Santa Fe Institute Events Wiki - User contributions [en]

Presentations 2012

2012-06-28T19:52:49Z

DPugh:

{{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: Political prediction markets: Can we use them to predict election outcomes?

10:15 - 10:45: BREAK

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

11:00 - 11:15: Sepehr

11:15 - 11:30: Xue, Χλοε, Xiaoli

11:30 - 11:45: Seth, Daniel, Cameron: flocking in iterated reasoning.

12:00 - 1:00: LUNCH

1:00-1:15: Group Photo

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: Use Entropy as a Measure of Traceability for Food Supply Networks

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

2:30 - 2:45: Ian, Marco, Oleksandr and Xin: Space of complex networks and robustness

2:45 - 3:00: BREAK

3:00 - 3:15: Marco and Matteo: Trade network formation: the role of technology and geography

3:15 - 3:30: Riccardo and Priya:

3:30 - 3:45: Nick A., Keegan, Matteo, Vikram, Sarah, Mark: Learning in Random Boolean Networks

3:45-4:00: Si, Miguel, Hide, Sarah: The Robustness, Stability and Persistence of Niche-Structured Food Webs

4:00 - 4:15 David, Sascha, Jianfeng: Modeling the Emergence of Money

5:00: Final Remarks & Farewell Dinner

Complex Systems Summer School 2012-Project Presentations

2012-06-28T15:55:19Z

DPugh: /* Emergence of Money through an agent based model */

{{Complex Systems Summer School 2012}}

Use this space to post project presentations and outlines. Include group members, a brief outline, and your slides.

[[Presentations 2012|SCHEDULE OF PRESENTATIONS SIGN-UP]]
=== The Robustness, Stability and Persistence of Niche-Structured Food Webs ===

Si, Miguel, Hide, Sarah

We are interested in the properties of food webs, a network structure describing who eats whom within an ecological community. In 2000, William and Martinez proposed the niche model, which generates synthetic food web structures by ordering species according to ‘niche values’ and assigning trophic relationships by allowing consumers to feed on the species whose niche values fall within a particular range. The niche model has successfully replicated many properties of empirical food webs.

However, whether the niche organization in food webs tends to produce more diverse, stable, robust and persistent communities remains unknown. In this study, we randomly generated food webs in which predator-prey links are formed according to the niche model and contrasted these with unstructured food webs in which predator-prey links are randomly assigned among species. We used the bioenergetic model to produce persisting food webs of both structural types. In the presentation on Friday, we will show a few preliminary results, such as the difference of the two types of food webs in complexity, robustness and stability.

Human impacts upon ecosystems – for example, the introduction of invasive species or the disturbance of the system and subsequent loss of species – may lead to collapse of entire ecological communities. We will continue to explore the persistence of these dynamic food webs under environmental perturbation after the summer school. We also plan to explore a separate structural model to study how the evolution of networks on both micro- and macroevolutionary timescales could possibly contribute to stability.

=== Political prediction markets: Can we use them to predict election outcomes? ===

Joanne, Vikram, Matteo, Sanith

Prediction markets have been shown to outperform traditional methods of polls and opinion surveys in forecasting future events. The futures contracts traded in these markets assess the expectation of occurrence of a variety of events spread across multiple domains (political, economic, entertainment, financial and weather). We explore the feasibility of 'predicting' the outcome of binary true/false prediction market contracts ahead of their expiry date using a neural-network based machine learning approach. In addition we focus on the characteristics of political-based contracts to establish whether they exhibit characteristic 'fundamental' properties.

=== How Complex Languages Replicate through Simple Brains ===

Katrien, Vanessa, Sandro, Cameron, Jasmeen

Through the use of an iterated learning experiment, we investigated the transmission of a "high entropy", randomised initial language through successive generations of participants. We want to see what features of the language replicated most easily, and what structure emerged by the end of the chain. Our hypothesis is that the language converges to a "low entropy" equilibrium state with a minimal number of words, morphemes, and form-meaning distinctions.

=== Collaboration in times of stress: an Agent Based Modelling approach ===

Fabio Cresto Aleina, Elena del Val, Tom Fennewald and Friederike Greb

We want to investigate the influence of exogenous stress on cooperative behaviour. We propose an agent based model in which the agents can be interpreted as farmers living in a water limited environment. With changes in precipitation patterns, the farmers undergo stress, and we observe how this impacts relationships among farmers and their properties.

=== Simple variation of the logistic map as a model to invoke questions on cellular protein trafficking ===
(Sepehr Ehsani, http://arxiv.org/abs/1206.5557)

Many open problems in biology, as in the physical sciences, display nonlinear and 'chaotic' dynamics, which, to the extent possible, cannot be reasonably understood. Moreover, mathematical models which aim to predict/estimate unknown aspects of a biological system cannot provide more information about the set of biologically meaningful (e.g., 'hidden') states of the system than could be understood by the designer of the model ab initio. Here, the case is made for the utilization of such models to shift from a 'predictive' to a 'questioning' nature, and a simple natural-logarithm variation of the logistic polynomial map is presented that can invoke questions about protein trafficking in eukaryotic cells.

===Changes in Social Network Structure in Response to Crisis: Using Twitter data to Explore the Effect of the Tōhoku Earthquake.===

Christa Brelsford and Xin Lu

Abstract:
We use twitter data from 7 days before and after the Tōhoku Earthquake to explore how cooperation rates, social network structure and connectivity, and social network vulnerability changed in Japan in response to the disaster. An English language data set is collected for the same time period to use as a control. Data is collected for a period of 96 hours starting from March 4th 2011 2:46pm JST and for 96 hours beginning March 11th 2011 2:46 pm JST. The rate of cooperative behavior, measured by the occurrence of helping words in tweets increases slightly in the English dataset and by an order of magnitude in the Japanese dataset. A network analysis is also performed. Network edges are retweets and direct messages. In future work, we hope to explore whether problem solving capacity in a social system changes in response to crises, based on changes in the rate of cooperation and information transfer in a network.

=== The CSSS Network ===

Tom & Riccardo (with JP and others)

We will investigate the questions you are dying to know: What interesting interactions are revealed from the first 3 weeks of the Complex System Summer School survey? Have barriers between academic disciplines been broken down? Do power laws fit the data!? ...

Let us know if you have specific questions or if you would like to be involved in data analysis!

=== Is there a method in the madness? the dynamic structures of human language use ===

Priya and Riccardo

Psychiatric anecdotal reports point to the monotony, lack of emotion and sometimes intelligibility in many clinical populations. Linear measures of fluency and prosody, however, present only controversial differences between patients and healthy controls and only in unnatural phonations (i.e. say "aaaaa" for 30 secs).
We therefore go complex and chaotic on a set of more ecological recordings and transcriptions from 4 clinical populations (Asperger's, Schizophrenics, Depressed and Right Hemisphere Damage patients) as well as from healthy controls.
We then set a classifier-driven race: will non-linear analyses outcompete linear analyses in discriminating between pathologies?

=== Escaping the Poverty Trap: Modeling the Interplay Between Economic Growth and the Ecology of Infectious Disease ===

Georg, Ben, Laurent, Oscar

The dynamics of economies and infectious disease are inexorably linked: economic well-being influences health (sanitation, nutrition, etc) and health influences economic well-being (labor productivity lost to sickness and disease). Often societies are locked into "poverty traps" of poor health and poor economy. Here, we demonstrate poverty traps formed in models of infection and endogenous growth, as well as ways to break out of poverty traps. We explore two mechanisms of escape: one, through an influx of capital, and another through changing the percentage of GDP spent on healthcare. We find large influxes of capital is successful, but increasing health spending does not. Our results have important policy implications in the distribution of aid and within-country healthcare spending.

=== The Targeting and Timing of Treatment Influences the Emergence of Influenza Resistance in Structured Populations ===

Ben, Laurent, Oscar, Georg

Evolution of antiviral resistance in influenza carries large societal impacts through morbidity and mortality caused by treatment failure. Several previous studies put forth theory regarding the optimal timing, targeting and absolute level of treatment in populations. Few of these studies, however, have considered populations with explicit structure. Here we present a model of antiviral resistance on networks and explore the timing, targeting and levels of treatment. Interestingly, we find bistability as a result of treatment leading to the existence of an unstable manifold, above which successful treatment (i.e.: no resistance) is impossible. We find, contrary to previous results, that degree-targeted treatment is not optimal, and leads to higher levels of resistance than random treatment. Additionally, in accordance with previous results, we find an optimum level of treatment which is less than 100%. These findings findings have important consequences in guiding policy behind influenza treatment. The bistability indicates that caution should be taken when treating populations when the absolute numbers of infections are unknown. Positively, our results indicate that putting resources into targeted treatment is not necessary, random treatment is preferable.

=== Learning in Random Boolean Networks ===

Nick A., Keegan, Matteo, Vikram, Sarah, Mark

Inspired by biochemical networks which adapt on evolutionary timescales, neural networks that adapt during development and learning, and universal computation in cellular automata, we have implemented several models of learning in Random Boolean Networks (RBNs) in order to better understand the relationships between network structure, node interaction rules, and network output.

=== Enzyme Catalysis and the Outcome of Chemical Reactions ===
Piotr and Georg W.
Enzymes are catalysts that accelerate chemical reactions but do not affect their outcome. This traditional paradism was developed under artificail test tube conditions. Our project investigates the possibility that the presence of an enzyme can alter the course of a reaction if it takes place under more physiologic conditions.

=== How Does a Stochastic Environment affect Community Assembly? ===

Xue, Χλοε, Xiaoli

We are interested in how exogenous temporal variability in resource availability affects the community structure of organisms with different resource-use strategies. Organisms induce additional resource stress on each other through competition. This is an abstraction of an arid environment with unreliable rainfall; the organisms themselves have been abstracted to four unitless parameters that allocate their resources to different parts of their lifecycles. The system has memory, as the previous presence of an organism affects the resource transport mechanism (an abstraction of soil).

=== How Does a Network’s Structure Influence its Traceability? ===

Xin and Abby

We are interested in systematically studying the problem of finding the source of a contamination spread through a network. We model a contamination spreading through the food distribution network, which we represent by interconnections between farmers, distributors, and retailers, and construct an estimator for the outbreak source given only this structure. We show how the ability of the estimator to narrow down the source identification problem changes with the connectivity and the number of observations. We propose a measure for traceability, or the overall ability to identify the source of spreading given any set of outbreak observations, based on entropy. We show how this measure appropriately reflects the range of uncertainty in identifying the source. We believe this measure may be useful in the design of networks that are conducive to accurate identification of the source of contamination.

=== We Got the Skills to Pay the Bills: Exploring the Link Between Occupation Diversity and Innovation===

Andrés, Charlie, Gareth, and Nick

Where does diversity in skills or occupations come from and why does it lead to more innovative cities? Previous work in this area has shown that there is a scaling behaviour which allows citizens of larger cities to earn an extra 15% in income whilst making use of 15% fewer gas stations, for example. Making use of occupation, patent, and population data of US Metropolitan Statistical Areas (MSA), we try to understand what factors make successful cities. Here we assume that successful cities are those cities which are most innovative as determined by the production of patents. In addition we use agent-based modelling to explore how and why people acquire new skills and whether this leads to more productive cities.

