on the cellular membrane in a short timescale (see for example [http://www.pnas.org/content/108/16/6438.full Weigel et al., PNAS 2011]). Using the cell as a manipulatable model of complexity, one could begin to define the parameters and questions, as they pertain to prokaryotic and eukaryotic cells. If interested, please drop me a line: Sepehr Ehsani; sepehr.ehsani[at]utoronto.ca.

=== Innovation and Technological Progress ===

I noticed that a number of people mentioned that they were interested in some way in relation to innovation. I was wondering if anyone was interested in a project looking at how particular technologies progress over time and whether charting the form of successful (and/or unsuccessful) previous technologies such as the transistor, fission reactor, mobile phone, etc. in terms of either price, efficiency, or some other variable may be useful in predicting whether a current technology such as solar PV, fuel cell, or something else is following a similar trajectory. Other possible ideas might be to look at using patent, publication, or collaboration network data to reveal certain features of innovation that are not captured by other statistics, particularly for technologies that have yet to reach the market. SFI Professor Doyne Farmer has looked at some of this already in 'The Role of Design Complexity in Technology Improvement', see link: http://adsabs.harvard.edu/abs/2009arXiv0907.0036M

This could be a jumping off point for some ideas on data, methods, models etc. Just throwing the idea out there and it's welcome to completely change but if you're interested, message me (Gareth Haslam) haslam@ias.unu.edu or find me in class.

=== Space, Stochasticity, Stability; Speciation? ===

[http://tuvalu.santafe.edu/events/workshops/index.php/Xue_Feng Xue], [http://tuvalu.santafe.edu/events/workshops/index.php/Chloe_Lewis Chloe] and [http://tuvalu.santafe.edu/events/workshops/index.php/Xiaoli_Dong Xiaoli]are all working in ecosystems that experience_ a lot of unpredictability in a limiting ecosystem variables (water and/or nutrients); we see patchiness in space and time in how organisms are arranged; and we have some ideas about how the stochasticity may cause the spatial arrangements. [http://tuvalu.santafe.edu/events/workshops/index.php/Si_Tang Si] is working on the stability and robustness of ecosystems.

With enough time, this is likely to involve speciation either to express different strategies, or as a result of spatial separation.

Find any of us walking-around, or meet in the cafeteria at 4:15 June 5th.

=== Plasticity in Neural Networks ===
I've done some modeling which shows that the amount of genetic variation that accumulates at any particular metabolic gene (enzyme) in a population at any given time is a function of the network topology in which the gene is embedded, as well as the distance of the network output from an optimum. So, for instance, in a linear metabolic network, enzymes at the beginning of a pathway will tend to be more constrained (show less variation in the population) than at the end of the pathway. This makes sense given that any changes in those first genes would ripple through the system and have a greater relative effect than mutations in later genes. However, this is only true when a population is already close to an optimum. When far from an optimum, we see the exact opposite trend with more variation in the front of the pathway. This also makes sense - when far from a goal, taking bigger steps gives individuals a better chance of achieving higher fitness. The system as a whole then uses the different relative step sizes according to pathway position to "fine tune" its output.
I think these findings are quite general - at least the model we used was simple enough that it could apply to many different types of directional developmental processes. We can conceptualize these "genes" more generally as sequential steps in a developmental process with some arbitrary goal. These could be steps in a factory assembly line, major product revisions versus minor releases, or (and this is my favorite), neurons learning about their environment. I'm curious what would happen if we took a similar approach to model neural networks. Genetic variation is the raw material for evolution while neural plasticity is the raw material for learning. The question we would be trying to answer is where, within a neural network, would we expect the most plasticity given a particular network topology and distance form a learning goal.
Please contact me (Mark Longo) if this sounds interesting. I'll be available during unstructured time, or you can email mlongo@stanford.edu.
[http://tuvalu.santafe.edu/events/workshops/index.php/Mark_D._Longo]

=== Robustness of complex networks ===
[[File:Zoo.png|thumb|Fig. 1. Zoo of complex networks (an example). Taken from Sol´e and Valverde, 2001.]]
==== Problem statement ====
Complex networks have various properties which can be measured in real networks (WWW, social networks, biological networks), e.g. degree distribution, modularity, hierarchy, assortativity etc. Robustness of complex networks is a big question, however only some progress have been done in this direction. For example, it was shown that the scale-free networks are much more topologically robust to random attacks than random networks. Many people claim that various characteristics of complex networks will influence the robustness interdependently. The question I am interested in is how?

==== Approach ====
The idea is to generate continuous topology space of various complex networks (networks with different modularity, degree distribution, hierarchy etc) and use it to measure their robustness (see Fig. 1). There are many approaches to measure the robustness of complex networks. For example we can remove edges of vertices of a complex network graph and look at the size of a giant cluster. We can discuss other possibilities.

If you are interested you can contact me directly or via my E-mail: krystoferivanov@gmail.com or via my [[Oleksandr Ivanov|discussion page in CSSS 2012 wiki]].

Complex Systems Summer School 2012-Projects & Working Groups

2012-06-05T20:34:30Z

GeGoerg: /* Ideas on how to approach this */

{{Complex Systems Summer School 2012}}

==Project proposals==

=== Nonequilibrium game theory ===
My hope is to adapt some SFI-based models, by people like Crutchfield and Farmer, so that they will quantitatively or qualitatively reproduce features of real human data. Of course, that is very specific, and I'm up for all kinds of ideas in the areas of game learning, game dynamics, small group collective behavior, cognitive science, nonlinear time series, non-eq time series, etc., etc.

Meet me, Seth Frey, at dinner on Thursday and Friday.

=== Enzyme kinetics – Do enzymes just accelerate equilibrium or play an active role in chemical reactions? ===
Enzyme networks (e.g. glycolysis) and catalysts in complex mixtures (e.g. Belusov-Zhabotinski reaction) can profoundly influence the outcome of a chemical reaction system. What about a single enzyme? Biochemistry textbooks uniformly say that an enzyme accelerates a reaction without altering its outcome. Yet, the set of differential equations that generically describes enzyme catalysis has remarkable resemblance to the Roessler equations (a textbook example of a non-linear, complex system). With a fixed substrate input or a steady substrate flow, a single enzyme probably cannot affect the reaction outcome. However, sinusoidal or pulsating substrate input, substrate activation or product inhibition, coupling of two enzymes could turn the reaction pattern non-linear. For this project, the sets of equations to study are quite well established – they need to be analyzed. In contrast to some of the more ambitious ideas circulated, this task is exhaustively doable in less than four weeks.

I am Georg Weber. If you are interested in studying this problem, please find me on Tuesday over lunch or dinner (or talk to me at any other unstructured time).
=== Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how to approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

If you are interested to see more about this check out the details on the `SFI Project` subsection on my site, [http://tuvalu.santafe.edu/events/workshops/index.php/Georg_M_Goerg Georg M. Goerg], or email me to my_3_initials_in_lowercase@stat.cmu.edu). Let's say we meet on Wednesday for lunch (or just ask me any other time you see me around).

=== Cultural Evolution - General Meet-up ===
Attention anyone who is interested in cultural evolution or applying your models/methodologies to this fabulous topic!

Let's meet at 4:15 (June 5th) in the cafe during the first "Time to work on Projects" slot. A bunch of us coalesced there tonight and figured we should all properly meet up and then bud off into different projects. Please post your potential buds below:

=== Cultural Evolution - things that look like drift but aren't ===
Lots of cultural evolution looks like drift (Bently et al 2004 'Random drift and culture change"). But what social transmission or cognitive learning mechanisms are isomorphic to random sampling with replacement from cultural inputs? In biological evolution, drift serves as a null model of sorts - one that should be ruled out before you can claim that anything more interesting is happening. However, it's not clear what the "uninteresting" type of change is for things that replicate by passing through human cognition and human social systems - like culture does. Is there even a reasonable equivalent of drift in cultural transmission? How should we go about conceptualizing and modeling the evolutionary forces at play in culture?

One candidate for a drifty-lookin' human behavior is probability matching: when people reproduce similar distributions of variation to that which they've learned from. And probability matching is rampant in human behavior (from language learning, to decision making, and even at the neural level). But I think this is a clearly different process than drift, however it still may qualify under Bentley's vague criteria - we can test that out. And there have got to be more drift-esque processes, anyone have any ideas?

If you're interested in these issues or modeling evolution (of any type of system), please give me a shout!

Vanessa

vanferdi [at] gmail.com

==="Small Steps and Big Ideas" Group===

[http://tuvalu.santafe.edu/events/workshops/index.php/Christa_Brelsford Christa] [http://tuvalu.santafe.edu/events/workshops/index.php/Daniel_Wu Dan] [http://tuvalu.santafe.edu/events/workshops/index.php/Xin_Lu Xin] and Tom spent a while talking after dinner about a bunch of big ideas. Some things we thought about were *big data type network problems, *integrating qualitative social information with models of physical systems, *using games to understand cooperation and decision making.

We'll meet at dinner at 5:30 today (Tuesday, June 5th) in the cafeteria.

=== 107 Proteins in 10-15 cubic meters ===
Cells rely on proteins to perform vital metabolic and signaling functions; however, it is unclear how proteins are successfully directed to their necessary cellular location(s) in a densely-packed macromolecular environment within the cytoplasm and on the cellular membrane in a short timescale (see for example [http://www.pnas.org/content/108/16/6438.full Weigel et al., PNAS 2011]). Using the cell as a manipulatable model of complexity, one could begin to define the parameters and questions, as they pertain to prokaryotic and eukaryotic cells. If interested, please drop me a line: Sepehr Ehsani; sepehr.ehsani[at]utoronto.ca.

=== Innovation and Technological Progress ===

I noticed that a number of people mentioned that they were interested in some way in relation to innovation. I was wondering if anyone was interested in a project looking at how particular technologies progress over time and whether charting the form of successful (and/or unsuccessful) previous technologies such as the transistor, fission reactor, mobile phone, etc. in terms of either price, efficiency, or some other variable may be useful in predicting whether a current technology such as solar PV, fuel cell, or something else is following a similar trajectory. Other possible ideas might be to look at using patent, publication, or collaboration network data to reveal certain features of innovation that are not captured by other statistics, particularly for technologies that have yet to reach the market. SFI Professor Doyne Farmer has looked at some of this already in 'The Role of Design Complexity in Technology Improvement', see link: http://adsabs.harvard.edu/abs/2009arXiv0907.0036M

This could be a jumping off point for some ideas on data, methods, models etc. Just throwing the idea out there and it's welcome to completely change but if you're interested, message me (Gareth Haslam) haslam@ias.unu.edu or find me in class.

=== Space, Stochasticity, Stability; Speciation? ===

[http://tuvalu.santafe.edu/events/workshops/index.php/Xue_Feng Xue], [http://tuvalu.santafe.edu/events/workshops/index.php/Chloe_Lewis Chloe] and [http://tuvalu.santafe.edu/events/workshops/index.php/Xiaoli_Dong Xiaoli]are all working in ecosystems that experience_ a lot of unpredictability in a limiting ecosystem variables (water and/or nutrients); we see patchiness in space and time in how organisms are arranged; and we have some ideas about how the stochasticity may cause the spatial arrangements. [http://tuvalu.santafe.edu/events/workshops/index.php/Si_Tang Si] is working on the stability and robustness of ecosystems.

With enough time, this is likely to involve speciation either to express different strategies, or as a result of spatial separation.

Find any of us walking-around, or meet in the cafeteria at 4:15 June 5th.

=== Plasticity in Neural Networks ===
I've done some modeling which shows that the amount of genetic variation that accumulates at any particular metabolic gene (enzyme) in a population at any given time is a function of the network topology in which the gene is embedded, as well as the distance of the network output from an optimum. So, for instance, in a linear metabolic network, enzymes at the beginning of a pathway will tend to be more constrained (show less variation in the population) than at the end of the pathway. This makes sense given that any changes in those first genes would ripple through the system and have a greater relative effect than mutations in later genes. However, this is only true when a population is already close to an optimum. When far from an optimum, we see the exact opposite trend with more variation in the front of the pathway. This also makes sense - when far from a goal, taking bigger steps gives individuals a better chance of achieving higher fitness. The system as a whole then uses the different relative step sizes according to pathway position to "fine tune" its output.
I think these findings are quite general - at least the model we used was simple enough that it could apply to many different types of directional developmental processes. We can conceptualize these "genes" more generally as sequential steps in a developmental process with some arbitrary goal. These could be steps in a factory assembly line, major product revisions versus minor releases, or (and this is my favorite), neurons learning about their environment. I'm curious what would happen if we took a similar approach to model neural networks. Genetic variation is the raw material for evolution while neural plasticity is the raw material for learning. The question we would be trying to answer is where, within a neural network, would we expect the most plasticity given a particular network topology and distance form a learning goal.
Please contact me (Mark Longo) if this sounds interesting. I'll be available during unstructured time, or you can email mlongo@stanford.edu.
[http://tuvalu.santafe.edu/events/workshops/index.php/Mark_D._Longo]

=== Robustness of complex networks ===
[[File:Zoo.png|thumb|Fig. 1. Zoo of complex networks (an example). Taken from Sol´e and Valverde, 2001.]]
==== Problem statement ====
Complex networks have various properties which can be measured in real networks (WWW, social networks, biological networks), e.g. degree distribution, modularity, hierarchy, assortativity etc. Robustness of complex networks is a big question, however only some progress have been done in this direction. For example, it was shown that the scale-free networks are much more topologically robust to random attacks than random networks. Many people claim that various characteristics of complex networks will influence the robustness interdependently. The question I am interested in is how?

==== Approach ====
The idea is to generate continuous topology space of various complex networks (networks with different modularity, degree distribution, hierarchy etc) and use it to measure their robustness (see Fig. 1). There are many approaches to measure the robustness of complex networks. For example we can remove edges of vertices of a complex network graph and look at the size of a giant cluster. We can discuss other possibilities.

If you are interested you can contact me directly or via my E-mail: krystoferivanov@gmail.com or via my [[Oleksandr Ivanov|discussion page in CSSS 2012 wiki]].

Complex Systems Summer School 2012-Projects & Working Groups

2012-06-05T20:34:00Z

GeGoerg: /* Ideas on how to approach this */

{{Complex Systems Summer School 2012}}

==Project proposals==

=== Nonequilibrium game theory ===
My hope is to adapt some SFI-based models, by people like Crutchfield and Farmer, so that they will quantitatively or qualitatively reproduce features of real human data. Of course, that is very specific, and I'm up for all kinds of ideas in the areas of game learning, game dynamics, small group collective behavior, cognitive science, nonlinear time series, non-eq time series, etc., etc.

Meet me, Seth Frey, at dinner on Thursday and Friday.

=== Enzyme kinetics – Do enzymes just accelerate equilibrium or play an active role in chemical reactions? ===
Enzyme networks (e.g. glycolysis) and catalysts in complex mixtures (e.g. Belusov-Zhabotinski reaction) can profoundly influence the outcome of a chemical reaction system. What about a single enzyme? Biochemistry textbooks uniformly say that an enzyme accelerates a reaction without altering its outcome. Yet, the set of differential equations that generically describes enzyme catalysis has remarkable resemblance to the Roessler equations (a textbook example of a non-linear, complex system). With a fixed substrate input or a steady substrate flow, a single enzyme probably cannot affect the reaction outcome. However, sinusoidal or pulsating substrate input, substrate activation or product inhibition, coupling of two enzymes could turn the reaction pattern non-linear. For this project, the sets of equations to study are quite well established – they need to be analyzed. In contrast to some of the more ambitious ideas circulated, this task is exhaustively doable in less than four weeks.

I am Georg Weber. If you are interested in studying this problem, please find me on Tuesday over lunch or dinner (or talk to me at any other unstructured time).
=== Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how to approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

If you are interested to see more about this check out the details on the ``SFI Project'' subsection on my site, [http://tuvalu.santafe.edu/events/workshops/index.php/Georg_M_Goerg Georg M. Goerg], or email me to my_3_initials_in_lowercase@stat.cmu.edu). Let's say we meet on Wednesday for lunch (or just ask me any other time you see me around).

=== Cultural Evolution - General Meet-up ===
Attention anyone who is interested in cultural evolution or applying your models/methodologies to this fabulous topic!

Let's meet at 4:15 (June 5th) in the cafe during the first "Time to work on Projects" slot. A bunch of us coalesced there tonight and figured we should all properly meet up and then bud off into different projects. Please post your potential buds below:

=== Cultural Evolution - things that look like drift but aren't ===
Lots of cultural evolution looks like drift (Bently et al 2004 'Random drift and culture change"). But what social transmission or cognitive learning mechanisms are isomorphic to random sampling with replacement from cultural inputs? In biological evolution, drift serves as a null model of sorts - one that should be ruled out before you can claim that anything more interesting is happening. However, it's not clear what the "uninteresting" type of change is for things that replicate by passing through human cognition and human social systems - like culture does. Is there even a reasonable equivalent of drift in cultural transmission? How should we go about conceptualizing and modeling the evolutionary forces at play in culture?

One candidate for a drifty-lookin' human behavior is probability matching: when people reproduce similar distributions of variation to that which they've learned from. And probability matching is rampant in human behavior (from language learning, to decision making, and even at the neural level). But I think this is a clearly different process than drift, however it still may qualify under Bentley's vague criteria - we can test that out. And there have got to be more drift-esque processes, anyone have any ideas?

If you're interested in these issues or modeling evolution (of any type of system), please give me a shout!

Vanessa

vanferdi [at] gmail.com

==="Small Steps and Big Ideas" Group===

[http://tuvalu.santafe.edu/events/workshops/index.php/Christa_Brelsford Christa] [http://tuvalu.santafe.edu/events/workshops/index.php/Daniel_Wu Dan] [http://tuvalu.santafe.edu/events/workshops/index.php/Xin_Lu Xin] and Tom spent a while talking after dinner about a bunch of big ideas. Some things we thought about were *big data type network problems, *integrating qualitative social information with models of physical systems, *using games to understand cooperation and decision making.

We'll meet at dinner at 5:30 today (Tuesday, June 5th) in the cafeteria.

=== 107 Proteins in 10-15 cubic meters ===
Cells rely on proteins to perform vital metabolic and signaling functions; however, it is unclear how proteins are successfully directed to their necessary cellular location(s) in a densely-packed macromolecular environment within the cytoplasm and on the cellular membrane in a short timescale (see for example [http://www.pnas.org/content/108/16/6438.full Weigel et al., PNAS 2011]). Using the cell as a manipulatable model of complexity, one could begin to define the parameters and questions, as they pertain to prokaryotic and eukaryotic cells. If interested, please drop me a line: Sepehr Ehsani; sepehr.ehsani[at]utoronto.ca.

=== Innovation and Technological Progress ===

I noticed that a number of people mentioned that they were interested in some way in relation to innovation. I was wondering if anyone was interested in a project looking at how particular technologies progress over time and whether charting the form of successful (and/or unsuccessful) previous technologies such as the transistor, fission reactor, mobile phone, etc. in terms of either price, efficiency, or some other variable may be useful in predicting whether a current technology such as solar PV, fuel cell, or something else is following a similar trajectory. Other possible ideas might be to look at using patent, publication, or collaboration network data to reveal certain features of innovation that are not captured by other statistics, particularly for technologies that have yet to reach the market. SFI Professor Doyne Farmer has looked at some of this already in 'The Role of Design Complexity in Technology Improvement', see link: http://adsabs.harvard.edu/abs/2009arXiv0907.0036M

This could be a jumping off point for some ideas on data, methods, models etc. Just throwing the idea out there and it's welcome to completely change but if you're interested, message me (Gareth Haslam) haslam@ias.unu.edu or find me in class.

=== Space, Stochasticity, Stability; Speciation? ===

[http://tuvalu.santafe.edu/events/workshops/index.php/Xue_Feng Xue], [http://tuvalu.santafe.edu/events/workshops/index.php/Chloe_Lewis Chloe] and [http://tuvalu.santafe.edu/events/workshops/index.php/Xiaoli_Dong Xiaoli]are all working in ecosystems that experience_ a lot of unpredictability in a limiting ecosystem variables (water and/or nutrients); we see patchiness in space and time in how organisms are arranged; and we have some ideas about how the stochasticity may cause the spatial arrangements. [http://tuvalu.santafe.edu/events/workshops/index.php/Si_Tang Si] is working on the stability and robustness of ecosystems.

With enough time, this is likely to involve speciation either to express different strategies, or as a result of spatial separation.

Find any of us walking-around, or meet in the cafeteria at 4:15 June 5th.

=== Plasticity in Neural Networks ===
I've done some modeling which shows that the amount of genetic variation that accumulates at any particular metabolic gene (enzyme) in a population at any given time is a function of the network topology in which the gene is embedded, as well as the distance of the network output from an optimum. So, for instance, in a linear metabolic network, enzymes at the beginning of a pathway will tend to be more constrained (show less variation in the population) than at the end of the pathway. This makes sense given that any changes in those first genes would ripple through the system and have a greater relative effect than mutations in later genes. However, this is only true when a population is already close to an optimum. When far from an optimum, we see the exact opposite trend with more variation in the front of the pathway. This also makes sense - when far from a goal, taking bigger steps gives individuals a better chance of achieving higher fitness. The system as a whole then uses the different relative step sizes according to pathway position to "fine tune" its output.
I think these findings are quite general - at least the model we used was simple enough that it could apply to many different types of directional developmental processes. We can conceptualize these "genes" more generally as sequential steps in a developmental process with some arbitrary goal. These could be steps in a factory assembly line, major product revisions versus minor releases, or (and this is my favorite), neurons learning about their environment. I'm curious what would happen if we took a similar approach to model neural networks. Genetic variation is the raw material for evolution while neural plasticity is the raw material for learning. The question we would be trying to answer is where, within a neural network, would we expect the most plasticity given a particular network topology and distance form a learning goal.
Please contact me (Mark Longo) if this sounds interesting. I'll be available during unstructured time, or you can email mlongo@stanford.edu.
[http://tuvalu.santafe.edu/events/workshops/index.php/Mark_D._Longo]

=== Robustness of complex networks ===
[[File:Zoo.png|thumb|Fig. 1. Zoo of complex networks (an example). Taken from Sol´e and Valverde, 2001.]]
==== Problem statement ====
Complex networks have various properties which can be measured in real networks (WWW, social networks, biological networks), e.g. degree distribution, modularity, hierarchy, assortativity etc. Robustness of complex networks is a big question, however only some progress have been done in this direction. For example, it was shown that the scale-free networks are much more topologically robust to random attacks than random networks. Many people claim that various characteristics of complex networks will influence the robustness interdependently. The question I am interested in is how?

==== Approach ====
The idea is to generate continuous topology space of various complex networks (networks with different modularity, degree distribution, hierarchy etc) and use it to measure their robustness (see Fig. 1). There are many approaches to measure the robustness of complex networks. For example we can remove edges of vertices of a complex network graph and look at the size of a giant cluster. We can discuss other possibilities.

If you are interested you can contact me directly or via my E-mail: krystoferivanov@gmail.com or via my [[Oleksandr Ivanov|discussion page in CSSS 2012 wiki]].

Georg M Goerg

2012-06-05T20:32:01Z

GeGoerg: /* Formal details */

[[File:RapaNui2.jpg|thumb|left|alt=Me in Rapa Nui.|Me (left).]]

== My path to SFI ==
I am a PhD candidate (starting 4th year) in [http://www.stat.cmu.edu/ Statistics at Carnegie Mellon]. I received my masters in mathematics (applied / econometrics / time series) from the Vienna University of Technology, Austria and before coming to the US, I spent a year in Chile teaching statistics (mainly time series) at PUC. For more details you can visit [http://www.stat.cmu.edu/~gmg/ my website]. You can email me at "my_3_initials_in_lowercase"@stat.cmu.edu.

I am very eager to participate in the CSSS; especially because of the inter-disciplinary research / collaborations on real world problems with people from many backgrounds - that's what statistics is all about (at least for me). So I am looking forward to meeting all of you and I am sure we'll have a great month ahead of us.

== Research Interests ==

In my thesis I work on local statistical complexity (LSC) - a measure of
''interestingness'' for spatio-temporal fields. We develop the
statistical methods and algorithms to i) forecast a spatio-temporal
system, and ii) discover patterns automatically solely from the data. We
do this using modern non-parametric statistical / machine learning
techniques with good properties for any kind of (complex)
spatio-temporal system.

One reason why I work on spatio-temporal systems is that I have always been drawn to time series (a la "My interest lies in the future because I am going to spend the rest of my life there. ” - Charles F. Kettering) and methods that try to solve real-world problems. These include time series clustering, forecasting, blind source separation techniques for forecastable time series, time-varying parameter models. Another side-project are skewed and heavy-tailed distributions, in particular how we can transform random variables to introduce skewness and heavy tails. And as a statistician what's even more relevant to me is how can I reverse this transformation so I can take data and remove skewness, remove power laws, remove heavy tails.

I do all my statistical computing in R -- for user-friendly code and R packages (two so far), and Python -- for huge data tasks.

In my spare time I like to play soccer, volleyball, salsa dancing, traveling, ...

== SFI Project: Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how to approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

=== Math / Statistics ===

==== Conceptual view ====

[http://www.jstor.org/stable/10.2307/2646712 Parke] proposes an error duration model (EDM) for how time series observed in a system happen to form:

''The basic mechanism for an error duration model is a sequence of shocks of stochastic magnitude and stochastic duration. The variable observed in a given period is the sum of those shocks that survive to that point.''