=== Space of complex networks and robustness ===
Ian, Marco, Xin, and Oleksandr

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. Various characteristics of complex networks might influence the robustness differently. The question is how?

We generated continuous topological space of networks with respect to degree distribution (from random to scale-free) and clustering (from none to high). Then we attacked the network by removing vertices randomly and highly connected (hubs). The next step is to calculate network robustness, it is non-trivial task because there are many different ways to do it. So far we calculate the size of giant component during attack process for the entire space.

=== Trade network formation: the role of technology and geography ===

Marco and Matteo

Geography and technology play important roles in economic activities, e.g. international trade flows diminish dramatically with distance; salaries, prices, and factor endowments vary across locations; and productivities are really different across countries' industries. International trade theories have gained some non-negligible reputation explaining the sizes of aggregated trade flows, nonetheless few attention has being payed to the formation of the bilateral trade relations. We develop a network formation model that incorporates differences in technological capabilities across countries and the effect of the geographical distance as a proxy of trade barriers.

===Cultural Evolution of Literary Genres===

Graham, Dan, Benji, Nona

Summary: This project explores the dynamics of cultural evolution using a case study: the rise and differentiation of literary genres. Genres are modeled as feature sets that may undergo gene-like mutations and are subject to the selective pressure of consumer preferences. We also hope to include the impact of institutional actors.

Methods: Agent Based Modeling, Genetic Algorithms, Cluster Analysis, Phylogenetics

===Emergence of Money through an agent based model===
Aleksandra, David, Jianfeng, Vikram

Ultimate goal of the project is to generate the emergence of money in an economy where there are fundamental limits on the ability of agents to commit. Currently we have completed the first 2 of the 4 parts of our ABM. Our most basic model provides a nice example (we think) of a phase transition.

===Level-k thinking, collective behavior, and limit cycles===
Seth, Daniel, Cameron

What do all those things have in common? That's what we're trying to figure out. The sub-presentations of this project are satellites around an experimental result showing that the individual-level heuristic adjustment of what-you-think-I-think-you-think behavior leads to a group-level collective behavior that drives a periodic trajectory through the strategy space of Rock-Paper-Scissors. We will have an analysis of the data, an agent-based model establishing sufficient conditions, and a sketch of an analytic result modeling this phenomenon as symmetry-breaking.

Experiment sign-up

2012-06-19T01:28:19Z

DPugh: /* Tuesday June 19 */

Hi! Please sign up for one slot below. The experiment will be held in the SENIOR Common Room (again) next to the great hall (YES, JP's office). Please arrive on time! Ta.

- Katrien, Vanessa, Sandro, Cameron & Jasmeen

==Monday June 18==

Morning Break:

10.20-10.30: Piotr X

10.30-10.40: Laurent X

Lunch Break:

12.05-12.15: Chloe X

'''12.15-12.25: Andres ???'''

12.25-12.35: Xue X

12.35-12.45: Oleksandr X

12.55-1.05: Nick A X

1.05-1.15: Fabio X

1.15-1.25: Priya X

Afternoon:

5.00-5.10: Matteo X

5.10-5.20: Abby X

5.20-5.30: Elena X

5.30-5.40: Riccardo X

5.40-5.50: [[JP]]! X

6.00-6.10: [[Xin]] X

6.10-6.20: Ian X

6.20-6.30: Keegan

6.30-6.40: Aleksandra

6.40-6.50: Tom X

6.50-7.00:Christa X

==Tuesday June 19==

Morning Break:

10.20-10.30: Vikram

10.30-10.40: Oscar

Lunch Break:

12.30-12.40: Gareth

12.40-12.50: Sanith

12.50-1.00: Nona

1.00-1.10: Ben

1.10-1.20: Sepehr

1.20-1.30: David P

Afternoon:

4.30-4.40: dan

4.40-4.50:

4.50-5.00:

5.00-5.10:

5.10-5.20:

5.20-5.30:

5.30-5.40:

5.40-5.50:

5.50-6.00:

6.00-6.10:

6.10-6.20:

6.20-6.30:

6.30-6.40:

6.40-6.50:

6.50-7.00:

Emergence of Money and Liquidity

2012-06-17T20:55:21Z

DPugh: /* The model */

== Project objective==
The best way to think about money is not to think about money. Do not want to assume what we are trying to explain (i.e., we do not want to code "money" into the model!). With these caveats in mind, the objective of this project is to build an ABM demonstrating the emergence of money that builds off of the previous models of [http://en.wikipedia.org/wiki/Nobuhiro_Kiyotaki Nobu Kiyotaki], [http://en.wikipedia.org/wiki/John_Hardman_Moore John Moore], and [http://en.wikipedia.org/wiki/Randall_Wright Randall Wright]. All of these models take a broad view of money: "money" is any asset which is widely accepted as a medium of exchange (i.e., is highly "liquid").

== Motivating example ==
The following is taken from Kiyotaki and Moore (2001). Suppose I need to get my teeth cleaned. To get my teeth cleaned, I can just go to a dentist who will clean my teeth in exchange for payment. Assume that both the dentist and I have bank accounts, and for simplicity assume that both account balances are initially zero. When I pay for my teeth cleaning using a debit card, funds are immediately transferred from my account to the dentist's. Now my account has a negative balance (i.e., I have issued an IOU to the bank); and the dentist's account has a positive balance (i.e., the bank has issued an IOU to the dentist).

* Question: Instead of using my debit card, why didn't I simply issue one of my own IOUs to the dentist as payment? In other words, why does the bank need to intermediate the transaction between myself and the dentist? Simple answer is that the dentist doesn't trust me enough to repay the debt. In other words my ability to credibly commit to repaying the debt is imperfect. Let 0 < θ ≤ 1 be a measure of my ability to commit with a particular agent (i.e., θ is a measure of ''bilateral commitment''). There is a more subtle answer. The dentist may trust me to repay (particularly as she could threaten to do something nasty to my teeth the next time I needed them cleaned), but perhaps no one ''else'' trusts me (i.e., the dentist is unable to use my IOU for her own purchases). Let 0 < φ ≤ 1 be a measure of my ability to commit with all other agents (i.e., φ is a measure of ''multilateral commitment'').

Note that if the dentist has the ability to perfectly commit with a particular agent (i.e., the dentist's θ=1), then my general lack of trustworthiness (i.e., the fact that both my θ and φ < 1) wouldn't matter. The dentist would be willing to accept my less than perfect IOU because she can simply issue her own IOUs anytime she wants to purchase something.

Suppose that no one else trusts the dentist either. In this case, the dentist can not issue her own IOUs (nor can she endorse my IOUs). Thus the only way the dentist can make purchases before the maturity date of my IOU is if she is paid with a bank IOU (and also, in return, the bank holds my IOU). The dentist gets more benefit from being paid with a bank IOU compared with my IOU. In the language of economics: my debt and bank debt are imperfect substitutes.

The bank’s IOU is used by me and the dentist to lubricate our transaction. Why? Because the bank’s IOU can freely circulate around the economy. Like blood, it is liquid. In fact, the bank's IOU is functionally equivalent to cash. But, unlike cash, it doesn’t come from outside the private system, it comes from inside. For this reason, bank debt is called "inside money". Quantitatively, "inside" money dwarfs "outside" (i.e., cash/coin or currency) money by more than almost 2 orders of magnitude.

The degree of trust between agents in an economy is hugely important in determining the level of investment activity that a given economy can sustain. Unfortunately, trust is a fundamentally scarce resource in economic systems. Why? To borrow from Arthur Eddington (and John Moore), the reason trust is fundamentally scarce resource in economics is because "[http://en.wikipedia.org/wiki/Arrow_of_time time's arrow] cannot fly backwards." Investment is not time-reversible (if it were, then trust would be irrelevant).

In broad terms, the degree of bilateral commitment in an economy places a bound on the entire stock of private paper, whereas the degree of multilateral
commitment determines how much of this paper can circulate.

== What is money? ==
Wikipedia's entry for [http://en.wikipedia.org/wiki/Money money] is a pretty good place to start.

'''Distinction between "inside" and "outside" money:''' When most people think of money, they have in mind cash/coin (i.e., currency) issued by the government. Currency is called "outside" money precisely because it is issued by the government which is by definition "outside" the private economic system. However, there can be many other forms of money that are created within the private economic system, such forms of money are called "inside" money. Richard Lagos from the Minneapolis FED has a nice short [http://www.minneapolisfed.org/research/sr/sr374.pdf paper] on the distinction between the two.

'''==First paper money in the world== by Jianfeng Xu'''
The first paper money “jiaozi” appeared in Sichuan province of China at 10th century. Facing the problem of limited supply of copper to make coins, varies of paper certificates issued by private businesses were circulating in the market. Local official regulated these “IOU” by limiting the issuers to 16 richest families and setting a 2 year limit to cash out or renew these “jiaozi”. (This is inside money). Finally government could not resist stepping in and printing state-issued paper money. Inflation happened when government printed money to cover over spending. Therefore, paper money lose credit and never been widely used until early 1900s. (This is outside money).

== What does money do? ==
I think of a market economy as being a (quite sophisticated) de-centralized optimization algorithm that maximizes the size of the "economic pie" (think output, GDP, etc) subject to resource constraints. Because agents in the economy have only local information about their economic environment, in order for the "algorithm" to work there needs to exist some mechanism for passing information about the relative scarcity of resources between agents. In market economies, prices are the mechanism for transmitting such information. In a way, prices are like the economy's central nervous system in that prices signal the needs of various parts of the economic body. Flow of money and private securities through the economy is analogous to the flow of blood: "money" dispatches resources to different parts of the economic body in response to price signals.

== The model ==
''' Consumption rule:''' Turtles are assumed to consume a fixed fraction, 1 - β (where 0 < β < 1 is a turtle's discount factor), of their start of period net-worth each period.

'''Movement rule:''' Movement is sequential across agents (the order of movement is randomized each period in order to eliminate any type of "first mover" advantage). If the entrepreneur sees available investment project(s) within his field of vision, he will move to the patch containing the project with the highest amount of resources (i.e., collateral). If there are no available investment projects within an entrepreneur's neighborhood, entrepreneurs are assumed to move to the un-occupied patch within their neighborhood that contains the maximal amount of resources.

''' Investment opportunities:''' Investment projects are created on a given patch in period t=0 with some exogenous probability, &pi (investment opportunities are what generate potential demand for borrowing). Investment projects are assumed to require an entrepreneur in order to become active. Basic idea: from time to time, entrepreneurs will encounter an investment opportunity (i.e., they will move to a patch that houses an investment opportunity); investment projects require some fixed number of periods, say T, to complete and once started the entrepreneur managing the project is required to remain fixed on his patch (implicitly we assume that the project cannot continue without the entrepreneur's specific supervision).

'''Production Technology:''' Each agent is assumed to have access to an identical constant returns to scale production technology. Specifically model assumes that output is a function of the amount of grain invested:

yt + T = (xt / a(1 - λ))1 - λ where 0 < &lamdba; < 1

Note that output takes T periods to produce! Useful to characterize the cost of producing a level of output yt + T:

G(y) = xt = a(1 - λ)yt + T1 / (1 - λ)

'''Finance:''' The scale of the project (i.e., the amount of ''y'' produced) will depend on the amount of capital available at the time the project is started, and the amount of capital depends on the amount of finance that the turtle can raise. A turtle with an investment opportunity has several options for finance:
* Self-finance: the turtle may invest his own funds in the project. If turtle has been a lender in the past, he may have assets (i.e., IOUs from other turtles) that he could sell. Additionally, turtle will also have savings (i.e., grain that has been collected but not consumed).
* Borrowed funds: the turtle may borrow funds from any other turtle (within his vision!) that does not have an investment project. Borrowing is subject to a collateral constraint which depends on the turtle's ability to commit bilaterally (i.e., turtle's θ) and on the inherent quality of the patch on which the project is being undertaken. Specifically, suppose that the patch has a ''max-grain-here'' =''G,'' then the turtle can borrow at most ''θG''.

When deciding whether or not to borrow, the turtle ''i'' compares his marginal benefit from borrowing an additional unit with the marginal cost of borrowing from turtle ''j'' and will want to borrow iff:

βiT(1 - θi)a ≥ Rj

The lending turtle ''j'' meanwhile will only be willing to lend iff the marginal benefit from lending exceeds his opportunity cost:

βjTθiRj ≥ max{1, Rk}

Note that the lender's opportunity cost allows for the possibility that he may have other turtles wishing to borrow funds from him! These two inequalities will pin down an interval in which the interest rate must fall if a bi-lateral agreement can be struck between turtle's ''i'' and ''j.'' Also, although a turtle who is managing a project must remained fixed on his patch, a lending turtle can move freely.

== Background reading ==
* [http://www.princeton.edu/~kiyotaki/papers/Evilistherootofallmoney.pdf Evil is the Root of all Money]: 2001 Clarendon Lecture given on money emerging as a solution to a bi-lateral and multi-lateral contracting problem. Origins of the θ - φ model of money.
* [http://www.princeton.edu/~kiyotaki/papers/Financial-Deepening.pdf Financial Deepening]: Gives a more succinct treatment of the θ - φ model of money.
* [https://pantherfile.uwm.edu/vesely/www/831/Kiyotaki%201989%20JPE.pdf On Money as a Medium of Exchange:] Another well known model of model in the economics literature that uses a [http://en.wikipedia.org/wiki/Search_theory search-theoretic] framework.
* Add a relevant paper...

Emergence of Money and Liquidity

2012-06-17T20:52:15Z

DPugh: /* The model */

== Project objective==
The best way to think about money is not to think about money. Do not want to assume what we are trying to explain (i.e., we do not want to code "money" into the model!). With these caveats in mind, the objective of this project is to build an ABM demonstrating the emergence of money that builds off of the previous models of [http://en.wikipedia.org/wiki/Nobuhiro_Kiyotaki Nobu Kiyotaki], [http://en.wikipedia.org/wiki/John_Hardman_Moore John Moore], and [http://en.wikipedia.org/wiki/Randall_Wright Randall Wright]. All of these models take a broad view of money: "money" is any asset which is widely accepted as a medium of exchange (i.e., is highly "liquid").

== Motivating example ==
The following is taken from Kiyotaki and Moore (2001). Suppose I need to get my teeth cleaned. To get my teeth cleaned, I can just go to a dentist who will clean my teeth in exchange for payment. Assume that both the dentist and I have bank accounts, and for simplicity assume that both account balances are initially zero. When I pay for my teeth cleaning using a debit card, funds are immediately transferred from my account to the dentist's. Now my account has a negative balance (i.e., I have issued an IOU to the bank); and the dentist's account has a positive balance (i.e., the bank has issued an IOU to the dentist).

* Question: Instead of using my debit card, why didn't I simply issue one of my own IOUs to the dentist as payment? In other words, why does the bank need to intermediate the transaction between myself and the dentist? Simple answer is that the dentist doesn't trust me enough to repay the debt. In other words my ability to credibly commit to repaying the debt is imperfect. Let 0 < θ ≤ 1 be a measure of my ability to commit with a particular agent (i.e., θ is a measure of ''bilateral commitment''). There is a more subtle answer. The dentist may trust me to repay (particularly as she could threaten to do something nasty to my teeth the next time I needed them cleaned), but perhaps no one ''else'' trusts me (i.e., the dentist is unable to use my IOU for her own purchases). Let 0 < φ ≤ 1 be a measure of my ability to commit with all other agents (i.e., φ is a measure of ''multilateral commitment'').

Note that if the dentist has the ability to perfectly commit with a particular agent (i.e., the dentist's θ=1), then my general lack of trustworthiness (i.e., the fact that both my θ and φ < 1) wouldn't matter. The dentist would be willing to accept my less than perfect IOU because she can simply issue her own IOUs anytime she wants to purchase something.

Suppose that no one else trusts the dentist either. In this case, the dentist can not issue her own IOUs (nor can she endorse my IOUs). Thus the only way the dentist can make purchases before the maturity date of my IOU is if she is paid with a bank IOU (and also, in return, the bank holds my IOU). The dentist gets more benefit from being paid with a bank IOU compared with my IOU. In the language of economics: my debt and bank debt are imperfect substitutes.

The bank’s IOU is used by me and the dentist to lubricate our transaction. Why? Because the bank’s IOU can freely circulate around the economy. Like blood, it is liquid. In fact, the bank's IOU is functionally equivalent to cash. But, unlike cash, it doesn’t come from outside the private system, it comes from inside. For this reason, bank debt is called "inside money". Quantitatively, "inside" money dwarfs "outside" (i.e., cash/coin or currency) money by more than almost 2 orders of magnitude.

The degree of trust between agents in an economy is hugely important in determining the level of investment activity that a given economy can sustain. Unfortunately, trust is a fundamentally scarce resource in economic systems. Why? To borrow from Arthur Eddington (and John Moore), the reason trust is fundamentally scarce resource in economics is because "[http://en.wikipedia.org/wiki/Arrow_of_time time's arrow] cannot fly backwards." Investment is not time-reversible (if it were, then trust would be irrelevant).

In broad terms, the degree of bilateral commitment in an economy places a bound on the entire stock of private paper, whereas the degree of multilateral
commitment determines how much of this paper can circulate.

== What is money? ==
Wikipedia's entry for [http://en.wikipedia.org/wiki/Money money] is a pretty good place to start.

'''Distinction between "inside" and "outside" money:''' When most people think of money, they have in mind cash/coin (i.e., currency) issued by the government. Currency is called "outside" money precisely because it is issued by the government which is by definition "outside" the private economic system. However, there can be many other forms of money that are created within the private economic system, such forms of money are called "inside" money. Richard Lagos from the Minneapolis FED has a nice short [http://www.minneapolisfed.org/research/sr/sr374.pdf paper] on the distinction between the two.

'''==First paper money in the world== by Jianfeng Xu'''
The first paper money “jiaozi” appeared in Sichuan province of China at 10th century. Facing the problem of limited supply of copper to make coins, varies of paper certificates issued by private businesses were circulating in the market. Local official regulated these “IOU” by limiting the issuers to 16 richest families and setting a 2 year limit to cash out or renew these “jiaozi”. (This is inside money). Finally government could not resist stepping in and printing state-issued paper money. Inflation happened when government printed money to cover over spending. Therefore, paper money lose credit and never been widely used until early 1900s. (This is outside money).

== What does money do? ==
I think of a market economy as being a (quite sophisticated) de-centralized optimization algorithm that maximizes the size of the "economic pie" (think output, GDP, etc) subject to resource constraints. Because agents in the economy have only local information about their economic environment, in order for the "algorithm" to work there needs to exist some mechanism for passing information about the relative scarcity of resources between agents. In market economies, prices are the mechanism for transmitting such information. In a way, prices are like the economy's central nervous system in that prices signal the needs of various parts of the economic body. Flow of money and private securities through the economy is analogous to the flow of blood: "money" dispatches resources to different parts of the economic body in response to price signals.

== The model ==
''' Consumption rule:''' Turtles are assumed to consume a fixed fraction, 1 - β (where 0 < β < 1 is a turtle's discount factor), of their start of period net-worth each period.

'''Movement rule:''' Movement is sequential across agents (the order of movement is randomized each period in order to eliminate any type of "first mover" advantage). If the entrepreneur sees available investment project(s) within his field of vision, he will move to the patch containing the project with the highest amount of resources (i.e., collateral). If there are no available investment projects within an entrepreneur's neighborhood, entrepreneurs are assumed to move to the un-occupied patch within their neighborhood that contains the maximal amount of resources.

''' Investment opportunities:''' Investment projects are created on a given patch in period t=0 with some exogenous probability, &pi (investment opportunities are what generate potential demand for borrowing). Investment projects are assumed to require an entrepreneur in order to become active. Basic idea: from time to time, entrepreneurs will encounter an investment opportunity (i.e., they will move to a patch that houses an investment opportunity); investment projects require some fixed number of periods, say T, to complete and once started the entrepreneur managing the project is required to remain fixed on his patch (implicitly we assume that the project cannot continue without the entrepreneur's specific supervision).

'''Production Technology:''' Each agent is assumed to have access to an identical constant returns to scale production technology. Specifically model assumes that output is a function of the amount of grain invested:

yt + T = (xt / a(1 - λ))1 - λ where 0 < &lamdba; < 1

Note that output takes T periods to produce! Useful to characterize the cost of producing a level of output yt + T:

xt = a(1 - λ)yt + T1 / (1 - λ)

'''Finance:''' The scale of the project (i.e., the amount of ''y'' produced) will depend on the amount of capital available at the time the project is started, and the amount of capital depends on the amount of finance that the turtle can raise. A turtle with an investment opportunity has several options for finance:
* Self-finance: the turtle may invest his own funds in the project. If turtle has been a lender in the past, he may have assets (i.e., IOUs from other turtles) that he could sell. Additionally, turtle will also have savings (i.e., grain that has been collected but not consumed).
* Borrowed funds: the turtle may borrow funds from any other turtle (within his vision!) that does not have an investment project. Borrowing is subject to a collateral constraint which depends on the turtle's ability to commit bilaterally (i.e., turtle's θ) and on the inherent quality of the patch on which the project is being undertaken. Specifically, suppose that the patch has a ''max-grain-here'' =''G,'' then the turtle can borrow at most ''θG''.

When deciding whether or not to borrow, the turtle ''i'' compares his marginal benefit from borrowing an additional unit with the marginal cost of borrowing from turtle ''j'' and will want to borrow iff:

βiT(1 - θi)a ≥ Rj

The lending turtle ''j'' meanwhile will only be willing to lend iff the marginal benefit from lending exceeds his opportunity cost:

βjTθiRj ≥ max{1, Rk}

Note that the lender's opportunity cost allows for the possibility that he may have other turtles wishing to borrow funds from him! These two inequalities will pin down an interval in which the interest rate must fall if a bi-lateral agreement can be struck between turtle's ''i'' and ''j.'' Also, although a turtle who is managing a project must remained fixed on his patch, a lending turtle can move freely.