The point of this formulation is that the distribution of the (unobserved) survival times determines the correlation structure of the observed series. Thus vice-versa we should be able to infer the lifetime distribution of the shocks from the correlation structure. The point of this is that in practice we don't observe neither the individual shocks nor their lifetime, but we can estimate the correlations of the observations.
Thus in principle it should be possible to infer/estimate the lifetime distribution only from the counts.

==== Formal details ====

Follows later or link to external pdf.

Georg M Goerg

2012-06-05T20:31:36Z

GeGoerg: /* Formal details */

[[File:RapaNui2.jpg|thumb|left|alt=Me in Rapa Nui.|Me (left).]]

== My path to SFI ==
I am a PhD candidate (starting 4th year) in [http://www.stat.cmu.edu/ Statistics at Carnegie Mellon]. I received my masters in mathematics (applied / econometrics / time series) from the Vienna University of Technology, Austria and before coming to the US, I spent a year in Chile teaching statistics (mainly time series) at PUC. For more details you can visit [http://www.stat.cmu.edu/~gmg/ my website]. You can email me at "my_3_initials_in_lowercase"@stat.cmu.edu.

I am very eager to participate in the CSSS; especially because of the inter-disciplinary research / collaborations on real world problems with people from many backgrounds - that's what statistics is all about (at least for me). So I am looking forward to meeting all of you and I am sure we'll have a great month ahead of us.

== Research Interests ==

In my thesis I work on local statistical complexity (LSC) - a measure of
''interestingness'' for spatio-temporal fields. We develop the
statistical methods and algorithms to i) forecast a spatio-temporal
system, and ii) discover patterns automatically solely from the data. We
do this using modern non-parametric statistical / machine learning
techniques with good properties for any kind of (complex)
spatio-temporal system.

One reason why I work on spatio-temporal systems is that I have always been drawn to time series (a la "My interest lies in the future because I am going to spend the rest of my life there. ” - Charles F. Kettering) and methods that try to solve real-world problems. These include time series clustering, forecasting, blind source separation techniques for forecastable time series, time-varying parameter models. Another side-project are skewed and heavy-tailed distributions, in particular how we can transform random variables to introduce skewness and heavy tails. And as a statistician what's even more relevant to me is how can I reverse this transformation so I can take data and remove skewness, remove power laws, remove heavy tails.

I do all my statistical computing in R -- for user-friendly code and R packages (two so far), and Python -- for huge data tasks.

In my spare time I like to play soccer, volleyball, salsa dancing, traveling, ...

== SFI Project: Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how to approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

=== Math / Statistics ===

==== Conceptual view ====

[http://www.jstor.org/stable/10.2307/2646712 Parke] proposes an error duration model (EDM) for how time series observed in a system happen to form:

''The basic mechanism for an error duration model is a sequence of shocks of stochastic magnitude and stochastic duration. The variable observed in a given period is the sum of those shocks that survive to that point.''

The point of this formulation is that the distribution of the (unobserved) survival times determines the correlation structure of the observed series. Thus vice-versa we should be able to infer the lifetime distribution of the shocks from the correlation structure. The point of this is that in practice we don't observe neither the individual shocks nor their lifetime, but we can estimate the correlations of the observations.
Thus in principle it should be possible to infer/estimate the lifetime distribution only from the counts.

==== Formal details ====
The general representation of an autoregressive model, well known as AR(''p''), is

: <math> Y_t =\alpha_0+\alpha_1 Y_{t-1}+\alpha_2 Y_{t-2}+\cdots+\alpha_p Y_{t-p}+\varepsilon_t\, </math>

where the term ε''t'' is the source of randomness and is called [[white noise]]. It is assumed to have the following characteristics:

Georg M Goerg

2012-06-05T20:31:11Z

GeGoerg: /* Formal details */

[[File:RapaNui2.jpg|thumb|left|alt=Me in Rapa Nui.|Me (left).]]

== My path to SFI ==
I am a PhD candidate (starting 4th year) in [http://www.stat.cmu.edu/ Statistics at Carnegie Mellon]. I received my masters in mathematics (applied / econometrics / time series) from the Vienna University of Technology, Austria and before coming to the US, I spent a year in Chile teaching statistics (mainly time series) at PUC. For more details you can visit [http://www.stat.cmu.edu/~gmg/ my website]. You can email me at "my_3_initials_in_lowercase"@stat.cmu.edu.

I am very eager to participate in the CSSS; especially because of the inter-disciplinary research / collaborations on real world problems with people from many backgrounds - that's what statistics is all about (at least for me). So I am looking forward to meeting all of you and I am sure we'll have a great month ahead of us.

== Research Interests ==

In my thesis I work on local statistical complexity (LSC) - a measure of
''interestingness'' for spatio-temporal fields. We develop the
statistical methods and algorithms to i) forecast a spatio-temporal
system, and ii) discover patterns automatically solely from the data. We
do this using modern non-parametric statistical / machine learning
techniques with good properties for any kind of (complex)
spatio-temporal system.

One reason why I work on spatio-temporal systems is that I have always been drawn to time series (a la "My interest lies in the future because I am going to spend the rest of my life there. ” - Charles F. Kettering) and methods that try to solve real-world problems. These include time series clustering, forecasting, blind source separation techniques for forecastable time series, time-varying parameter models. Another side-project are skewed and heavy-tailed distributions, in particular how we can transform random variables to introduce skewness and heavy tails. And as a statistician what's even more relevant to me is how can I reverse this transformation so I can take data and remove skewness, remove power laws, remove heavy tails.

I do all my statistical computing in R -- for user-friendly code and R packages (two so far), and Python -- for huge data tasks.

In my spare time I like to play soccer, volleyball, salsa dancing, traveling, ...

== SFI Project: Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how to approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

=== Math / Statistics ===

==== Conceptual view ====

[http://www.jstor.org/stable/10.2307/2646712 Parke] proposes an error duration model (EDM) for how time series observed in a system happen to form:

''The basic mechanism for an error duration model is a sequence of shocks of stochastic magnitude and stochastic duration. The variable observed in a given period is the sum of those shocks that survive to that point.''

The point of this formulation is that the distribution of the (unobserved) survival times determines the correlation structure of the observed series. Thus vice-versa we should be able to infer the lifetime distribution of the shocks from the correlation structure. The point of this is that in practice we don't observe neither the individual shocks nor their lifetime, but we can estimate the correlations of the observations.
Thus in principle it should be possible to infer/estimate the lifetime distribution only from the counts.

==== Formal details ====

: <math> X_t = c + \varepsilon_t + \sum_{i=1}^p \varphi_i X_{t-i} + \sum_{i=1}^q \theta_i \varepsilon_{t-i}.\,</math>

Georg M Goerg

2012-06-05T20:30:35Z

GeGoerg: /* More details */

[[File:RapaNui2.jpg|thumb|left|alt=Me in Rapa Nui.|Me (left).]]

== My path to SFI ==
I am a PhD candidate (starting 4th year) in [http://www.stat.cmu.edu/ Statistics at Carnegie Mellon]. I received my masters in mathematics (applied / econometrics / time series) from the Vienna University of Technology, Austria and before coming to the US, I spent a year in Chile teaching statistics (mainly time series) at PUC. For more details you can visit [http://www.stat.cmu.edu/~gmg/ my website]. You can email me at "my_3_initials_in_lowercase"@stat.cmu.edu.

I am very eager to participate in the CSSS; especially because of the inter-disciplinary research / collaborations on real world problems with people from many backgrounds - that's what statistics is all about (at least for me). So I am looking forward to meeting all of you and I am sure we'll have a great month ahead of us.

== Research Interests ==

In my thesis I work on local statistical complexity (LSC) - a measure of
''interestingness'' for spatio-temporal fields. We develop the
statistical methods and algorithms to i) forecast a spatio-temporal
system, and ii) discover patterns automatically solely from the data. We
do this using modern non-parametric statistical / machine learning
techniques with good properties for any kind of (complex)
spatio-temporal system.

One reason why I work on spatio-temporal systems is that I have always been drawn to time series (a la "My interest lies in the future because I am going to spend the rest of my life there. ” - Charles F. Kettering) and methods that try to solve real-world problems. These include time series clustering, forecasting, blind source separation techniques for forecastable time series, time-varying parameter models. Another side-project are skewed and heavy-tailed distributions, in particular how we can transform random variables to introduce skewness and heavy tails. And as a statistician what's even more relevant to me is how can I reverse this transformation so I can take data and remove skewness, remove power laws, remove heavy tails.

I do all my statistical computing in R -- for user-friendly code and R packages (two so far), and Python -- for huge data tasks.

In my spare time I like to play soccer, volleyball, salsa dancing, traveling, ...

== SFI Project: Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how to approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

=== Math / Statistics ===

==== Conceptual view ====

[http://www.jstor.org/stable/10.2307/2646712 Parke] proposes an error duration model (EDM) for how time series observed in a system happen to form:

''The basic mechanism for an error duration model is a sequence of shocks of stochastic magnitude and stochastic duration. The variable observed in a given period is the sum of those shocks that survive to that point.''

The point of this formulation is that the distribution of the (unobserved) survival times determines the correlation structure of the observed series. Thus vice-versa we should be able to infer the lifetime distribution of the shocks from the correlation structure. The point of this is that in practice we don't observe neither the individual shocks nor their lifetime, but we can estimate the correlations of the observations.
Thus in principle it should be possible to infer/estimate the lifetime distribution only from the counts.

==== Formal details ====

:<math> X_t = c + \varepsilon_t + \sum_{i=1}^p \varphi_i X_{t-i} + \sum_{i=1}^q \theta_i \varepsilon_{t-i}.\,</math>

Georg M Goerg

2012-06-05T20:26:47Z

GeGoerg:

[[File:RapaNui2.jpg|thumb|left|alt=Me in Rapa Nui.|Me (left).]]

== My path to SFI ==
I am a PhD candidate (starting 4th year) in [http://www.stat.cmu.edu/ Statistics at Carnegie Mellon]. I received my masters in mathematics (applied / econometrics / time series) from the Vienna University of Technology, Austria and before coming to the US, I spent a year in Chile teaching statistics (mainly time series) at PUC. For more details you can visit [http://www.stat.cmu.edu/~gmg/ my website]. You can email me at "my_3_initials_in_lowercase"@stat.cmu.edu.

I am very eager to participate in the CSSS; especially because of the inter-disciplinary research / collaborations on real world problems with people from many backgrounds - that's what statistics is all about (at least for me). So I am looking forward to meeting all of you and I am sure we'll have a great month ahead of us.

== Research Interests ==

In my thesis I work on local statistical complexity (LSC) - a measure of
''interestingness'' for spatio-temporal fields. We develop the
statistical methods and algorithms to i) forecast a spatio-temporal
system, and ii) discover patterns automatically solely from the data. We
do this using modern non-parametric statistical / machine learning
techniques with good properties for any kind of (complex)
spatio-temporal system.

One reason why I work on spatio-temporal systems is that I have always been drawn to time series (a la "My interest lies in the future because I am going to spend the rest of my life there. ” - Charles F. Kettering) and methods that try to solve real-world problems. These include time series clustering, forecasting, blind source separation techniques for forecastable time series, time-varying parameter models. Another side-project are skewed and heavy-tailed distributions, in particular how we can transform random variables to introduce skewness and heavy tails. And as a statistician what's even more relevant to me is how can I reverse this transformation so I can take data and remove skewness, remove power laws, remove heavy tails.

I do all my statistical computing in R -- for user-friendly code and R packages (two so far), and Python -- for huge data tasks.

In my spare time I like to play soccer, volleyball, salsa dancing, traveling, ...

== SFI Project: Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how to approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

=== Math / Statistics ===

==== Conceptual view ====

[http://www.jstor.org/stable/10.2307/2646712 Parke] proposes an error duration model (EDM) for how time series observed in a system happen to form:

''The basic mechanism for an error duration model is a sequence of shocks of stochastic magnitude and stochastic duration. The variable observed in a given period is the sum of those shocks that survive to that point.''

The point of this formulation is that the distribution of the (unobserved) survival times determines the correlation structure of the observed series. Thus vice-versa we should be able to infer the lifetime distribution of the shocks from the correlation structure. The point of this is that in practice we don't observe neither the individual shocks nor their lifetime, but we can estimate the correlations of the observations.
Thus in principle it should be possible to infer/estimate the lifetime distribution only from the counts.

==== More details ====

Georg M Goerg

2012-06-05T20:26:04Z

GeGoerg:

[[File:RapaNui2.jpg|thumb|left|alt=Me in Rapa Nui.|Me (left).]]

== My path to SFI ==
I am a PhD candidate (starting 4th year) in [http://www.stat.cmu.edu/ Statistics at Carnegie Mellon]. I received my masters in mathematics (applied / econometrics / time series)
from the Vienna University of Technology, Austria and before coming to
the US, I spent a year in Chile teaching statistics (mainly time series)
at PUC. For more details you can visit [http://www.stat.cmu.edu/~gmg/ my website]. You can email me at "my_3_initials_in_lowercase"@stat.cmu.edu.