== Background reading ==
* [http://www.princeton.edu/~kiyotaki/papers/Evilistherootofallmoney.pdf Evil is the Root of all Money]: 2001 Clarendon Lecture given on money emerging as a solution to a bi-lateral and multi-lateral contracting problem. Origins of the θ - φ model of money.
* [http://www.princeton.edu/~kiyotaki/papers/Financial-Deepening.pdf Financial Deepening]: Gives a more succinct treatment of the θ - φ model of money.
* [https://pantherfile.uwm.edu/vesely/www/831/Kiyotaki%201989%20JPE.pdf On Money as a Medium of Exchange:] Another well known model of model in the economics literature that uses a [http://en.wikipedia.org/wiki/Search_theory search-theoretic] framework.
* Add a relevant paper...

Emergence of Money and Liquidity

2012-06-17T20:51:35Z

DPugh: /* The model */

== Project objective==
The best way to think about money is not to think about money. Do not want to assume what we are trying to explain (i.e., we do not want to code "money" into the model!). With these caveats in mind, the objective of this project is to build an ABM demonstrating the emergence of money that builds off of the previous models of [http://en.wikipedia.org/wiki/Nobuhiro_Kiyotaki Nobu Kiyotaki], [http://en.wikipedia.org/wiki/John_Hardman_Moore John Moore], and [http://en.wikipedia.org/wiki/Randall_Wright Randall Wright]. All of these models take a broad view of money: "money" is any asset which is widely accepted as a medium of exchange (i.e., is highly "liquid").

== Motivating example ==
The following is taken from Kiyotaki and Moore (2001). Suppose I need to get my teeth cleaned. To get my teeth cleaned, I can just go to a dentist who will clean my teeth in exchange for payment. Assume that both the dentist and I have bank accounts, and for simplicity assume that both account balances are initially zero. When I pay for my teeth cleaning using a debit card, funds are immediately transferred from my account to the dentist's. Now my account has a negative balance (i.e., I have issued an IOU to the bank); and the dentist's account has a positive balance (i.e., the bank has issued an IOU to the dentist).

* Question: Instead of using my debit card, why didn't I simply issue one of my own IOUs to the dentist as payment? In other words, why does the bank need to intermediate the transaction between myself and the dentist? Simple answer is that the dentist doesn't trust me enough to repay the debt. In other words my ability to credibly commit to repaying the debt is imperfect. Let 0 < θ ≤ 1 be a measure of my ability to commit with a particular agent (i.e., θ is a measure of ''bilateral commitment''). There is a more subtle answer. The dentist may trust me to repay (particularly as she could threaten to do something nasty to my teeth the next time I needed them cleaned), but perhaps no one ''else'' trusts me (i.e., the dentist is unable to use my IOU for her own purchases). Let 0 < φ ≤ 1 be a measure of my ability to commit with all other agents (i.e., φ is a measure of ''multilateral commitment'').

Note that if the dentist has the ability to perfectly commit with a particular agent (i.e., the dentist's θ=1), then my general lack of trustworthiness (i.e., the fact that both my θ and φ < 1) wouldn't matter. The dentist would be willing to accept my less than perfect IOU because she can simply issue her own IOUs anytime she wants to purchase something.

Suppose that no one else trusts the dentist either. In this case, the dentist can not issue her own IOUs (nor can she endorse my IOUs). Thus the only way the dentist can make purchases before the maturity date of my IOU is if she is paid with a bank IOU (and also, in return, the bank holds my IOU). The dentist gets more benefit from being paid with a bank IOU compared with my IOU. In the language of economics: my debt and bank debt are imperfect substitutes.

The bank’s IOU is used by me and the dentist to lubricate our transaction. Why? Because the bank’s IOU can freely circulate around the economy. Like blood, it is liquid. In fact, the bank's IOU is functionally equivalent to cash. But, unlike cash, it doesn’t come from outside the private system, it comes from inside. For this reason, bank debt is called "inside money". Quantitatively, "inside" money dwarfs "outside" (i.e., cash/coin or currency) money by more than almost 2 orders of magnitude.

The degree of trust between agents in an economy is hugely important in determining the level of investment activity that a given economy can sustain. Unfortunately, trust is a fundamentally scarce resource in economic systems. Why? To borrow from Arthur Eddington (and John Moore), the reason trust is fundamentally scarce resource in economics is because "[http://en.wikipedia.org/wiki/Arrow_of_time time's arrow] cannot fly backwards." Investment is not time-reversible (if it were, then trust would be irrelevant).

In broad terms, the degree of bilateral commitment in an economy places a bound on the entire stock of private paper, whereas the degree of multilateral
commitment determines how much of this paper can circulate.

== What is money? ==
Wikipedia's entry for [http://en.wikipedia.org/wiki/Money money] is a pretty good place to start.

'''Distinction between "inside" and "outside" money:''' When most people think of money, they have in mind cash/coin (i.e., currency) issued by the government. Currency is called "outside" money precisely because it is issued by the government which is by definition "outside" the private economic system. However, there can be many other forms of money that are created within the private economic system, such forms of money are called "inside" money. Richard Lagos from the Minneapolis FED has a nice short [http://www.minneapolisfed.org/research/sr/sr374.pdf paper] on the distinction between the two.

'''==First paper money in the world== by Jianfeng Xu'''
The first paper money “jiaozi” appeared in Sichuan province of China at 10th century. Facing the problem of limited supply of copper to make coins, varies of paper certificates issued by private businesses were circulating in the market. Local official regulated these “IOU” by limiting the issuers to 16 richest families and setting a 2 year limit to cash out or renew these “jiaozi”. (This is inside money). Finally government could not resist stepping in and printing state-issued paper money. Inflation happened when government printed money to cover over spending. Therefore, paper money lose credit and never been widely used until early 1900s. (This is outside money).

== What does money do? ==
I think of a market economy as being a (quite sophisticated) de-centralized optimization algorithm that maximizes the size of the "economic pie" (think output, GDP, etc) subject to resource constraints. Because agents in the economy have only local information about their economic environment, in order for the "algorithm" to work there needs to exist some mechanism for passing information about the relative scarcity of resources between agents. In market economies, prices are the mechanism for transmitting such information. In a way, prices are like the economy's central nervous system in that prices signal the needs of various parts of the economic body. Flow of money and private securities through the economy is analogous to the flow of blood: "money" dispatches resources to different parts of the economic body in response to price signals.

== The model ==
''' Consumption rule:''' Turtles are assumed to consume a fixed fraction, 1 - β (where 0 < β < 1 is a turtle's discount factor), of their start of period net-worth each period.

'''Movement rule:''' Movement is sequential across agents (the order of movement is randomized each period in order to eliminate any type of "first mover" advantage). If the entrepreneur sees available investment project(s) within his field of vision, he will move to the patch containing the project with the highest amount of resources (i.e., collateral). If there are no available investment projects within an entrepreneur's neighborhood, entrepreneurs are assumed to move to the un-occupied patch within their neighborhood that contains the maximal amount of resources.

''' Investment opportunities:''' Investment projects are created on a given patch in period t=0 with some exogenous probability, &pi (investment opportunities are what generate potential demand for borrowing). Investment projects are assumed to require an entrepreneur in order to become active. Basic idea: from time to time, entrepreneurs will encounter an investment opportunity (i.e., they will move to a patch that houses an investment opportunity); investment projects require some fixed number of periods, say T, to complete and once started the entrepreneur managing the project is required to remain fixed on his patch (implicitly we assume that the project cannot continue without the entrepreneur's specific supervision).

'''Production Technology:''' Each agent is assumed to have access to an identical constant returns to scale production technology. Specifically model assumes that output is a function of the amount of grain invested:

yt + T = (xt / a(1 - λ))1 - λ

Note that output takes T periods to produce! Useful to characterize the cost of producing a level of output yt + T:

xt = a(1 - λ)yt + T1 / (1 - λ)

'''Finance:''' The scale of the project (i.e., the amount of ''y'' produced) will depend on the amount of capital available at the time the project is started, and the amount of capital depends on the amount of finance that the turtle can raise. A turtle with an investment opportunity has several options for finance:
* Self-finance: the turtle may invest his own funds in the project. If turtle has been a lender in the past, he may have assets (i.e., IOUs from other turtles) that he could sell. Additionally, turtle will also have savings (i.e., grain that has been collected but not consumed).
* Borrowed funds: the turtle may borrow funds from any other turtle (within his vision!) that does not have an investment project. Borrowing is subject to a collateral constraint which depends on the turtle's ability to commit bilaterally (i.e., turtle's θ) and on the inherent quality of the patch on which the project is being undertaken. Specifically, suppose that the patch has a ''max-grain-here'' =''G,'' then the turtle can borrow at most ''θG''.

When deciding whether or not to borrow, the turtle ''i'' compares his marginal benefit from borrowing an additional unit with the marginal cost of borrowing from turtle ''j'' and will want to borrow iff:

βiT(1 - θi)a ≥ Rj

The lending turtle ''j'' meanwhile will only be willing to lend iff the marginal benefit from lending exceeds his opportunity cost:

βjTθiRj ≥ max{1, Rk}

Note that the lender's opportunity cost allows for the possibility that he may have other turtles wishing to borrow funds from him! These two inequalities will pin down an interval in which the interest rate must fall if a bi-lateral agreement can be struck between turtle's ''i'' and ''j.'' Also, although a turtle who is managing a project must remained fixed on his patch, a lending turtle can move freely.

== Background reading ==
* [http://www.princeton.edu/~kiyotaki/papers/Evilistherootofallmoney.pdf Evil is the Root of all Money]: 2001 Clarendon Lecture given on money emerging as a solution to a bi-lateral and multi-lateral contracting problem. Origins of the θ - φ model of money.
* [http://www.princeton.edu/~kiyotaki/papers/Financial-Deepening.pdf Financial Deepening]: Gives a more succinct treatment of the θ - φ model of money.
* [https://pantherfile.uwm.edu/vesely/www/831/Kiyotaki%201989%20JPE.pdf On Money as a Medium of Exchange:] Another well known model of model in the economics literature that uses a [http://en.wikipedia.org/wiki/Search_theory search-theoretic] framework.
* Add a relevant paper...

Emergence of Money and Liquidity

2012-06-17T20:48:10Z

DPugh: /* The model */

== Project objective==
The best way to think about money is not to think about money. Do not want to assume what we are trying to explain (i.e., we do not want to code "money" into the model!). With these caveats in mind, the objective of this project is to build an ABM demonstrating the emergence of money that builds off of the previous models of [http://en.wikipedia.org/wiki/Nobuhiro_Kiyotaki Nobu Kiyotaki], [http://en.wikipedia.org/wiki/John_Hardman_Moore John Moore], and [http://en.wikipedia.org/wiki/Randall_Wright Randall Wright]. All of these models take a broad view of money: "money" is any asset which is widely accepted as a medium of exchange (i.e., is highly "liquid").

== Motivating example ==
The following is taken from Kiyotaki and Moore (2001). Suppose I need to get my teeth cleaned. To get my teeth cleaned, I can just go to a dentist who will clean my teeth in exchange for payment. Assume that both the dentist and I have bank accounts, and for simplicity assume that both account balances are initially zero. When I pay for my teeth cleaning using a debit card, funds are immediately transferred from my account to the dentist's. Now my account has a negative balance (i.e., I have issued an IOU to the bank); and the dentist's account has a positive balance (i.e., the bank has issued an IOU to the dentist).

* Question: Instead of using my debit card, why didn't I simply issue one of my own IOUs to the dentist as payment? In other words, why does the bank need to intermediate the transaction between myself and the dentist? Simple answer is that the dentist doesn't trust me enough to repay the debt. In other words my ability to credibly commit to repaying the debt is imperfect. Let 0 < θ ≤ 1 be a measure of my ability to commit with a particular agent (i.e., θ is a measure of ''bilateral commitment''). There is a more subtle answer. The dentist may trust me to repay (particularly as she could threaten to do something nasty to my teeth the next time I needed them cleaned), but perhaps no one ''else'' trusts me (i.e., the dentist is unable to use my IOU for her own purchases). Let 0 < φ ≤ 1 be a measure of my ability to commit with all other agents (i.e., φ is a measure of ''multilateral commitment'').

Note that if the dentist has the ability to perfectly commit with a particular agent (i.e., the dentist's θ=1), then my general lack of trustworthiness (i.e., the fact that both my θ and φ < 1) wouldn't matter. The dentist would be willing to accept my less than perfect IOU because she can simply issue her own IOUs anytime she wants to purchase something.

Suppose that no one else trusts the dentist either. In this case, the dentist can not issue her own IOUs (nor can she endorse my IOUs). Thus the only way the dentist can make purchases before the maturity date of my IOU is if she is paid with a bank IOU (and also, in return, the bank holds my IOU). The dentist gets more benefit from being paid with a bank IOU compared with my IOU. In the language of economics: my debt and bank debt are imperfect substitutes.

The bank’s IOU is used by me and the dentist to lubricate our transaction. Why? Because the bank’s IOU can freely circulate around the economy. Like blood, it is liquid. In fact, the bank's IOU is functionally equivalent to cash. But, unlike cash, it doesn’t come from outside the private system, it comes from inside. For this reason, bank debt is called "inside money". Quantitatively, "inside" money dwarfs "outside" (i.e., cash/coin or currency) money by more than almost 2 orders of magnitude.

The degree of trust between agents in an economy is hugely important in determining the level of investment activity that a given economy can sustain. Unfortunately, trust is a fundamentally scarce resource in economic systems. Why? To borrow from Arthur Eddington (and John Moore), the reason trust is fundamentally scarce resource in economics is because "[http://en.wikipedia.org/wiki/Arrow_of_time time's arrow] cannot fly backwards." Investment is not time-reversible (if it were, then trust would be irrelevant).

In broad terms, the degree of bilateral commitment in an economy places a bound on the entire stock of private paper, whereas the degree of multilateral
commitment determines how much of this paper can circulate.

== What is money? ==
Wikipedia's entry for [http://en.wikipedia.org/wiki/Money money] is a pretty good place to start.

'''Distinction between "inside" and "outside" money:''' When most people think of money, they have in mind cash/coin (i.e., currency) issued by the government. Currency is called "outside" money precisely because it is issued by the government which is by definition "outside" the private economic system. However, there can be many other forms of money that are created within the private economic system, such forms of money are called "inside" money. Richard Lagos from the Minneapolis FED has a nice short [http://www.minneapolisfed.org/research/sr/sr374.pdf paper] on the distinction between the two.

'''==First paper money in the world== by Jianfeng Xu'''
The first paper money “jiaozi” appeared in Sichuan province of China at 10th century. Facing the problem of limited supply of copper to make coins, varies of paper certificates issued by private businesses were circulating in the market. Local official regulated these “IOU” by limiting the issuers to 16 richest families and setting a 2 year limit to cash out or renew these “jiaozi”. (This is inside money). Finally government could not resist stepping in and printing state-issued paper money. Inflation happened when government printed money to cover over spending. Therefore, paper money lose credit and never been widely used until early 1900s. (This is outside money).

== What does money do? ==
I think of a market economy as being a (quite sophisticated) de-centralized optimization algorithm that maximizes the size of the "economic pie" (think output, GDP, etc) subject to resource constraints. Because agents in the economy have only local information about their economic environment, in order for the "algorithm" to work there needs to exist some mechanism for passing information about the relative scarcity of resources between agents. In market economies, prices are the mechanism for transmitting such information. In a way, prices are like the economy's central nervous system in that prices signal the needs of various parts of the economic body. Flow of money and private securities through the economy is analogous to the flow of blood: "money" dispatches resources to different parts of the economic body in response to price signals.

== The model ==
''' Consumption rule:''' Turtles are assumed to consume a fixed fraction, 1 - β (where 0 < β < 1 is a turtle's discount factor), of their start of period net-worth each period.

'''Movement rule:''' Movement is sequential across agents (the order of movement is randomized each period in order to eliminate any type of "first mover" advantage). If the entrepreneur sees available investment project(s) within his field of vision, he will move to the patch containing the project with the highest amount of resources (i.e., collateral). If there are no available investment projects within an entrepreneur's neighborhood, entrepreneurs are assumed to move to the un-occupied patch within their neighborhood that contains the maximal amount of resources.

''' Investment opportunities:''' Investment projects are created on a given patch in period t=0 with some exogenous probability, &pi (investment opportunities are what generate potential demand for borrowing). Investment projects are assumed to require an entrepreneur in order to become active. Basic idea: from time to time, entrepreneurs will encounter an investment opportunity (i.e., they will move to a patch that houses an investment opportunity); investment projects require some fixed number of periods, say T, to complete and once started the entrepreneur managing the project is required to remain fixed on his patch (implicitly we assume that the project cannot continue without the entrepreneur's specific supervision).

'''Production Technology:''' Each agent is assumed to have access to an identical constant returns to scale production technology. Specifically model assumes that output is a function of capital:

yt + T = (xt / a(1 - λ))1 - λ, where a > 1.

Note that output takes T periods to produce!

'''Finance:''' The scale of the project (i.e., the amount of ''y'' produced) will depend on the amount of capital available at the time the project is started, and the amount of capital depends on the amount of finance that the turtle can raise. A turtle with an investment opportunity has several options for finance:
* Self-finance: the turtle may invest his own funds in the project. If turtle has been a lender in the past, he may have assets (i.e., IOUs from other turtles) that he could sell. Additionally, turtle will also have savings (i.e., grain that has been collected but not consumed).
* Borrowed funds: the turtle may borrow funds from any other turtle (within his vision!) that does not have an investment project. Borrowing is subject to a collateral constraint which depends on the turtle's ability to commit bilaterally (i.e., turtle's θ) and on the inherent quality of the patch on which the project is being undertaken. Specifically, suppose that the patch has a ''max-grain-here'' =''G,'' then the turtle can borrow at most ''θG''.

When deciding whether or not to borrow, the turtle ''i'' compares his marginal benefit from borrowing an additional unit with the marginal cost of borrowing from turtle ''j'' and will want to borrow iff:

βiT(1 - θi)a ≥ Rj

The lending turtle ''j'' meanwhile will only be willing to lend iff the marginal benefit from lending exceeds his opportunity cost:

βjTθiRj ≥ max{1, Rk}

Note that the lender's opportunity cost allows for the possibility that he may have other turtles wishing to borrow funds from him! These two inequalities will pin down an interval in which the interest rate must fall if a bi-lateral agreement can be struck between turtle's ''i'' and ''j.'' Also, although a turtle who is managing a project must remained fixed on his patch, a lending turtle can move freely.

== Background reading ==
* [http://www.princeton.edu/~kiyotaki/papers/Evilistherootofallmoney.pdf Evil is the Root of all Money]: 2001 Clarendon Lecture given on money emerging as a solution to a bi-lateral and multi-lateral contracting problem. Origins of the θ - φ model of money.
* [http://www.princeton.edu/~kiyotaki/papers/Financial-Deepening.pdf Financial Deepening]: Gives a more succinct treatment of the θ - φ model of money.
* [https://pantherfile.uwm.edu/vesely/www/831/Kiyotaki%201989%20JPE.pdf On Money as a Medium of Exchange:] Another well known model of model in the economics literature that uses a [http://en.wikipedia.org/wiki/Search_theory search-theoretic] framework.
* Add a relevant paper...

Emergence of Money and Liquidity

2012-06-17T20:41:30Z

DPugh: /* Motivating example */

== Project objective==
The best way to think about money is not to think about money. Do not want to assume what we are trying to explain (i.e., we do not want to code "money" into the model!). With these caveats in mind, the objective of this project is to build an ABM demonstrating the emergence of money that builds off of the previous models of [http://en.wikipedia.org/wiki/Nobuhiro_Kiyotaki Nobu Kiyotaki], [http://en.wikipedia.org/wiki/John_Hardman_Moore John Moore], and [http://en.wikipedia.org/wiki/Randall_Wright Randall Wright]. All of these models take a broad view of money: "money" is any asset which is widely accepted as a medium of exchange (i.e., is highly "liquid").

== Motivating example ==
The following is taken from Kiyotaki and Moore (2001). Suppose I need to get my teeth cleaned. To get my teeth cleaned, I can just go to a dentist who will clean my teeth in exchange for payment. Assume that both the dentist and I have bank accounts, and for simplicity assume that both account balances are initially zero. When I pay for my teeth cleaning using a debit card, funds are immediately transferred from my account to the dentist's. Now my account has a negative balance (i.e., I have issued an IOU to the bank); and the dentist's account has a positive balance (i.e., the bank has issued an IOU to the dentist).

* Question: Instead of using my debit card, why didn't I simply issue one of my own IOUs to the dentist as payment? In other words, why does the bank need to intermediate the transaction between myself and the dentist? Simple answer is that the dentist doesn't trust me enough to repay the debt. In other words my ability to credibly commit to repaying the debt is imperfect. Let 0 < θ ≤ 1 be a measure of my ability to commit with a particular agent (i.e., θ is a measure of ''bilateral commitment''). There is a more subtle answer. The dentist may trust me to repay (particularly as she could threaten to do something nasty to my teeth the next time I needed them cleaned), but perhaps no one ''else'' trusts me (i.e., the dentist is unable to use my IOU for her own purchases). Let 0 < φ ≤ 1 be a measure of my ability to commit with all other agents (i.e., φ is a measure of ''multilateral commitment'').

Note that if the dentist has the ability to perfectly commit with a particular agent (i.e., the dentist's θ=1), then my general lack of trustworthiness (i.e., the fact that both my θ and φ < 1) wouldn't matter. The dentist would be willing to accept my less than perfect IOU because she can simply issue her own IOUs anytime she wants to purchase something.

Suppose that no one else trusts the dentist either. In this case, the dentist can not issue her own IOUs (nor can she endorse my IOUs). Thus the only way the dentist can make purchases before the maturity date of my IOU is if she is paid with a bank IOU (and also, in return, the bank holds my IOU). The dentist gets more benefit from being paid with a bank IOU compared with my IOU. In the language of economics: my debt and bank debt are imperfect substitutes.

The bank’s IOU is used by me and the dentist to lubricate our transaction. Why? Because the bank’s IOU can freely circulate around the economy. Like blood, it is liquid. In fact, the bank's IOU is functionally equivalent to cash. But, unlike cash, it doesn’t come from outside the private system, it comes from inside. For this reason, bank debt is called "inside money". Quantitatively, "inside" money dwarfs "outside" (i.e., cash/coin or currency) money by more than almost 2 orders of magnitude.