I am very eager to participate in the CSSS; especially because of the inter-disciplinary research / collaborations on real world problems with people from many backgrounds - that's what statistics is all about (at least for me). So I am looking forward to meeting all of you and I am sure we'll have a great month ahead of us.

== Research Interests ==

In my thesis I work on local statistical complexity (LSC) - a measure of
''interestingness'' for spatio-temporal fields. We develop the
statistical methods and algorithms to i) forecast a spatio-temporal
system, and ii) discover patterns automatically solely from the data. We
do this using modern non-parametric statistical / machine learning
techniques with good properties for any kind of (complex)
spatio-temporal system.

One reason why I work on spatio-temporal systems is that I have always been drawn to time series (a la "My interest lies in the future because I am going to spend the rest of my life there. ” - Charles F. Kettering) and methods that try to solve real-world problems. These include time series clustering, forecasting, blind source separation techniques for forecastable time series, time-varying parameter models. Another side-project are skewed and heavy-tailed distributions, in particular how we can transform random variables to introduce skewness and heavy tails. And as a statistician what's even more relevant to me is how can I reverse this transformation so I can take data and remove skewness, remove power laws, remove heavy tails.

I do all my statistical computing in R -- for user-friendly code and R packages (two so far), and Python -- for huge data tasks.

In my spare time I like to play soccer, volleyball, salsa dancing, traveling, ...

== SFI Project: Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how to approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

If you are interested let me know ("my_3_initials_in_lowercase"@stat.cmu.edu).

=== Math / Statistics ===

==== Conceptual view ====

[http://www.jstor.org/stable/10.2307/2646712 Parke] proposes an error duration model (EDM) for how time series observed in a system happen to form:

''The basic mechanism for an error duration model is a sequence of shocks of stochastic magnitude and stochastic duration. The variable observed in a given period is the sum of those shocks that survive to that point.''

The point of this formulation is that the distribution of the (unobserved) survival times determines the correlation structure of the observed series. Thus vice-versa we should be able to infer the lifetime distribution of the shocks from the correlation structure. The point of this is that in practice we don't observe neither the individual shocks nor their lifetime, but we can estimate the correlations of the observations.
Thus in principle it should be possible to infer/estimate the lifetime distribution only from the counts.

==== More details ====

Georg M Goerg

2012-06-05T20:25:09Z

GeGoerg: /* Ideas on how to approach this */

[[File:RapaNui2.jpg|thumb|left|alt=Me in Rapa Nui.|Me (left).]]

== My path to SFI ==
I am a PhD candidate (starting 4th year) in [http://www.stat.cmu.edu/ Statistics at Carnegie Mellon]. I received my masters in mathematics (applied / econometrics / time series)
from the Vienna University of Technology, Austria and before coming to
the US, I spent a year in Chile teaching statistics (mainly time series)
at PUC. For more details you can visit [http://www.stat.cmu.edu/~gmg/ my website]. You can email me at "my_3_initials_in_lowercase"@stat.cmu.edu.

I am very eager to participate in the CSSS; especially because of the inter-disciplinary research / collaborations on real world problems with people from many backgrounds - that's what statistics is all about (at least for me). So I am looking forward to meeting all of you and I am sure we'll have a great month ahead of us.

== Research Interests ==

In my thesis I work on local statistical complexity (LSC) - a measure of
''interestingness'' for spatio-temporal fields. We develop the
statistical methods and algorithms to i) forecast a spatio-temporal
system, and ii) discover patterns automatically solely from the data. We
do this using modern non-parametric statistical / machine learning
techniques with good properties for any kind of (complex)
spatio-temporal system.

One reason why I work on spatio-temporal systems is that I have always been drawn to time series (a la "My interest lies in the future because I am going to spend the rest of my life there. ” - Charles F. Kettering) and methods that try to solve real-world problems. These include time series clustering, forecasting, blind source separation techniques for forecastable time series, time-varying parameter models. Another side-project are skewed and heavy-tailed distributions, in particular how we can transform random variables to introduce skewness and heavy tails. And as a statistician what's even more relevant to me is how can I reverse this transformation so I can take data and remove skewness, remove power laws, remove heavy tails.

I do all my statistical computing in R -- for user-friendly code and R packages (two so far), and Python -- for huge data tasks.

In my spare time I like to play soccer, volleyball, salsa dancing, traveling, ...

== SFI Project ==

=== Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how to approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

If you are interested let me know ("my_3_initials_in_lowercase"@stat.cmu.edu).

=== Math / Statistics ===

==== Conceptual view ====

[http://www.jstor.org/stable/10.2307/2646712 Parke] proposes an error duration model (EDM) for how time series observed in a system happen to form:

''The basic mechanism for an error duration model is a sequence of shocks of stochastic magnitude and stochastic duration. The variable observed in a given period is the sum of those shocks that survive to that point.''

The point of this formulation is that the distribution of the (unobserved) survival times determines the correlation structure of the observed series. Thus vice-versa we should be able to infer the lifetime distribution of the shocks from the correlation structure. The point of this is that in practice we don't observe neither the individual shocks nor their lifetime, but we can estimate the correlations of the observations.
Thus in principle it should be possible to infer/estimate the lifetime distribution only from the counts.

==== More details ====

Georg M Goerg

2012-06-05T20:24:51Z

GeGoerg: /* SFI Project */

[[File:RapaNui2.jpg|thumb|left|alt=Me in Rapa Nui.|Me (left).]]

== My path to SFI ==
I am a PhD candidate (starting 4th year) in [http://www.stat.cmu.edu/ Statistics at Carnegie Mellon]. I received my masters in mathematics (applied / econometrics / time series)
from the Vienna University of Technology, Austria and before coming to
the US, I spent a year in Chile teaching statistics (mainly time series)
at PUC. For more details you can visit [http://www.stat.cmu.edu/~gmg/ my website]. You can email me at "my_3_initials_in_lowercase"@stat.cmu.edu.

I am very eager to participate in the CSSS; especially because of the inter-disciplinary research / collaborations on real world problems with people from many backgrounds - that's what statistics is all about (at least for me). So I am looking forward to meeting all of you and I am sure we'll have a great month ahead of us.

== Research Interests ==

In my thesis I work on local statistical complexity (LSC) - a measure of
''interestingness'' for spatio-temporal fields. We develop the
statistical methods and algorithms to i) forecast a spatio-temporal
system, and ii) discover patterns automatically solely from the data. We
do this using modern non-parametric statistical / machine learning
techniques with good properties for any kind of (complex)
spatio-temporal system.

One reason why I work on spatio-temporal systems is that I have always been drawn to time series (a la "My interest lies in the future because I am going to spend the rest of my life there. ” - Charles F. Kettering) and methods that try to solve real-world problems. These include time series clustering, forecasting, blind source separation techniques for forecastable time series, time-varying parameter models. Another side-project are skewed and heavy-tailed distributions, in particular how we can transform random variables to introduce skewness and heavy tails. And as a statistician what's even more relevant to me is how can I reverse this transformation so I can take data and remove skewness, remove power laws, remove heavy tails.

I do all my statistical computing in R -- for user-friendly code and R packages (two so far), and Python -- for huge data tasks.

In my spare time I like to play soccer, volleyball, salsa dancing, traveling, ...

== SFI Project ==

=== Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how to approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

If you are interested let me know (email my_3_initials_in_lowercase@stat.cmu.edu).

=== Math / Statistics ===

==== Conceptual view ====

[http://www.jstor.org/stable/10.2307/2646712 Parke] proposes an error duration model (EDM) for how time series observed in a system happen to form:

''The basic mechanism for an error duration model is a sequence of shocks of stochastic magnitude and stochastic duration. The variable observed in a given period is the sum of those shocks that survive to that point.''

The point of this formulation is that the distribution of the (unobserved) survival times determines the correlation structure of the observed series. Thus vice-versa we should be able to infer the lifetime distribution of the shocks from the correlation structure. The point of this is that in practice we don't observe neither the individual shocks nor their lifetime, but we can estimate the correlations of the observations.
Thus in principle it should be possible to infer/estimate the lifetime distribution only from the counts.

==== More details ====

Georg M Goerg

2012-06-05T20:23:31Z

GeGoerg:

Georg M Goerg

2012-06-05T20:22:57Z

GeGoerg: /* Conceptual view */

Georg M Goerg

2012-06-05T20:22:44Z

GeGoerg: /* Conceptual view */

Georg M Goerg

2012-06-05T20:22:31Z

GeGoerg: /* Conceptual view */

Georg M Goerg

2012-06-05T20:21:32Z

GeGoerg: /* Conceptual view */

Georg M Goerg

2012-06-05T20:18:27Z

GeGoerg: /* SFI Project */

Georg M Goerg

2012-06-05T20:13:43Z

GeGoerg:

Complex Systems Summer School 2012-Projects & Working Groups

2012-06-05T20:10:27Z

GeGoerg: /* Ideas on how to approach this */

{{Complex Systems Summer School 2012}}

==Project proposals==

=== Nonequilibrium game theory ===
My hope is to adapt some SFI-based models, by people like Crutchfield and Farmer, so that they will quantitatively or qualitatively reproduce features of real human data. Of course, that is very specific, and I'm up for all kinds of ideas in the areas of game learning, game dynamics, small group collective behavior, cognitive science, nonlinear time series, non-eq time series, etc., etc.

Meet me, Seth Frey, at dinner on Thursday and Friday.

=== Enzyme kinetics – Do enzymes just accelerate equilibrium or play an active role in chemical reactions? ===
Enzyme networks (e.g. glycolysis) and catalysts in complex mixtures (e.g. Belusov-Zhabotinski reaction) can profoundly influence the outcome of a chemical reaction system. What about a single enzyme? Biochemistry textbooks uniformly say that an enzyme accelerates a reaction without altering its outcome. Yet, the set of differential equations that generically describes enzyme catalysis has remarkable resemblance to the Roessler equations (a textbook example of a non-linear, complex system). With a fixed substrate input or a steady substrate flow, a single enzyme probably cannot affect the reaction outcome. However, sinusoidal or pulsating substrate input, substrate activation or product inhibition, coupling of two enzymes could turn the reaction pattern non-linear. For this project, the sets of equations to study are quite well established – they need to be analyzed. In contrast to some of the more ambitious ideas circulated, this task is exhaustively doable in less than four weeks.

I am Georg Weber. If you are interested in studying this problem, please find me on Tuesday over lunch or dinner (or talk to me at any other unstructured time).
=== Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how to approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system, and their velocity is just inverse proportional to their lifetime in this highway section). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

If you are interested let me know (me = [http://tuvalu.santafe.edu/events/workshops/index.php/Georg_M_Goerg Georg M. Goerg]; email my_3_initials_in_lowercase@stat.cmu.edu). Let's say Wednesday for lunch (or any other time you see me around).

=== Cultural Evolution - General Meet-up ===
Attention anyone who is interested in cultural evolution or applying your models/methodologies to this fabulous topic!

Let's meet at 4:15 (June 5th) in the cafe during the first "Time to work on Projects" slot. A bunch of us coalesced there tonight and figured we should all properly meet up and then bud off into different projects. Please post your potential buds below:

=== Cultural Evolution - things that look like drift but aren't ===
Lots of cultural evolution looks like drift (Bently et al 2004 'Random drift and culture change"). But what social transmission or cognitive learning mechanisms are isomorphic to random sampling with replacement from cultural inputs? In biological evolution, drift serves as a null model of sorts - one that should be ruled out before you can claim that anything more interesting is happening. However, it's not clear what the "uninteresting" type of change is for things that replicate by passing through human cognition and human social systems - like culture does. Is there even a reasonable equivalent of drift in cultural transmission? How should we go about conceptualizing and modeling the evolutionary forces at play in culture?

One candidate for a drifty-lookin' human behavior is probability matching: when people reproduce similar distributions of variation to that which they've learned from. And probability matching is rampant in human behavior (from language learning, to decision making, and even at the neural level). But I think this is a clearly different process than drift, however it still may qualify under Bentley's vague criteria - we can test that out. And there have got to be more drift-esque processes, anyone have any ideas?

If you're interested in these issues or modeling evolution (of any type of system), please give me a shout!

Vanessa

vanferdi [at] gmail.com

==="Small Steps and Big Ideas" Group===

[http://tuvalu.santafe.edu/events/workshops/index.php/Christa_Brelsford Christa] [http://tuvalu.santafe.edu/events/workshops/index.php/Daniel_Wu Dan] [http://tuvalu.santafe.edu/events/workshops/index.php/Xin_Lu Xin] and Tom spent a while talking after dinner about a bunch of big ideas. Some things we thought about were *big data type network problems, *integrating qualitative social information with models of physical systems, *using games to understand cooperation and decision making.

We'll meet at dinner at 5:30 today (Tuesday, June 5th) in the cafeteria.

=== 107 Proteins in 10-15 cubic meters ===
Cells rely on proteins to perform vital metabolic and signaling functions; however, it is unclear how proteins are successfully directed to their necessary cellular location(s) in a densely-packed macromolecular environment within the cytoplasm and on the cellular membrane in a short timescale (see for example [http://www.pnas.org/content/108/16/6438.full Weigel et al., PNAS 2011]). Using the cell as a manipulatable model of complexity, one could begin to define the parameters and questions, as they pertain to prokaryotic and eukaryotic cells. If interested, please drop me a line: Sepehr Ehsani; sepehr.ehsani[at]utoronto.ca.

=== Innovation and Technological Progress ===

I noticed that a number of people mentioned that they were interested in some way in relation to innovation. I was wondering if anyone was interested in a project looking at how particular technologies progress over time and whether charting the form of successful (and/or unsuccessful) previous technologies such as the transistor, fission reactor, mobile phone, etc. in terms of either price, efficiency, or some other variable may be useful in predicting whether a current technology such as solar PV, fuel cell, or something else is following a similar trajectory. Other possible ideas might be to look at using patent, publication, or collaboration network data to reveal certain features of innovation that are not captured by other statistics, particularly for technologies that have yet to reach the market. SFI Professor Doyne Farmer has looked at some of this already in 'The Role of Design Complexity in Technology Improvement', see link: http://adsabs.harvard.edu/abs/2009arXiv0907.0036M

This could be a jumping off point for some ideas on data, methods, models etc. Just throwing the idea out there and it's welcome to completely change but if you're interested, message me (Gareth Haslam) haslam@ias.unu.edu or find me in class.

=== Space, Stochasticity, Stability; Speciation? ===

[http://tuvalu.santafe.edu/events/workshops/index.php/Xue_Feng Xue], [http://tuvalu.santafe.edu/events/workshops/index.php/Chloe_Lewis Chloe] and [http://tuvalu.santafe.edu/events/workshops/index.php/Xiaoli_Dong Xiaoli]are all working in ecosystems that experience_ a lot of unpredictability in a limiting ecosystem variables (water and/or nutrients); we see patchiness in space and time in how organisms are arranged; and we have some ideas about how the stochasticity may cause the spatial arrangements. [http://tuvalu.santafe.edu/events/workshops/index.php/Si_Tang Si] is working on the stability and robustness of ecosystems.

With enough time, this is likely to involve speciation either to express different strategies, or as a result of spatial separation.

Find any of us walking-around, or meet in the cafeteria at 4:15 June 5th.

=== Plasticity in Neural Networks ===
I've done some modeling which shows that the amount of genetic variation that accumulates at any particular metabolic gene (enzyme) in a population at any given time is a function of the network topology in which the gene is embedded, as well as the distance of the network output from an optimum. So, for instance, in a linear metabolic network, enzymes at the beginning of a pathway will tend to be more constrained (show less variation in the population) than at the end of the pathway. This makes sense given that any changes in those first genes would ripple through the system and have a greater relative effect than mutations in later genes. However, this is only true when a population is already close to an optimum. When far from an optimum, we see the exact opposite trend with more variation in the front of the pathway. This also makes sense - when far from a goal, taking bigger steps gives individuals a better chance of achieving higher fitness. The system as a whole then uses the different relative step sizes according to pathway position to "fine tune" its output.
I think these findings are quite general - at least the model we used was simple enough that it could apply to many different types of directional developmental processes. We can conceptualize these "genes" more generally as sequential steps in a developmental process with some arbitrary goal. These could be steps in a factory assembly line, major product revisions versus minor releases, or (and this is my favorite), neurons learning about their environment. I'm curious what would happen if we took a similar approach to model neural networks. Genetic variation is the raw material for evolution while neural plasticity is the raw material for learning. The question we would be trying to answer is where, within a neural network, would we expect the most plasticity given a particular network topology and distance form a learning goal.
Please contact me (Mark Longo) if this sounds interesting. I'll be available during unstructured time, or you can email mlongo@stanford.edu.
[http://tuvalu.santafe.edu/events/workshops/index.php/Mark_D._Longo]

=== Robustness of complex networks ===
[[File:Zoo.png|thumb|Fig. 1. Zoo of complex networks (an example). Taken from Sol´e and Valverde, 2001.]]
==== Problem statement ====
Complex networks have various properties which can be measured in real networks (WWW, social networks, biological networks), e.g. degree distribution, modularity, hierarchy, assortativity etc. Robustness of complex networks is a big question, however only some progress have been done in this direction. For example, it was shown that the scale-free networks are much more topologically robust to random attacks than random networks. Many people claim that various characteristics of complex networks will influence the robustness interdependently. The question I am interested in is how?

==== Approach ====
The idea is to generate continuous topology space of various complex networks (networks with different modularity, degree distribution, hierarchy etc) and use it to measure their robustness (see Fig. 1). There are many approaches to measure the robustness of complex networks. For example we can remove edges of vertices of a complex network graph and look at the size of a giant cluster. We can discuss other possibilities.

If you are interested you can contact me directly or via my E-mail: krystoferivanov@gmail.com or via my [[Oleksandr Ivanov|discussion page in CSSS 2012 wiki]].

Complex Systems Summer School 2012-Projects & Working Groups

2012-06-05T17:07:19Z

GeGoerg: /* Ideas on how to approach this */

{{Complex Systems Summer School 2012}}

==Project proposals==

=== Nonequilibrium game theory ===
My hope is to adapt some SFI-based models, by people like Crutchfield and Farmer, so that they will quantitatively or qualitatively reproduce features of real human data. Of course, that is very specific, and I'm up for all kinds of ideas in the areas of game learning, game dynamics, small group collective behavior, cognitive science, nonlinear time series, non-eq time series, etc., etc.

Meet me, Seth Frey, at dinner on Thursday and Friday.

=== Enzyme kinetics – Do enzymes just accelerate equilibrium or play an active role in chemical reactions? ===
Enzyme networks (e.g. glycolysis) and catalysts in complex mixtures (e.g. Belusov-Zhabotinski reaction) can profoundly influence the outcome of a chemical reaction system. What about a single enzyme? Biochemistry textbooks uniformly say that an enzyme accelerates a reaction without altering its outcome. Yet, the set of differential equations that generically describes enzyme catalysis has remarkable resemblance to the Roessler equations (a textbook example of a non-linear, complex system). With a fixed substrate input or a steady substrate flow, a single enzyme probably cannot affect the reaction outcome. However, sinusoidal or pulsating substrate input, substrate activation or product inhibition, coupling of two enzymes could turn the reaction pattern non-linear. For this project, the sets of equations to study are quite well established – they need to be analyzed. In contrast to some of the more ambitious ideas circulated, this task is exhaustively doable in less than four weeks.

I am Georg Weber. If you are interested in studying this problem, please find me on Tuesday over lunch or dinner (or talk to me at any other unstructured time).
=== Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how to approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system). I would like to see if using explicit physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update 06/05/12:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

If you are interested let me know (me = [http://tuvalu.santafe.edu/events/workshops/index.php/Georg_M_Goerg Georg M. Goerg]; email my_3_initials_in_lowercase@stat.cmu.edu). Let's say Wednesday for lunch (or any other time you see me around).

=== Cultural Evolution - General Meet-up ===
Attention anyone who is interested in cultural evolution or applying your models/methodologies to this fabulous topic!

Let's meet at 4:15 (June 5th) in the cafe during the first "Time to work on Projects" slot. A bunch of us coalesced there tonight and figured we should all properly meet up and then bud off into different projects. Please post your potential buds below:

=== Cultural Evolution - things that look like drift but aren't ===
Lots of cultural evolution looks like drift (Bently et al 2004 'Random drift and culture change"). But what social transmission or cognitive learning mechanisms are isomorphic to random sampling with replacement from cultural inputs? In biological evolution, drift serves as a null model of sorts - one that should be ruled out before you can claim that anything more interesting is happening. However, it's not clear what the "uninteresting" type of change is for things that replicate by passing through human cognition and human social systems - like culture does. Is there even a reasonable equivalent of drift in cultural transmission? How should we go about conceptualizing and modeling the evolutionary forces at play in culture?

One candidate for a drifty-lookin' human behavior is probability matching: when people reproduce similar distributions of variation to that which they've learned from. And probability matching is rampant in human behavior (from language learning, to decision making, and even at the neural level). But I think this is a clearly different process than drift, however it still may qualify under Bentley's vague criteria - we can test that out. And there have got to be more drift-esque processes, anyone have any ideas?

If you're interested in these issues or modeling evolution (of any type of system), please give me a shout!

Vanessa

vanferdi [at] gmail.com

==="Small Steps and Big Ideas" Group===

[http://tuvalu.santafe.edu/events/workshops/index.php/Christa_Brelsford Christa] [http://tuvalu.santafe.edu/events/workshops/index.php/Daniel_Wu Dan] [http://tuvalu.santafe.edu/events/workshops/index.php/Xin_Lu Xin] and Tom spent a while talking after dinner about a bunch of big ideas. Some things we thought about were *big data type network problems, *integrating qualitative social information with models of physical systems, *using games to understand cooperation and decision making.

=== 107 Proteins in 10-15 cubic meters ===
Cells rely on proteins to perform vital metabolic and signaling functions; however, it is unclear how proteins are successfully directed to their necessary cellular location(s) in a densely-packed macromolecular environment within the cytoplasm and on the cellular membrane in a short timescale (see for example [http://www.pnas.org/content/108/16/6438.full Weigel et al., PNAS 2011]). Using the cell as a manipulatable model of complexity, one could begin to define the parameters and questions, as they pertain to prokaryotic and eukaryotic cells. If interested, please drop me a line: Sepehr Ehsani; sepehr.ehsani[at]utoronto.ca.

=== Innovation and Technological Progress ===

I noticed that a number of people mentioned that they were interested in some way in relation to innovation. I was wondering if anyone was interested in a project looking at how particular technologies progress over time and whether charting the form of successful (and/or unsuccessful) previous technologies such as the transistor, fission reactor, mobile phone, etc. in terms of either price, efficiency, or some other variable may be useful in predicting whether a current technology such as solar PV, fuel cell, or something else is following a similar trajectory. Other possible ideas might be to look at using patent, publication, or collaboration network data to reveal certain features of innovation that are not captured by other statistics, particularly for technologies that have yet to reach the market. SFI Professor Doyne Farmer has looked at some of this already in 'The Role of Design Complexity in Technology Improvement', see link: http://adsabs.harvard.edu/abs/2009arXiv0907.0036M

This could be a jumping off point for some ideas on data, methods, models etc. Just throwing the idea out there and it's welcome to completely change but if you're interested, message me (Gareth Haslam) haslam@ias.unu.edu or find me in class.

=== Space, Stochasticity, Stability; Speciation? ===

[http://tuvalu.santafe.edu/events/workshops/index.php/Xue_Feng Xue], [http://tuvalu.santafe.edu/events/workshops/index.php/Chloe_Lewis Chloe] and [http://tuvalu.santafe.edu/events/workshops/index.php/Xiaoli_Dong Xiaoli]are all working in ecosystems that experience_ a lot of unpredictability in a limiting ecosystem variables (water and/or nutrients); we see patchiness in space and time in how organisms are arranged; and we have some ideas about how the stochasticity may cause the spatial arrangements. [http://tuvalu.santafe.edu/events/workshops/index.php/Si_Tang Si] is working on the stability and robustness of ecosystems.

With enough time, this is likely to involve speciation either to express different strategies, or as a result of spatial separation.

Find any of us walking-around, or meet in the cafeteria at 4:15 June 5th.

=== Plasticity in Neural Networks ===
I've done some modeling which shows that the amount of genetic variation that accumulates at any particular metabolic gene (enzyme) in a population at any given time is a function of the network topology in which the gene is embedded, as well as the distance of the network output from an optimum. So, for instance, in a linear metabolic network, enzymes at the beginning of a pathway will tend to be more constrained (show less variation in the population) than at the end of the pathway. This makes sense given that any changes in those first genes would ripple through the system and have a greater relative effect than mutations in later genes. However, this is only true when a population is already close to an optimum. When far from an optimum, we see the exact opposite trend with more variation in the front of the pathway. This also makes sense - when far from a goal, taking bigger steps gives individuals a better chance of achieving higher fitness. The system as a whole then uses the different relative step sizes according to pathway position to "fine tune" its output.
I think these findings are quite general - at least the model we used was simple enough that it could apply to many different types of directional developmental processes. We can conceptualize these "genes" more generally as sequential steps in a developmental process with some arbitrary goal. These could be steps in a factory assembly line, major product revisions versus minor releases, or (and this is my favorite), neurons learning about their environment. I'm curious what would happen if we took a similar approach to model neural networks. Genetic variation is the raw material for evolution while neural plasticity is the raw material for learning. The question we would be trying to answer is where, within a neural network, would we expect the most plasticity given a particular network topology and distance form a learning goal.
Please contact me (Mark Longo) if this sounds interesting. I'll be available during unstructured time, or you can email mlongo@stanford.edu.
[http://tuvalu.santafe.edu/events/workshops/index.php/Mark_D._Longo]

=== Robustness of complex networks ===
[[File:Zoo.png|thumb|Fig. 1. Zoo of complex networks (an example). Taken from Sol´e and Valverde, 2001.]]
==== Problem statement ====
Complex networks have various properties which can be measured in real networks (WWW, social networks, biological networks), e.g. degree distribution, modularity, hierarchy, assortativity etc. Robustness of complex networks is a big question, however only some progress have been done in this direction. For example, it was shown that the scale-free networks are much more topologically robust to random attacks than random networks. Many people claim that various characteristics of complex networks will influence the robustness interdependently. The question I am interested in is how?