The degree of trust between agents in an economy is hugely important in determining the level of investment activity that a given economy can sustain. Unfortunately, trust is a fundamentally scarce resource in economic systems. Why? To borrow from Arthur Eddington (and John Moore), the reason trust is fundamentally scarce resource in economics is because "[http://en.wikipedia.org/wiki/Arrow_of_time time's arrow] cannot fly backwards." Investment is not time-reversible (if it were, then trust would be irrelevant).

In broad terms, the degree of bilateral commitment in an economy places a bound on the entire stock of private paper, whereas the degree of multilateral
commitment determines how much of this paper can circulate.

== What is money? ==
Wikipedia's entry for [http://en.wikipedia.org/wiki/Money money] is a pretty good place to start.

'''Distinction between "inside" and "outside" money:''' When most people think of money, they have in mind cash/coin (i.e., currency) issued by the government. Currency is called "outside" money precisely because it is issued by the government which is by definition "outside" the private economic system. However, there can be many other forms of money that are created within the private economic system, such forms of money are called "inside" money. Richard Lagos from the Minneapolis FED has a nice short [http://www.minneapolisfed.org/research/sr/sr374.pdf paper] on the distinction between the two.

'''==First paper money in the world== by Jianfeng Xu'''
The first paper money “jiaozi” appeared in Sichuan province of China at 10th century. Facing the problem of limited supply of copper to make coins, varies of paper certificates issued by private businesses were circulating in the market. Local official regulated these “IOU” by limiting the issuers to 16 richest families and setting a 2 year limit to cash out or renew these “jiaozi”. (This is inside money). Finally government could not resist stepping in and printing state-issued paper money. Inflation happened when government printed money to cover over spending. Therefore, paper money lose credit and never been widely used until early 1900s. (This is outside money).

== What does money do? ==
I think of a market economy as being a (quite sophisticated) de-centralized optimization algorithm that maximizes the size of the "economic pie" (think output, GDP, etc) subject to resource constraints. Because agents in the economy have only local information about their economic environment, in order for the "algorithm" to work there needs to exist some mechanism for passing information about the relative scarcity of resources between agents. In market economies, prices are the mechanism for transmitting such information. In a way, prices are like the economy's central nervous system in that prices signal the needs of various parts of the economic body. Flow of money and private securities through the economy is analogous to the flow of blood: "money" dispatches resources to different parts of the economic body in response to price signals.

== The model ==
''' Consumption rule:''' Turtles are assumed to consume a fixed fraction, 1 - β (where 0 < β < 1 is a turtle's discount factor), of their start of period net-worth each period.

'''Movement rule:''' Movement is sequential across agents (the order of movement is randomized each period in order to eliminate any type of "first mover" advantage). If the entrepreneur sees available investment project(s) within his field of vision, he will move to the patch containing the project with the highest amount of resources (i.e., collateral). If there are no available investment projects within an entrepreneur's neighborhood, entrepreneurs are assumed to move to the un-occupied patch within their neighborhood that contains the maximal amount of resources.

''' Investment opportunities:''' Investment projects are created on a given patch in period t=0 with some exogenous probability, &pi (investment opportunities are what generate potential demand for borrowing). Investment projects are assumed to require an entrepreneur in order to become active. Basic idea: from time to time, entrepreneurs will encounter an investment opportunity (i.e., they will move to a patch that houses an investment opportunity); investment projects require some fixed number of periods, say T, to complete and once started the entrepreneur managing the project is required to remain fixed on his patch (implicitly we assume that the project cannot continue without the entrepreneur's specific supervision).

'''Production Technology:''' Each agent is assumed to have access to an identical constant returns to scale production technology. Specifically model assumes that output is a function of capital:

yt + T = akt, where a > 1.

Note that output takes T periods to produce!

'''Finance:''' The scale of the project (i.e., the amount of ''y'' produced) will depend on the amount of capital available at the time the project is started, and the amount of capital depends on the amount of finance that the turtle can raise. A turtle with an investment opportunity has several options for finance:
* Self-finance: the turtle may invest his own funds in the project. If turtle has been a lender in the past, he may have assets (i.e., IOUs from other turtles) that he could sell. Additionally, turtle will also have savings (i.e., grain that has been collected but not consumed).
* Borrowed funds: the turtle may borrow funds from any other turtle (within his vision!) that does not have an investment project. Borrowing is subject to a collateral constraint which depends on the turtle's ability to commit bilaterally (i.e., turtle's θ) and on the inherent quality of the patch on which the project is being undertaken. Specifically, suppose that the patch has a ''max-grain-here'' =''G,'' then the turtle can borrow at most ''θG''.

When deciding whether or not to borrow, the turtle ''i'' compares his marginal benefit from borrowing an additional unit with the marginal cost of borrowing from turtle ''j'' and will want to borrow iff:

βiT(1 - θi)a ≥ Rj

The lending turtle ''j'' meanwhile will only be willing to lend iff the marginal benefit from lending exceeds his opportunity cost:

βjTθiRj ≥ max{1, Rk}

Note that the lender's opportunity cost allows for the possibility that he may have other turtles wishing to borrow funds from him! These two inequalities will pin down an interval in which the interest rate must fall if a bi-lateral agreement can be struck between turtle's ''i'' and ''j.'' Also, although a turtle who is managing a project must remained fixed on his patch, a lending turtle can move freely.

== Background reading ==
* [http://www.princeton.edu/~kiyotaki/papers/Evilistherootofallmoney.pdf Evil is the Root of all Money]: 2001 Clarendon Lecture given on money emerging as a solution to a bi-lateral and multi-lateral contracting problem. Origins of the θ - φ model of money.
* [http://www.princeton.edu/~kiyotaki/papers/Financial-Deepening.pdf Financial Deepening]: Gives a more succinct treatment of the θ - φ model of money.
* [https://pantherfile.uwm.edu/vesely/www/831/Kiyotaki%201989%20JPE.pdf On Money as a Medium of Exchange:] Another well known model of model in the economics literature that uses a [http://en.wikipedia.org/wiki/Search_theory search-theoretic] framework.
* Add a relevant paper...

Emergence of Money and Liquidity

2012-06-14T22:19:41Z

DPugh: /* The model */

== Project objective==
The best way to think about money is not to think about money. Do not want to assume what we are trying to explain (i.e., we do not want to code "money" into the model!). With these caveats in mind, the objective of this project is to build an ABM demonstrating the emergence of money that builds off of the previous models of [http://en.wikipedia.org/wiki/Nobuhiro_Kiyotaki Nobu Kiyotaki], [http://en.wikipedia.org/wiki/John_Hardman_Moore John Moore], and [http://en.wikipedia.org/wiki/Randall_Wright Randall Wright]. All of these models take a broad view of money: "money" is any asset which is widely accepted as a medium of exchange (i.e., is highly "liquid").

== Motivating example ==
The following is taken from Kiyotaki and Moore (2001). Suppose I need to get my teeth cleaned. To get my teeth cleaned, I can just go to a dentist who will clean my teeth in exchange for payment. Assume that both the dentist and I have bank accounts, and for simplicity assume that both account balances are initially zero. When I pay for my teeth cleaning using a debit card, funds are immediately transferred from my account to the dentist's. Now my account has a negative balance (i.e., I have issued an IOU to the bank); and the dentist's account has a positive balance (i.e., the bank has issued an IOU to the dentist).

* Question: Instead of using my debit card, why didn't I simply issue one of my own IOUs to the dentist as payment? In other words, why does the bank need to intermediate the transaction between myself and the dentist? Simple answer is that the dentist doesn't trust me enough to repay the debt. In other words my ability to credibly commit to repaying the debt is imperfect. Let 0 < θ ≤ 1 be a measure of my ability to commit with a particular agent (i.e., θ is a measure of ''bilateral commitment''). There is a more subtle answer. The dentist may trust me to repay (particularly as she could threaten to do something nasty to my teeth the next time I needed them cleaned), but perhaps no one ''else'' trusts me (i.e., the dentist is unable to use my IOU for her own purchases). Let 0 < φ ≤ 1 be a measure of my ability to commit with all other agents (i.e., φ is a measure of ''multilateral commitment'').

Note that if the dentist has the ability to perfectly commit with a particular agent (i.e., the dentist's θ=1), then my general lack of trustworthiness (i.e., the fact that both my θ and φ < 1) wouldn't matter. The dentist would be willing to accept my less than perfect IOU because she can simply issue her own IOUs anytime she wants to purchase something.

Suppose that no one else trusts the dentist either. In this case, the dentist can not issue her own IOUs (nor can she endorse my IOUs). Thus the only way the dentist can make purchases before the maturity date of my IOU is if she is paid with a bank IOU (and also, in return, the bank holds my IOU). The dentist gets more benefit from being paid with a bank IOU compared with my IOU. In the language of economics: my debt and bank debt are imperfect substitutes.

The bank’s IOU is used by me and the dentist to lubricate our transaction. Why? Because the bank’s IOU can freely circulate around the economy. Like blood, it is liquid. In fact, the bank's IOU is functionally equivalent to cash. But, unlike cash, it doesn’t come from outside the private system, it comes from inside. For this reason, bank debt is called "inside money". Quantitatively, "inside" money dwarfs "outside" (i.e., cash/coin or currency) money by more than almost 2 orders of magnitude.

== What is money? ==
Wikipedia's entry for [http://en.wikipedia.org/wiki/Money money] is a pretty good place to start.

'''Distinction between "inside" and "outside" money:''' When most people think of money, they have in mind cash/coin (i.e., currency) issued by the government. Currency is called "outside" money precisely because it is issued by the government which is by definition "outside" the private economic system. However, there can be many other forms of money that are created within the private economic system, such forms of money are called "inside" money. Richard Lagos from the Minneapolis FED has a nice short [http://www.minneapolisfed.org/research/sr/sr374.pdf paper] on the distinction between the two.

'''==First paper money in the world== by Jianfeng Xu'''
The first paper money “jiaozi” appeared in Sichuan province of China at 10th century. Facing the problem of limited supply of copper to make coins, varies of paper certificates issued by private businesses were circulating in the market. Local official regulated these “IOU” by limiting the issuers to 16 richest families and setting a 2 year limit to cash out or renew these “jiaozi”. (This is inside money). Finally government could not resist stepping in and printing state-issued paper money. Inflation happened when government printed money to cover over spending. Therefore, paper money lose credit and never been widely used until early 1900s. (This is outside money).

== What does money do? ==
I think of a market economy as being a (quite sophisticated) de-centralized optimization algorithm that maximizes the size of the "economic pie" (think output, GDP, etc) subject to resource constraints. Because agents in the economy have only local information about their economic environment, in order for the "algorithm" to work there needs to exist some mechanism for passing information about the relative scarcity of resources between agents. In market economies, prices are the mechanism for transmitting such information. In a way, prices are like the economy's central nervous system in that prices signal the needs of various parts of the economic body. Flow of money and private securities through the economy is analogous to the flow of blood: "money" dispatches resources to different parts of the economic body in response to price signals.

== The model ==
''' Consumption rule:''' Turtles are assumed to consume a fixed fraction, 1 - β (where 0 < β < 1 is a turtle's discount factor), of their start of period net-worth each period.

'''Movement rule:''' Movement is sequential across agents (the order of movement is randomized each period in order to eliminate any type of "first mover" advantage). If the entrepreneur sees available investment project(s) within his field of vision, he will move to the patch containing the project with the highest amount of resources (i.e., collateral). If there are no available investment projects within an entrepreneur's neighborhood, entrepreneurs are assumed to move to the un-occupied patch within their neighborhood that contains the maximal amount of resources.