==== Approach ====
The idea is to generate continuous topology space of various complex networks (networks with different modularity, degree distribution, hierarchy etc) and use it to measure their robustness (see Fig. 1). There are many approaches to measure the robustness of complex networks. For example we can remove edges of vertices of a complex network graph and look at the size of a giant cluster. We can discuss other possibilities.

If you are interested you can contact me directly or via my E-mail: krystoferivanov@gmail.com or via my [[Oleksandr Ivanov|discussion page in CSSS 2012 wiki]].

Complex Systems Summer School 2012-Projects & Working Groups

2012-06-05T16:46:35Z

GeGoerg: /* Approach */

{{Complex Systems Summer School 2012}}

==Project proposals==

=== Nonequilibrium game theory ===
My hope is to adapt some SFI-based models, by people like Crutchfield and Farmer, so that they will quantitatively or qualitatively reproduce features of real human data. Of course, that is very specific, and I'm up for all kinds of ideas in the areas of game learning, game dynamics, small group collective behavior, cognitive science, nonlinear time series, non-eq time series, etc., etc.

Meet me, Seth Frey, at dinner on Thursday and Friday.

=== Enzyme kinetics – Do enzymes just accelerate equilibrium or play an active role in chemical reactions? ===
Enzyme networks (e.g. glycolysis) and catalysts in complex mixtures (e.g. Belusov-Zhabotinski reaction) can profoundly influence the outcome of a chemical reaction system. What about a single enzyme? Biochemistry textbooks uniformly say that an enzyme accelerates a reaction without altering its outcome. Yet, the set of differential equations that generically describes enzyme catalysis has remarkable resemblance to the Roessler equations (a textbook example of a non-linear, complex system). With a fixed substrate input or a steady substrate flow, a single enzyme probably cannot affect the reaction outcome. However, sinusoidal or pulsating substrate input, substrate activation or product inhibition, coupling of two enzymes could turn the reaction pattern non-linear. For this project, the sets of equations to study are quite well established – they need to be analyzed. In contrast to some of the more ambitious ideas circulated, this task is exhaustively doable in less than four weeks.

I am Georg Weber. If you are interested in studying this problem, please find me on Tuesday over lunch or dinner (or talk to me at any other unstructured time).
=== Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Ideas on how to approach this ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system). I would like to see if using more physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

If you are interested let me know (me = [http://tuvalu.santafe.edu/events/workshops/index.php/Georg_M_Goerg Georg M. Goerg]; email my_3_initials_in_lowercase@stat.cmu.edu). Let's say Wednesday for lunch (or any other time you see me around).

=== Cultural Evolution - General Meet-up ===
Attention anyone who is interested in cultural evolution or applying your models/methodologies to this fabulous topic!

Let's meet at 4:15 (June 5th) in the cafe during the first "Time to work on Projects" slot. A bunch of us coalesced there tonight and figured we should all properly meet up and then bud off into different projects. Please post your potential buds below:

=== Cultural Evolution - things that look like drift but aren't ===
Lots of cultural evolution looks like drift (Bently et al 2004 'Random drift and culture change"). But what social transmission or cognitive learning mechanisms are isomorphic to random sampling with replacement from cultural inputs? In biological evolution, drift serves as a null model of sorts - one that should be ruled out before you can claim that anything more interesting is happening. However, it's not clear what the "uninteresting" type of change is for things that replicate by passing through human cognition and human social systems - like culture does. Is there even a reasonable equivalent of drift in cultural transmission? How should we go about conceptualizing and modeling the evolutionary forces at play in culture?

One candidate for a drifty-lookin' human behavior is probability matching: when people reproduce similar distributions of variation to that which they've learned from. And probability matching is rampant in human behavior (from language learning, to decision making, and even at the neural level). But I think this is a clearly different process than drift, however it still may qualify under Bentley's vague criteria - we can test that out. And there have got to be more drift-esque processes, anyone have any ideas?

If you're interested in these issues or modeling evolution (of any type of system), please give me a shout!

Vanessa

vanferdi [at] gmail.com

==="Small Steps and Big Ideas" Group===

[http://tuvalu.santafe.edu/events/workshops/index.php/Christa_Brelsford Christa] [http://tuvalu.santafe.edu/events/workshops/index.php/Daniel_Wu Dan] [http://tuvalu.santafe.edu/events/workshops/index.php/Xin_Lu Xin] and Tom spent a while talking after dinner about a bunch of big ideas. Some things we thought about were *big data type network problems, *integrating qualitative social information with models of physical systems, *using games to understand cooperation and decision making.

=== 107 Proteins in 10-15 cubic meters ===
Cells rely on proteins to perform vital metabolic and signaling functions; however, it is unclear how proteins are successfully directed to their necessary cellular location(s) in a densely-packed macromolecular environment within the cytoplasm and on the cellular membrane in a short timescale (see for example [http://www.pnas.org/content/108/16/6438.full Weigel et al., PNAS 2011]). Using the cell as a manipulatable model of complexity, one could begin to define the parameters and questions, as they pertain to prokaryotic and eukaryotic cells. If interested, please drop me a line: Sepehr Ehsani; sepehr.ehsani[at]utoronto.ca.

=== Innovation and Technological Progress ===

I noticed that a number of people mentioned that they were interested in some way in relation to innovation. I was wondering if anyone was interested in a project looking at how particular technologies progress over time and whether charting the form of successful (and/or unsuccessful) previous technologies such as the transistor, fission reactor, mobile phone, etc. in terms of either price, efficiency, or some other variable may be useful in predicting whether a current technology such as solar PV, fuel cell, or something else is following a similar trajectory. Other possible ideas might be to look at using patent, publication, or collaboration network data to reveal certain features of innovation that are not captured by other statistics, particularly for technologies that have yet to reach the market. SFI Professor Doyne Farmer has looked at some of this already in 'The Role of Design Complexity in Technology Improvement', see link: http://adsabs.harvard.edu/abs/2009arXiv0907.0036M

This could be a jumping off point for some ideas on data, methods, models etc. Just throwing the idea out there and it's welcome to completely change but if you're interested, message me (Gareth Haslam) haslam@ias.unu.edu or find me in class.

=== Space, Stochasticity, Stability; Speciation? ===

[http://tuvalu.santafe.edu/events/workshops/index.php/Xue_Feng Xue], [http://tuvalu.santafe.edu/events/workshops/index.php/Chloe_Lewis Chloe] and [http://tuvalu.santafe.edu/events/workshops/index.php/Xiaoli_Dong Xiaoli]are all working in ecosystems that experience_ a lot of unpredictability in a limiting ecosystem variables (water and/or nutrients); we see patchiness in space and time in how organisms are arranged; and we have some ideas about how the stochasticity may cause the spatial arrangements. [http://tuvalu.santafe.edu/events/workshops/index.php/Si_Tang Si] is working on the stability and robustness of ecosystems.

With enough time, this is likely to involve speciation either to express different strategies, or as a result of spatial separation.

Find any of us walking-around, or meet in the cafeteria at 4:15 June 5th.

=== Plasticity in Neural Networks ===
I've done some modeling which shows that the amount of genetic variation that accumulates at any particular metabolic gene (enzyme) in a population at any given time is a function of the network topology in which the gene is embedded, as well as the distance of the network output from an optimum. So, for instance, in a linear metabolic network, enzymes at the beginning of a pathway will tend to be more constrained (show less variation in the population) than at the end of the pathway. This makes sense given that any changes in those first genes would ripple through the system and have a greater relative effect than mutations in later genes. However, this is only true when a population is already close to an optimum. When far from an optimum, we see the exact opposite trend with more variation in the front of the pathway. This also makes sense - when far from a goal, taking bigger steps gives individuals a better chance of achieving higher fitness. The system as a whole then uses the different relative step sizes according to pathway position to "fine tune" its output.
I think these findings are quite general - at least the model we used was simple enough that it could apply to many different types of directional developmental processes. We can conceptualize these "genes" more generally as sequential steps in a developmental process with some arbitrary goal. These could be steps in a factory assembly line, major product revisions versus minor releases, or (and this is my favorite), neurons learning about their environment. I'm curious what would happen if we took a similar approach to model neural networks. Genetic variation is the raw material for evolution while neural plasticity is the raw material for learning. The question we would be trying to answer is where, within a neural network, would we expect the most plasticity given a particular network topology and distance form a learning goal.
Please contact me (Mark Longo) if this sounds interesting. I'll be available during unstructured time, or you can email mlongo@stanford.edu.
[http://tuvalu.santafe.edu/events/workshops/index.php/Mark_D._Longo]

=== Robustness of complex networks ===
[[File:Zoo.png|thumb|Fig. 1. Zoo of complex networks (an example). Taken from Sol´e and Valverde, 2001.]]
==== Problem statement ====
Complex networks have various properties which can be measured in real networks (WWW, social networks, biological networks), e.g. degree distribution, modularity, hierarchy, assortativity etc. Robustness of complex networks is a big question, however only some progress have been done in this direction. For example, it was shown that the scale-free networks are much more topologically robust to random attacks than random networks. Many people claim that various characteristics of complex networks will influence the robustness interdependently. The question I am interested in is how?

==== Approach ====
The idea is to generate continuous topology space of various complex networks (networks with different modularity, degree distribution, hierarchy etc) and use it to measure their robustness (see Fig. 1). There are many approaches to measure the robustness of complex networks. For example we can remove edges of vertices of a complex network graph and look at the size of a giant cluster. We can discuss other possibilities.

If you are interested you can contact me directly or via my E-mail: krystoferivanov@gmail.com or via my [[Oleksandr Ivanov|discussion page in CSSS 2012 wiki]].

Complex Systems Summer School 2012-Projects & Working Groups

2012-06-05T16:45:44Z

GeGoerg:

{{Complex Systems Summer School 2012}}

==Project proposals==

=== Nonequilibrium game theory ===
My hope is to adapt some SFI-based models, by people like Crutchfield and Farmer, so that they will quantitatively or qualitatively reproduce features of real human data. Of course, that is very specific, and I'm up for all kinds of ideas in the areas of game learning, game dynamics, small group collective behavior, cognitive science, nonlinear time series, non-eq time series, etc., etc.

Meet me, Seth Frey, at dinner on Thursday and Friday.

=== Enzyme kinetics – Do enzymes just accelerate equilibrium or play an active role in chemical reactions? ===
Enzyme networks (e.g. glycolysis) and catalysts in complex mixtures (e.g. Belusov-Zhabotinski reaction) can profoundly influence the outcome of a chemical reaction system. What about a single enzyme? Biochemistry textbooks uniformly say that an enzyme accelerates a reaction without altering its outcome. Yet, the set of differential equations that generically describes enzyme catalysis has remarkable resemblance to the Roessler equations (a textbook example of a non-linear, complex system). With a fixed substrate input or a steady substrate flow, a single enzyme probably cannot affect the reaction outcome. However, sinusoidal or pulsating substrate input, substrate activation or product inhibition, coupling of two enzymes could turn the reaction pattern non-linear. For this project, the sets of equations to study are quite well established – they need to be analyzed. In contrast to some of the more ambitious ideas circulated, this task is exhaustively doable in less than four weeks.

I am Georg Weber. If you are interested in studying this problem, please find me on Tuesday over lunch or dinner (or talk to me at any other unstructured time).
=== Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
==== Problem statement ====
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.
==== Approach ====
I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system). I would like to see if using more physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

If you are interested let me know (me = [http://tuvalu.santafe.edu/events/workshops/index.php/Georg_M_Goerg Georg M. Goerg]; email my_3_initials_in_lowercase@stat.cmu.edu). Let's say Wednesday for lunch (or any other time you see me around).

=== Cultural Evolution - General Meet-up ===
Attention anyone who is interested in cultural evolution or applying your models/methodologies to this fabulous topic!

Let's meet at 4:15 (June 5th) in the cafe during the first "Time to work on Projects" slot. A bunch of us coalesced there tonight and figured we should all properly meet up and then bud off into different projects. Please post your potential buds below:

=== Cultural Evolution - things that look like drift but aren't ===
Lots of cultural evolution looks like drift (Bently et al 2004 'Random drift and culture change"). But what social transmission or cognitive learning mechanisms are isomorphic to random sampling with replacement from cultural inputs? In biological evolution, drift serves as a null model of sorts - one that should be ruled out before you can claim that anything more interesting is happening. However, it's not clear what the "uninteresting" type of change is for things that replicate by passing through human cognition and human social systems - like culture does. Is there even a reasonable equivalent of drift in cultural transmission? How should we go about conceptualizing and modeling the evolutionary forces at play in culture?

One candidate for a drifty-lookin' human behavior is probability matching: when people reproduce similar distributions of variation to that which they've learned from. And probability matching is rampant in human behavior (from language learning, to decision making, and even at the neural level). But I think this is a clearly different process than drift, however it still may qualify under Bentley's vague criteria - we can test that out. And there have got to be more drift-esque processes, anyone have any ideas?

If you're interested in these issues or modeling evolution (of any type of system), please give me a shout!

Vanessa

vanferdi [at] gmail.com

==="Small Steps and Big Ideas" Group===

[http://tuvalu.santafe.edu/events/workshops/index.php/Christa_Brelsford Christa] [http://tuvalu.santafe.edu/events/workshops/index.php/Daniel_Wu Dan] [http://tuvalu.santafe.edu/events/workshops/index.php/Xin_Lu Xin] and Tom spent a while talking after dinner about a bunch of big ideas. Some things we thought about were *big data type network problems, *integrating qualitative social information with models of physical systems, *using games to understand cooperation and decision making.

=== 107 Proteins in 10-15 cubic meters ===
Cells rely on proteins to perform vital metabolic and signaling functions; however, it is unclear how proteins are successfully directed to their necessary cellular location(s) in a densely-packed macromolecular environment within the cytoplasm and on the cellular membrane in a short timescale (see for example [http://www.pnas.org/content/108/16/6438.full Weigel et al., PNAS 2011]). Using the cell as a manipulatable model of complexity, one could begin to define the parameters and questions, as they pertain to prokaryotic and eukaryotic cells. If interested, please drop me a line: Sepehr Ehsani; sepehr.ehsani[at]utoronto.ca.

=== Innovation and Technological Progress ===

I noticed that a number of people mentioned that they were interested in some way in relation to innovation. I was wondering if anyone was interested in a project looking at how particular technologies progress over time and whether charting the form of successful (and/or unsuccessful) previous technologies such as the transistor, fission reactor, mobile phone, etc. in terms of either price, efficiency, or some other variable may be useful in predicting whether a current technology such as solar PV, fuel cell, or something else is following a similar trajectory. Other possible ideas might be to look at using patent, publication, or collaboration network data to reveal certain features of innovation that are not captured by other statistics, particularly for technologies that have yet to reach the market. SFI Professor Doyne Farmer has looked at some of this already in 'The Role of Design Complexity in Technology Improvement', see link: http://adsabs.harvard.edu/abs/2009arXiv0907.0036M

This could be a jumping off point for some ideas on data, methods, models etc. Just throwing the idea out there and it's welcome to completely change but if you're interested, message me (Gareth Haslam) haslam@ias.unu.edu or find me in class.

=== Space, Stochasticity, Stability; Speciation? ===

[http://tuvalu.santafe.edu/events/workshops/index.php/Xue_Feng Xue], [http://tuvalu.santafe.edu/events/workshops/index.php/Chloe_Lewis Chloe] and [http://tuvalu.santafe.edu/events/workshops/index.php/Xiaoli_Dong Xiaoli]are all working in ecosystems that experience_ a lot of unpredictability in a limiting ecosystem variables (water and/or nutrients); we see patchiness in space and time in how organisms are arranged; and we have some ideas about how the stochasticity may cause the spatial arrangements. [http://tuvalu.santafe.edu/events/workshops/index.php/Si_Tang Si] is working on the stability and robustness of ecosystems.

With enough time, this is likely to involve speciation either to express different strategies, or as a result of spatial separation.

Find any of us walking-around, or meet in the cafeteria at 4:15 June 5th.

=== Plasticity in Neural Networks ===
I've done some modeling which shows that the amount of genetic variation that accumulates at any particular metabolic gene (enzyme) in a population at any given time is a function of the network topology in which the gene is embedded, as well as the distance of the network output from an optimum. So, for instance, in a linear metabolic network, enzymes at the beginning of a pathway will tend to be more constrained (show less variation in the population) than at the end of the pathway. This makes sense given that any changes in those first genes would ripple through the system and have a greater relative effect than mutations in later genes. However, this is only true when a population is already close to an optimum. When far from an optimum, we see the exact opposite trend with more variation in the front of the pathway. This also makes sense - when far from a goal, taking bigger steps gives individuals a better chance of achieving higher fitness. The system as a whole then uses the different relative step sizes according to pathway position to "fine tune" its output.
I think these findings are quite general - at least the model we used was simple enough that it could apply to many different types of directional developmental processes. We can conceptualize these "genes" more generally as sequential steps in a developmental process with some arbitrary goal. These could be steps in a factory assembly line, major product revisions versus minor releases, or (and this is my favorite), neurons learning about their environment. I'm curious what would happen if we took a similar approach to model neural networks. Genetic variation is the raw material for evolution while neural plasticity is the raw material for learning. The question we would be trying to answer is where, within a neural network, would we expect the most plasticity given a particular network topology and distance form a learning goal.
Please contact me (Mark Longo) if this sounds interesting. I'll be available during unstructured time, or you can email mlongo@stanford.edu.
[http://tuvalu.santafe.edu/events/workshops/index.php/Mark_D._Longo]

=== Robustness of complex networks ===
[[File:Zoo.png|thumb|Fig. 1. Zoo of complex networks (an example). Taken from Sol´e and Valverde, 2001.]]
==== Problem statement ====
Complex networks have various properties which can be measured in real networks (WWW, social networks, biological networks), e.g. degree distribution, modularity, hierarchy, assortativity etc. Robustness of complex networks is a big question, however only some progress have been done in this direction. For example, it was shown that the scale-free networks are much more topologically robust to random attacks than random networks. Many people claim that various characteristics of complex networks will influence the robustness interdependently. The question I am interested in is how?

==== Approach ====
The idea is to generate continuous topology space of various complex networks (networks with different modularity, degree distribution, hierarchy etc) and use it to measure their robustness (see Fig. 1). There are many approaches to measure the robustness of complex networks. For example we can remove edges of vertices of a complex network graph and look at the size of a giant cluster. We can discuss other possibilities.

If you are interested you can contact me directly or via my E-mail: krystoferivanov@gmail.com or via my [[Oleksandr Ivanov|discussion page in CSSS 2012 wiki]].

Complex Systems Summer School 2012-Projects & Working Groups

2012-06-05T16:42:35Z

GeGoerg: /* Traffic pattern analysis - Can we estimate car velocity by only observing car counts? */

{{Complex Systems Summer School 2012}}

==Project proposals==

=== Nonequilibrium game theory ===
My hope is to adapt some SFI-based models, by people like Crutchfield and Farmer, so that they will quantitatively or qualitatively reproduce features of real human data. Of course, that is very specific, and I'm up for all kinds of ideas in the areas of game learning, game dynamics, small group collective behavior, cognitive science, nonlinear time series, non-eq time series, etc., etc.

Meet me, Seth Frey, at dinner on Thursday and Friday.

=== Enzyme kinetics – Do enzymes just accelerate equilibrium or play an active role in chemical reactions? ===
Enzyme networks (e.g. glycolysis) and catalysts in complex mixtures (e.g. Belusov-Zhabotinski reaction) can profoundly influence the outcome of a chemical reaction system. What about a single enzyme? Biochemistry textbooks uniformly say that an enzyme accelerates a reaction without altering its outcome. Yet, the set of differential equations that generically describes enzyme catalysis has remarkable resemblance to the Roessler equations (a textbook example of a non-linear, complex system). With a fixed substrate input or a steady substrate flow, a single enzyme probably cannot affect the reaction outcome. However, sinusoidal or pulsating substrate input, substrate activation or product inhibition, coupling of two enzymes could turn the reaction pattern non-linear. For this project, the sets of equations to study are quite well established – they need to be analyzed. In contrast to some of the more ambitious ideas circulated, this task is exhaustively doable in less than four weeks.

I am Georg Weber. If you are interested in studying this problem, please find me on Tuesday over lunch or dinner (or talk to me at any other unstructured time).
=== Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.

I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system). I would like to see if using more physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.

'''Update:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

If you are interested let me know (me = [http://tuvalu.santafe.edu/events/workshops/index.php/Georg_M_Goerg Georg M. Goerg]; email my_3_initials_in_lowercase@stat.cmu.edu). Let's say Wednesday for lunch (or any other time you see me around).

=== Cultural Evolution - General Meet-up ===
Attention anyone who is interested in cultural evolution or applying your models/methodologies to this fabulous topic!

Let's meet at 4:15 (June 5th) in the cafe during the first "Time to work on Projects" slot. A bunch of us coalesced there tonight and figured we should all properly meet up and then bud off into different projects. Please post your potential buds below:

=== Cultural Evolution - things that look like drift but aren't ===
Lots of cultural evolution looks like drift (Bently et al 2004 'Random drift and culture change"). But what social transmission or cognitive learning mechanisms are isomorphic to random sampling with replacement from cultural inputs? In biological evolution, drift serves as a null model of sorts - one that should be ruled out before you can claim that anything more interesting is happening. However, it's not clear what the "uninteresting" type of change is for things that replicate by passing through human cognition and human social systems - like culture does. Is there even a reasonable equivalent of drift in cultural transmission? How should we go about conceptualizing and modeling the evolutionary forces at play in culture?

One candidate for a drifty-lookin' human behavior is probability matching: when people reproduce similar distributions of variation to that which they've learned from. And probability matching is rampant in human behavior (from language learning, to decision making, and even at the neural level). But I think this is a clearly different process than drift, however it still may qualify under Bentley's vague criteria - we can test that out. And there have got to be more drift-esque processes, anyone have any ideas?

If you're interested in these issues or modeling evolution (of any type of system), please give me a shout!

Vanessa

vanferdi [at] gmail.com

==="Small Steps and Big Ideas" Group===

[http://tuvalu.santafe.edu/events/workshops/index.php/Christa_Brelsford Christa] [http://tuvalu.santafe.edu/events/workshops/index.php/Daniel_Wu Dan] [http://tuvalu.santafe.edu/events/workshops/index.php/Xin_Lu Xin] and Tom spent a while talking after dinner about a bunch of big ideas. Some things we thought about were *big data type network problems, *integrating qualitative social information with models of physical systems, *using games to understand cooperation and decision making.

=== 107 Proteins in 10-15 cubic meters ===
Cells rely on proteins to perform vital metabolic and signaling functions; however, it is unclear how proteins are successfully directed to their necessary cellular location(s) in a densely-packed macromolecular environment within the cytoplasm and on the cellular membrane in a short timescale (see for example [http://www.pnas.org/content/108/16/6438.full Weigel et al., PNAS 2011]). Using the cell as a manipulatable model of complexity, one could begin to define the parameters and questions, as they pertain to prokaryotic and eukaryotic cells. If interested, please drop me a line: Sepehr Ehsani; sepehr.ehsani[at]utoronto.ca.

=== Innovation and Technological Progress ===

I noticed that a number of people mentioned that they were interested in some way in relation to innovation. I was wondering if anyone was interested in a project looking at how particular technologies progress over time and whether charting the form of successful (and/or unsuccessful) previous technologies such as the transistor, fission reactor, mobile phone, etc. in terms of either price, efficiency, or some other variable may be useful in predicting whether a current technology such as solar PV, fuel cell, or something else is following a similar trajectory. Other possible ideas might be to look at using patent, publication, or collaboration network data to reveal certain features of innovation that are not captured by other statistics, particularly for technologies that have yet to reach the market. SFI Professor Doyne Farmer has looked at some of this already in 'The Role of Design Complexity in Technology Improvement', see link: http://adsabs.harvard.edu/abs/2009arXiv0907.0036M

This could be a jumping off point for some ideas on data, methods, models etc. Just throwing the idea out there and it's welcome to completely change but if you're interested, message me (Gareth Haslam) haslam@ias.unu.edu or find me in class.

=== Space, Stochasticity, Stability; Speciation? ===

[http://tuvalu.santafe.edu/events/workshops/index.php/Xue_Feng Xue], [http://tuvalu.santafe.edu/events/workshops/index.php/Chloe_Lewis Chloe] and [http://tuvalu.santafe.edu/events/workshops/index.php/Xiaoli_Dong Xiaoli]are all working in ecosystems that experience_ a lot of unpredictability in a limiting ecosystem variables (water and/or nutrients); we see patchiness in space and time in how organisms are arranged; and we have some ideas about how the stochasticity may cause the spatial arrangements. [http://tuvalu.santafe.edu/events/workshops/index.php/Si_Tang Si] is working on the stability and robustness of ecosystems.

With enough time, this is likely to involve speciation either to express different strategies, or as a result of spatial separation.

Find any of us walking-around, or meet in the cafeteria at 4:15 June 5th.