''' Investment opportunities:''' Investment projects are created on a given patch in period t=0 with some exogenous probability, &pi (investment opportunities are what generate potential demand for borrowing). Investment projects are assumed to require an entrepreneur in order to become active. Basic idea: from time to time, entrepreneurs will encounter an investment opportunity (i.e., they will move to a patch that houses an investment opportunity); investment projects require some fixed number of periods, say T, to complete and once started the entrepreneur managing the project is required to remain fixed on his patch (implicitly we assume that the project cannot continue without the entrepreneur's specific supervision).

'''Production Technology:''' Each agent is assumed to have access to an identical constant returns to scale production technology. Specifically model assumes that output is a function of capital:

yt + T = akt, where a > 1.

Note that output takes T periods to produce!

'''Finance:''' The scale of the project (i.e., the amount of ''y'' produced) will depend on the amount of capital available at the time the project is started, and the amount of capital depends on the amount of finance that the turtle can raise. A turtle with an investment opportunity has several options for finance:
* Self-finance: the turtle may invest his own funds in the project. If turtle has been a lender in the past, he may have assets (i.e., IOUs from other turtles) that he could sell. Additionally, turtle will also have savings (i.e., grain that has been collected but not consumed).
* Borrowed funds: the turtle may borrow funds from any other turtle (within his vision!) that does not have an investment project. Borrowing is subject to a collateral constraint which depends on the turtle's ability to commit bilaterally (i.e., turtle's θ) and on the inherent quality of the patch on which the project is being undertaken. Specifically, suppose that the patch has a ''max-grain-here'' =''G,'' then the turtle can borrow at most ''θG''.

When deciding whether or not to borrow, the turtle ''i'' compares his marginal benefit from borrowing an additional unit with the marginal cost of borrowing from turtle ''j'' and will want to borrow iff:

βiT(1 - θi)a ≥ Rj

The lending turtle ''j'' meanwhile will only be willing to lend iff the marginal benefit from lending exceeds his opportunity cost:

βjTθiRj ≥ max{1, Rk}

Note that the lender's opportunity cost allows for the possibility that he may have other turtles wishing to borrow funds from him! These two inequalities will pin down an interval in which the interest rate must fall if a bi-lateral agreement can be struck between turtle's ''i'' and ''j.'' Also, although a turtle who is managing a project must remained fixed on his patch, a lending turtle can move freely.

== Background reading ==
* [http://www.princeton.edu/~kiyotaki/papers/Evilistherootofallmoney.pdf Evil is the Root of all Money]: 2001 Clarendon Lecture given on money emerging as a solution to a bi-lateral and multi-lateral contracting problem. Origins of the θ - φ model of money.
* [http://www.princeton.edu/~kiyotaki/papers/Financial-Deepening.pdf Financial Deepening]: Gives a more succinct treatment of the θ - φ model of money.
* [https://pantherfile.uwm.edu/vesely/www/831/Kiyotaki%201989%20JPE.pdf On Money as a Medium of Exchange:] Another well known model of model in the economics literature that uses a [http://en.wikipedia.org/wiki/Search_theory search-theoretic] framework.
* Add a relevant paper...

Emergence of Money and Liquidity

2012-06-14T22:18:53Z

DPugh: /* The model */

== Project objective==
The best way to think about money is not to think about money. Do not want to assume what we are trying to explain (i.e., we do not want to code "money" into the model!). With these caveats in mind, the objective of this project is to build an ABM demonstrating the emergence of money that builds off of the previous models of [http://en.wikipedia.org/wiki/Nobuhiro_Kiyotaki Nobu Kiyotaki], [http://en.wikipedia.org/wiki/John_Hardman_Moore John Moore], and [http://en.wikipedia.org/wiki/Randall_Wright Randall Wright]. All of these models take a broad view of money: "money" is any asset which is widely accepted as a medium of exchange (i.e., is highly "liquid").

== Motivating example ==
The following is taken from Kiyotaki and Moore (2001). Suppose I need to get my teeth cleaned. To get my teeth cleaned, I can just go to a dentist who will clean my teeth in exchange for payment. Assume that both the dentist and I have bank accounts, and for simplicity assume that both account balances are initially zero. When I pay for my teeth cleaning using a debit card, funds are immediately transferred from my account to the dentist's. Now my account has a negative balance (i.e., I have issued an IOU to the bank); and the dentist's account has a positive balance (i.e., the bank has issued an IOU to the dentist).

* Question: Instead of using my debit card, why didn't I simply issue one of my own IOUs to the dentist as payment? In other words, why does the bank need to intermediate the transaction between myself and the dentist? Simple answer is that the dentist doesn't trust me enough to repay the debt. In other words my ability to credibly commit to repaying the debt is imperfect. Let 0 < θ ≤ 1 be a measure of my ability to commit with a particular agent (i.e., θ is a measure of ''bilateral commitment''). There is a more subtle answer. The dentist may trust me to repay (particularly as she could threaten to do something nasty to my teeth the next time I needed them cleaned), but perhaps no one ''else'' trusts me (i.e., the dentist is unable to use my IOU for her own purchases). Let 0 < φ ≤ 1 be a measure of my ability to commit with all other agents (i.e., φ is a measure of ''multilateral commitment'').

Note that if the dentist has the ability to perfectly commit with a particular agent (i.e., the dentist's θ=1), then my general lack of trustworthiness (i.e., the fact that both my θ and φ < 1) wouldn't matter. The dentist would be willing to accept my less than perfect IOU because she can simply issue her own IOUs anytime she wants to purchase something.

Suppose that no one else trusts the dentist either. In this case, the dentist can not issue her own IOUs (nor can she endorse my IOUs). Thus the only way the dentist can make purchases before the maturity date of my IOU is if she is paid with a bank IOU (and also, in return, the bank holds my IOU). The dentist gets more benefit from being paid with a bank IOU compared with my IOU. In the language of economics: my debt and bank debt are imperfect substitutes.

The bank’s IOU is used by me and the dentist to lubricate our transaction. Why? Because the bank’s IOU can freely circulate around the economy. Like blood, it is liquid. In fact, the bank's IOU is functionally equivalent to cash. But, unlike cash, it doesn’t come from outside the private system, it comes from inside. For this reason, bank debt is called "inside money". Quantitatively, "inside" money dwarfs "outside" (i.e., cash/coin or currency) money by more than almost 2 orders of magnitude.

== What is money? ==
Wikipedia's entry for [http://en.wikipedia.org/wiki/Money money] is a pretty good place to start.

'''Distinction between "inside" and "outside" money:''' When most people think of money, they have in mind cash/coin (i.e., currency) issued by the government. Currency is called "outside" money precisely because it is issued by the government which is by definition "outside" the private economic system. However, there can be many other forms of money that are created within the private economic system, such forms of money are called "inside" money. Richard Lagos from the Minneapolis FED has a nice short [http://www.minneapolisfed.org/research/sr/sr374.pdf paper] on the distinction between the two.

'''==First paper money in the world== by Jianfeng Xu'''
The first paper money “jiaozi” appeared in Sichuan province of China at 10th century. Facing the problem of limited supply of copper to make coins, varies of paper certificates issued by private businesses were circulating in the market. Local official regulated these “IOU” by limiting the issuers to 16 richest families and setting a 2 year limit to cash out or renew these “jiaozi”. (This is inside money). Finally government could not resist stepping in and printing state-issued paper money. Inflation happened when government printed money to cover over spending. Therefore, paper money lose credit and never been widely used until early 1900s. (This is outside money).

== What does money do? ==
I think of a market economy as being a (quite sophisticated) de-centralized optimization algorithm that maximizes the size of the "economic pie" (think output, GDP, etc) subject to resource constraints. Because agents in the economy have only local information about their economic environment, in order for the "algorithm" to work there needs to exist some mechanism for passing information about the relative scarcity of resources between agents. In market economies, prices are the mechanism for transmitting such information. In a way, prices are like the economy's central nervous system in that prices signal the needs of various parts of the economic body. Flow of money and private securities through the economy is analogous to the flow of blood: "money" dispatches resources to different parts of the economic body in response to price signals.

== The model ==
''' Consumption rule:''' Turtles are assumed to consume a fixed fraction, 1 - β (where 0 < β < 1 is a turtle's discount factor), of their start of period net-worth each period.

'''Movement rule:''' Movement is sequential across agents (the order of movement is randomized each period in order to eliminate any type of "first mover" advantage). If the entrepreneur sees available investment project(s) within his field of vision, he will move to the patch containing the project with the highest amount of resources (i.e., collateral). If there are no available investment projects within an entrepreneurs Entrepreneurs are assumed to move to the un-occupied patch within their neighborhood that contains the maximal amount of resources.

''' Investment opportunities:''' Investment projects are created on a given patch in period t=0 with some exogenous probability, &pi (investment opportunities are what generate potential demand for borrowing). Investment projects are assumed to require an entrepreneur in order to become active. Basic idea: from time to time, entrepreneurs will encounter an investment opportunity (i.e., they will move to a patch that houses an investment opportunity); investment projects require some fixed number of periods, say T, to complete and once started the entrepreneur managing the project is required to remain fixed on his patch (implicitly we assume that the project cannot continue without the entrepreneur's specific supervision).

'''Production Technology:''' Each agent is assumed to have access to an identical constant returns to scale production technology. Specifically model assumes that output is a function of capital:

yt + T = akt, where a > 1.

Note that output takes T periods to produce!

'''Finance:''' The scale of the project (i.e., the amount of ''y'' produced) will depend on the amount of capital available at the time the project is started, and the amount of capital depends on the amount of finance that the turtle can raise. A turtle with an investment opportunity has several options for finance:
* Self-finance: the turtle may invest his own funds in the project. If turtle has been a lender in the past, he may have assets (i.e., IOUs from other turtles) that he could sell. Additionally, turtle will also have savings (i.e., grain that has been collected but not consumed).
* Borrowed funds: the turtle may borrow funds from any other turtle (within his vision!) that does not have an investment project. Borrowing is subject to a collateral constraint which depends on the turtle's ability to commit bilaterally (i.e., turtle's θ) and on the inherent quality of the patch on which the project is being undertaken. Specifically, suppose that the patch has a ''max-grain-here'' =''G,'' then the turtle can borrow at most ''θG''.

When deciding whether or not to borrow, the turtle ''i'' compares his marginal benefit from borrowing an additional unit with the marginal cost of borrowing from turtle ''j'' and will want to borrow iff:

βiT(1 - θi)a ≥ Rj

The lending turtle ''j'' meanwhile will only be willing to lend iff the marginal benefit from lending exceeds his opportunity cost:

βjTθiRj ≥ max{1, Rk}

Note that the lender's opportunity cost allows for the possibility that he may have other turtles wishing to borrow funds from him! These two inequalities will pin down an interval in which the interest rate must fall if a bi-lateral agreement can be struck between turtle's ''i'' and ''j.'' Also, although a turtle who is managing a project must remained fixed on his patch, a lending turtle can move freely.