=== Plasticity in Neural Networks ===
I've done some modeling which shows that the amount of genetic variation that accumulates at any particular metabolic gene (enzyme) in a population at any given time is a function of the network topology in which the gene is embedded, as well as the distance of the network output from an optimum. So, for instance, in a linear metabolic network, enzymes at the beginning of a pathway will tend to be more constrained (show less variation in the population) than at the end of the pathway. This makes sense given that any changes in those first genes would ripple through the system and have a greater relative effect than mutations in later genes. However, this is only true when a population is already close to an optimum. When far from an optimum, we see the exact opposite trend with more variation in the front of the pathway. This also makes sense - when far from a goal, taking bigger steps gives individuals a better chance of achieving higher fitness. The system as a whole then uses the different relative step sizes according to pathway position to "fine tune" its output.
I think these findings are quite general - at least the model we used was simple enough that it could apply to many different types of directional developmental processes. We can conceptualize these "genes" more generally as sequential steps in a developmental process with some arbitrary goal. These could be steps in a factory assembly line, major product revisions versus minor releases, or (and this is my favorite), neurons learning about their environment. I'm curious what would happen if we took a similar approach to model neural networks. Genetic variation is the raw material for evolution while neural plasticity is the raw material for learning. The question we would be trying to answer is where, within a neural network, would we expect the most plasticity given a particular network topology and distance form a learning goal.
Please contact me (Mark Longo) if this sounds interesting. I'll be available during unstructured time, or you can email mlongo@stanford.edu.
[http://tuvalu.santafe.edu/events/workshops/index.php/Mark_D._Longo]

=== Robustness of complex networks ===
[[File:Zoo.png|thumb|Fig. 1. Zoo of complex networks (an example). Taken from Sol´e and Valverde, 2001.]]
==== Problem statement ====
Complex networks have various properties which can be measured in real networks (WWW, social networks, biological networks), e.g. degree distribution, modularity, hierarchy, assortativity etc. Robustness of complex networks is a big question, however only some progress have been done in this direction. For example, it was shown that the scale-free networks are much more topologically robust to random attacks than random networks. Many people claim that various characteristics of complex networks will influence the robustness interdependently. The question I am interested in is how?

==== Approach ====
The idea is to generate continuous topology space of various complex networks (networks with different modularity, degree distribution, hierarchy etc) and use it to measure their robustness (see Fig. 1). There are many approaches to measure the robustness of complex networks. For example we can remove edges of vertices of a complex network graph and look at the size of a giant cluster. We can discuss other possibilities.

If you are interested you can contact me directly or via my E-mail: krystoferivanov@gmail.com or via my [[Oleksandr Ivanov|discussion page in CSSS 2012 wiki]].

Complex Systems Summer School 2012-Projects & Working Groups

2012-06-05T16:42:24Z

GeGoerg: /* Traffic pattern analysis - Can we estimate car velocity by only observing car counts? */

{{Complex Systems Summer School 2012}}

==Project proposals==

=== Nonequilibrium game theory ===
My hope is to adapt some SFI-based models, by people like Crutchfield and Farmer, so that they will quantitatively or qualitatively reproduce features of real human data. Of course, that is very specific, and I'm up for all kinds of ideas in the areas of game learning, game dynamics, small group collective behavior, cognitive science, nonlinear time series, non-eq time series, etc., etc.

Meet me, Seth Frey, at dinner on Thursday and Friday.

=== Enzyme kinetics – Do enzymes just accelerate equilibrium or play an active role in chemical reactions? ===
Enzyme networks (e.g. glycolysis) and catalysts in complex mixtures (e.g. Belusov-Zhabotinski reaction) can profoundly influence the outcome of a chemical reaction system. What about a single enzyme? Biochemistry textbooks uniformly say that an enzyme accelerates a reaction without altering its outcome. Yet, the set of differential equations that generically describes enzyme catalysis has remarkable resemblance to the Roessler equations (a textbook example of a non-linear, complex system). With a fixed substrate input or a steady substrate flow, a single enzyme probably cannot affect the reaction outcome. However, sinusoidal or pulsating substrate input, substrate activation or product inhibition, coupling of two enzymes could turn the reaction pattern non-linear. For this project, the sets of equations to study are quite well established – they need to be analyzed. In contrast to some of the more ambitious ideas circulated, this task is exhaustively doable in less than four weeks.

I am Georg Weber. If you are interested in studying this problem, please find me on Tuesday over lunch or dinner (or talk to me at any other unstructured time).
=== Traffic pattern analysis - Can we estimate car velocity by only observing car counts? ===
Imagine you have a monitored highway section with a start and end point. At both points you count the number of cars that pass by. The question I'd like to answer / simulate / estimate is: can we make some inference about the velocity of cars although we only have their counts? This would be very useful from an engineering / economic perspective because it's much easier / cheaper to count cars instead of actually tracking them from A to B.

I have some intuition about how to go about this, but these are purely statistical (think of it as birth and death process; or as particles in a system that have a certain lifetime - cars in the highway section are like particles in a system). I would like to see if using more physical modeling of motion and agent-based modeling of traffic flow could shed more light on this problem.
'''Update:''' Just today we saw ''Takens theorem'' about how we can infer a systems structure from only observing a subset of variables. Well, it seems like that's exactly what this project is about.

If you are interested let me know (me = [http://tuvalu.santafe.edu/events/workshops/index.php/Georg_M_Goerg Georg M. Goerg]; email my_3_initials_in_lowercase@stat.cmu.edu). Let's say Wednesday for lunch (or any other time you see me around).

=== Cultural Evolution - General Meet-up ===
Attention anyone who is interested in cultural evolution or applying your models/methodologies to this fabulous topic!

Let's meet at 4:15 (June 5th) in the cafe during the first "Time to work on Projects" slot. A bunch of us coalesced there tonight and figured we should all properly meet up and then bud off into different projects. Please post your potential buds below:

=== Cultural Evolution - things that look like drift but aren't ===
Lots of cultural evolution looks like drift (Bently et al 2004 'Random drift and culture change"). But what social transmission or cognitive learning mechanisms are isomorphic to random sampling with replacement from cultural inputs? In biological evolution, drift serves as a null model of sorts - one that should be ruled out before you can claim that anything more interesting is happening. However, it's not clear what the "uninteresting" type of change is for things that replicate by passing through human cognition and human social systems - like culture does. Is there even a reasonable equivalent of drift in cultural transmission? How should we go about conceptualizing and modeling the evolutionary forces at play in culture?

One candidate for a drifty-lookin' human behavior is probability matching: when people reproduce similar distributions of variation to that which they've learned from. And probability matching is rampant in human behavior (from language learning, to decision making, and even at the neural level). But I think this is a clearly different process than drift, however it still may qualify under Bentley's vague criteria - we can test that out. And there have got to be more drift-esque processes, anyone have any ideas?

If you're interested in these issues or modeling evolution (of any type of system), please give me a shout!

Vanessa

vanferdi [at] gmail.com

==="Small Steps and Big Ideas" Group===

[http://tuvalu.santafe.edu/events/workshops/index.php/Christa_Brelsford Christa] [http://tuvalu.santafe.edu/events/workshops/index.php/Daniel_Wu Dan] [http://tuvalu.santafe.edu/events/workshops/index.php/Xin_Lu Xin] and Tom spent a while talking after dinner about a bunch of big ideas. Some things we thought about were *big data type network problems, *integrating qualitative social information with models of physical systems, *using games to understand cooperation and decision making.

=== 107 Proteins in 10-15 cubic meters ===
Cells rely on proteins to perform vital metabolic and signaling functions; however, it is unclear how proteins are successfully directed to their necessary cellular location(s) in a densely-packed macromolecular environment within the cytoplasm and on the cellular membrane in a short timescale (see for example [http://www.pnas.org/content/108/16/6438.full Weigel et al., PNAS 2011]). Using the cell as a manipulatable model of complexity, one could begin to define the parameters and questions, as they pertain to prokaryotic and eukaryotic cells. If interested, please drop me a line: Sepehr Ehsani; sepehr.ehsani[at]utoronto.ca.

=== Innovation and Technological Progress ===

I noticed that a number of people mentioned that they were interested in some way in relation to innovation. I was wondering if anyone was interested in a project looking at how particular technologies progress over time and whether charting the form of successful (and/or unsuccessful) previous technologies such as the transistor, fission reactor, mobile phone, etc. in terms of either price, efficiency, or some other variable may be useful in predicting whether a current technology such as solar PV, fuel cell, or something else is following a similar trajectory. Other possible ideas might be to look at using patent, publication, or collaboration network data to reveal certain features of innovation that are not captured by other statistics, particularly for technologies that have yet to reach the market. SFI Professor Doyne Farmer has looked at some of this already in 'The Role of Design Complexity in Technology Improvement', see link: http://adsabs.harvard.edu/abs/2009arXiv0907.0036M

This could be a jumping off point for some ideas on data, methods, models etc. Just throwing the idea out there and it's welcome to completely change but if you're interested, message me (Gareth Haslam) haslam@ias.unu.edu or find me in class.

=== Space, Stochasticity, Stability; Speciation? ===

[http://tuvalu.santafe.edu/events/workshops/index.php/Xue_Feng Xue], [http://tuvalu.santafe.edu/events/workshops/index.php/Chloe_Lewis Chloe] and [http://tuvalu.santafe.edu/events/workshops/index.php/Xiaoli_Dong Xiaoli]are all working in ecosystems that experience_ a lot of unpredictability in a limiting ecosystem variables (water and/or nutrients); we see patchiness in space and time in how organisms are arranged; and we have some ideas about how the stochasticity may cause the spatial arrangements. [http://tuvalu.santafe.edu/events/workshops/index.php/Si_Tang Si] is working on the stability and robustness of ecosystems.

With enough time, this is likely to involve speciation either to express different strategies, or as a result of spatial separation.

Find any of us walking-around, or meet in the cafeteria at 4:15 June 5th.

=== Plasticity in Neural Networks ===
I've done some modeling which shows that the amount of genetic variation that accumulates at any particular metabolic gene (enzyme) in a population at any given time is a function of the network topology in which the gene is embedded, as well as the distance of the network output from an optimum. So, for instance, in a linear metabolic network, enzymes at the beginning of a pathway will tend to be more constrained (show less variation in the population) than at the end of the pathway. This makes sense given that any changes in those first genes would ripple through the system and have a greater relative effect than mutations in later genes. However, this is only true when a population is already close to an optimum. When far from an optimum, we see the exact opposite trend with more variation in the front of the pathway. This also makes sense - when far from a goal, taking bigger steps gives individuals a better chance of achieving higher fitness. The system as a whole then uses the different relative step sizes according to pathway position to "fine tune" its output.
I think these findings are quite general - at least the model we used was simple enough that it could apply to many different types of directional developmental processes. We can conceptualize these "genes" more generally as sequential steps in a developmental process with some arbitrary goal. These could be steps in a factory assembly line, major product revisions versus minor releases, or (and this is my favorite), neurons learning about their environment. I'm curious what would happen if we took a similar approach to model neural networks. Genetic variation is the raw material for evolution while neural plasticity is the raw material for learning. The question we would be trying to answer is where, within a neural network, would we expect the most plasticity given a particular network topology and distance form a learning goal.
Please contact me (Mark Longo) if this sounds interesting. I'll be available during unstructured time, or you can email mlongo@stanford.edu.
[http://tuvalu.santafe.edu/events/workshops/index.php/Mark_D._Longo]

=== Robustness of complex networks ===
[[File:Zoo.png|thumb|Fig. 1. Zoo of complex networks (an example). Taken from Sol´e and Valverde, 2001.]]
==== Problem statement ====
Complex networks have various properties which can be measured in real networks (WWW, social networks, biological networks), e.g. degree distribution, modularity, hierarchy, assortativity etc. Robustness of complex networks is a big question, however only some progress have been done in this direction. For example, it was shown that the scale-free networks are much more topologically robust to random attacks than random networks. Many people claim that various characteristics of complex networks will influence the robustness interdependently. The question I am interested in is how?

==== Approach ====
The idea is to generate continuous topology space of various complex networks (networks with different modularity, degree distribution, hierarchy etc) and use it to measure their robustness (see Fig. 1). There are many approaches to measure the robustness of complex networks. For example we can remove edges of vertices of a complex network graph and look at the size of a giant cluster. We can discuss other possibilities.

If you are interested you can contact me directly or via my E-mail: krystoferivanov@gmail.com or via my [[Oleksandr Ivanov|discussion page in CSSS 2012 wiki]].

Georg M Goerg

2012-06-05T04:52:14Z

GeGoerg:

Complex Systems Summer School 2012-Projects & Working Groups

2012-06-05T04:51:18Z

GeGoerg: /* Traffic pattern analysis - Can we estimate car velocity by only observing car counts? */

Complex Systems Summer School 2012-Projects & Working Groups

2012-06-05T04:43:22Z

GeGoerg: /* Traffic pattern analysis - Can we estimate car velocity by only observing car counts? */