== Background reading ==
* [http://www.princeton.edu/~kiyotaki/papers/Evilistherootofallmoney.pdf Evil is the Root of all Money]: 2001 Clarendon Lecture given on money emerging as a solution to a bi-lateral and multi-lateral contracting problem. Origins of the θ - φ model of money.
* [http://www.princeton.edu/~kiyotaki/papers/Financial-Deepening.pdf Financial Deepening]: Gives a more succinct treatment of the θ - φ model of money.
* [https://pantherfile.uwm.edu/vesely/www/831/Kiyotaki%201989%20JPE.pdf On Money as a Medium of Exchange:] Another well known model of model in the economics literature that uses a [http://en.wikipedia.org/wiki/Search_theory search-theoretic] framework.
* Add a relevant paper...

Emergence of Money and Liquidity

2012-06-13T19:26:40Z

DPugh: /* 12 June 2012 */

== Project objective==
The best way to think about money is not to think about money. Do not want to assume what we are trying to explain (i.e., we do not want to code "money" into the model!). With these caveats in mind, the objective of this project is to build an ABM demonstrating the emergence of money that builds off of the previous models of [http://en.wikipedia.org/wiki/Nobuhiro_Kiyotaki Nobu Kiyotaki], [http://en.wikipedia.org/wiki/John_Hardman_Moore John Moore], and [http://en.wikipedia.org/wiki/Randall_Wright Randall Wright]. All of these models take a broad view of money: "money" is any asset which is widely accepted as a medium of exchange (i.e., is highly "liquid").

== Motivating example ==
The following is taken from Kiyotaki and Moore (2001). Suppose I need to get my teeth cleaned. To get my teeth cleaned, I can just go to a dentist who will clean my teeth in exchange for payment. Assume that both the dentist and I have bank accounts, and for simplicity assume that both account balances are initially zero. When I pay for my teeth cleaning using a debit card, funds are immediately transferred from my account to the dentist's. Now my account has a negative balance (i.e., I have issued an IOU to the bank); and the dentist's account has a positive balance (i.e., the bank has issued an IOU to the dentist).

* Question: Instead of using my debit card, why didn't I simply issue one of my own IOUs to the dentist as payment? In other words, why does the bank need to intermediate the transaction between myself and the dentist? Simple answer is that the dentist doesn't trust me enough to repay the debt. In other words my ability to credibly commit to repaying the debt is imperfect. Let 0 < θ ≤ 1 be a measure of my ability to commit with a particular agent (i.e., θ is a measure of ''bilateral commitment''). There is a more subtle answer. The dentist may trust me to repay (particularly as she could threaten to do something nasty to my teeth the next time I needed them cleaned), but perhaps no one ''else'' trusts me (i.e., the dentist is unable to use my IOU for her own purchases). Let 0 < φ ≤ 1 be a measure of my ability to commit with all other agents (i.e., φ is a measure of ''multilateral commitment'').

Note that if the dentist has the ability to perfectly commit with a particular agent (i.e., the dentist's θ=1), then my general lack of trustworthiness (i.e., the fact that both my θ and φ < 1) wouldn't matter. The dentist would be willing to accept my less than perfect IOU because she can simply issue her own IOUs anytime she wants to purchase something.

Suppose that no one else trusts the dentist either. In this case, the dentist can not issue her own IOUs (nor can she endorse my IOUs). Thus the only way the dentist can make purchases before the maturity date of my IOU is if she is paid with a bank IOU (and also, in return, the bank holds my IOU). The dentist gets more benefit from being paid with a bank IOU compared with my IOU. In the language of economics: my debt and bank debt are imperfect substitutes.

The bank’s IOU is used by me and the dentist to lubricate our transaction. Why? Because the bank’s IOU can freely circulate around the economy. Like blood, it is liquid. In fact, the bank's IOU is functionally equivalent to cash. But, unlike cash, it doesn’t come from outside the private system, it comes from inside. For this reason, bank debt is called "inside money". Quantitatively, "inside" money dwarfs "outside" (i.e., cash/coin or currency) money by more than almost 2 orders of magnitude.

== What is money? ==
Wikipedia's entry for [http://en.wikipedia.org/wiki/Money money] is a pretty good place to start.

'''Distinction between "inside" and "outside" money:''' When most people think of money, they have in mind cash/coin (i.e., currency) issued by the government. Currency is called "outside" money precisely because it is issued by the government which is by definition "outside" the private economic system. However, there can be many other forms of money that are created within the private economic system, such forms of money are called "inside" money. Richard Lagos from the Minneapolis FED has a nice short [http://www.minneapolisfed.org/research/sr/sr374.pdf paper] on the distinction between the two.

'''==First paper money in the world== by Jianfeng Xu'''
The first paper money “jiaozi” appeared in Sichuan province of China at 10th century. Facing the problem of limited supply of copper to make coins, varies of paper certificates issued by private businesses were circulating in the market. Local official regulated these “IOU” by limiting the issuers to 16 richest families and setting a 2 year limit to cash out or renew these “jiaozi”. (This is inside money). Finally government could not resist stepping in and printing state-issued paper money. Inflation happened when government printed money to cover over spending. Therefore, paper money lose credit and never been widely used until early 1900s. (This is outside money).

== What does money do? ==
I think of a market economy as being a (quite sophisticated) de-centralized optimization algorithm that maximizes the size of the "economic pie" (think output, GDP, etc) subject to resource constraints. Because agents in the economy have only local information about their economic environment, in order for the "algorithm" to work there needs to exist some mechanism for passing information about the relative scarcity of resources between agents. In market economies, prices are the mechanism for transmitting such information. In a way, prices are like the economy's central nervous system in that prices signal the needs of various parts of the economic body. Flow of money and private securities through the economy is analogous to the flow of blood: "money" dispatches resources to different parts of the economic body in response to price signals.

== The model ==
''' Consumption rule:''' Turtles are assumed to consume a fixed fraction, 1 - β (where 0 < β < 1 is a turtle's discount factor), of their start of period net-worth each period.

'''Movement rule:''' Movement is sequential across agents (the order of movement is randomized each period in order to eliminate any type of "first mover" advantage). Turtles are assumed to move to the un-occupied patch within their neighborhood that contains the maximal amount of resources.

''' Investment opportunities:''' The model assumes patches become "ripe" for investment with exogenous probability, π which is assumed to be iid across both patches and time. This implies that from time to time, turtles will encounter an investment opportunity (i.e., they will move to a patch that is "ripe" for investment); investment opportunities are what generate potential demand for borrowing. Specifically, investment projects are assumed to be ephemeral in the sense that if a patch is "ripe" for investment in a given period, but there is no turtle on the patch to cultivate that investment project then it disappears. Investment projects require some fixed number of periods, say T, to complete and once started the turtle managing the project is required to remain fixed on his patch (implicitly we assume that the project cannot continue without the turtle's specific supervision).

'''Production Technology:''' Each agent is assumed to have access to an identical constant returns to scale production technology. Specifically model assumes that output is a function of capital:

yt + T = akt, where a > 1.

Note that output takes T periods to produce!

'''Finance:''' The scale of the project (i.e., the amount of ''y'' produced) will depend on the amount of capital available at the time the project is started, and the amount of capital depends on the amount of finance that the turtle can raise. A turtle with an investment opportunity has several options for finance:
* Self-finance: the turtle may invest his own funds in the project. If turtle has been a lender in the past, he may have assets (i.e., IOUs from other turtles) that he could sell. Additionally, turtle will also have savings (i.e., grain that has been collected but not consumed).
* Borrowed funds: the turtle may borrow funds from any other turtle (within his vision!) that does not have an investment project. Borrowing is subject to a collateral constraint which depends on the turtle's ability to commit bilaterally (i.e., turtle's θ) and on the inherent quality of the patch on which the project is being undertaken. Specifically, suppose that the patch has a ''max-grain-here'' =''G,'' then the turtle can borrow at most ''θG''.

When deciding whether or not to borrow, the turtle ''i'' compares his marginal benefit from borrowing an additional unit with the marginal cost of borrowing from turtle ''j'' and will want to borrow iff:

βiT(1 - θi)a ≥ Rj

The lending turtle ''j'' meanwhile will only be willing to lend iff the marginal benefit from lending exceeds his opportunity cost:

βjTθiRj ≥ max{1, Rk}

Note that the lender's opportunity cost allows for the possibility that he may have other turtles wishing to borrow funds from him! These two inequalities will pin down an interval in which the interest rate must fall if a bi-lateral agreement can be struck between turtle's ''i'' and ''j.'' Also, although a turtle who is managing a project must remained fixed on his patch, a lending turtle can move freely.

== Background reading ==
* [http://www.princeton.edu/~kiyotaki/papers/Evilistherootofallmoney.pdf Evil is the Root of all Money]: 2001 Clarendon Lecture given on money emerging as a solution to a bi-lateral and multi-lateral contracting problem. Origins of the θ - φ model of money.
* [http://www.princeton.edu/~kiyotaki/papers/Financial-Deepening.pdf Financial Deepening]: Gives a more succinct treatment of the θ - φ model of money.
* [https://pantherfile.uwm.edu/vesely/www/831/Kiyotaki%201989%20JPE.pdf On Money as a Medium of Exchange:] Another well known model of model in the economics literature that uses a [http://en.wikipedia.org/wiki/Search_theory search-theoretic] framework.
* Add a relevant paper...

Emergence of Money and Liquidity

2012-06-13T05:32:16Z

DPugh: /* 12 June 2012 */

== Project objective==
The best way to think about money is not to think about money. Do not want to assume what we are trying to explain (i.e., we do not want to code "money" into the model!). With these caveats in mind, the objective of this project is to build an ABM demonstrating the emergence of money that builds off of the previous models of [http://en.wikipedia.org/wiki/Nobuhiro_Kiyotaki Nobu Kiyotaki], [http://en.wikipedia.org/wiki/John_Hardman_Moore John Moore], and [http://en.wikipedia.org/wiki/Randall_Wright Randall Wright]. All of these models take a broad view of money: "money" is any asset which is widely accepted as a medium of exchange (i.e., is highly "liquid").

== Motivating example ==
The following is taken from Kiyotaki and Moore (2001). Suppose I need to get my teeth cleaned. To get my teeth cleaned, I can just go to a dentist who will clean my teeth in exchange for payment. Assume that both the dentist and I have bank accounts, and for simplicity assume that both account balances are initially zero. When I pay for my teeth cleaning using a debit card, funds are immediately transferred from my account to the dentist's. Now my account has a negative balance (i.e., I have issued an IOU to the bank); and the dentist's account has a positive balance (i.e., the bank has issued an IOU to the dentist).

* Question: Instead of using my debit card, why didn't I simply issue one of my own IOUs to the dentist as payment? In other words, why does the bank need to intermediate the transaction between myself and the dentist? Simple answer is that the dentist doesn't trust me enough to repay the debt. In other words my ability to credibly commit to repaying the debt is imperfect. Let 0 < θ ≤ 1 be a measure of my ability to commit with a particular agent (i.e., θ is a measure of ''bilateral commitment''). There is a more subtle answer. The dentist may trust me to repay (particularly as she could threaten to do something nasty to my teeth the next time I needed them cleaned), but perhaps no one ''else'' trusts me (i.e., the dentist is unable to use my IOU for her own purchases). Let 0 < φ ≤ 1 be a measure of my ability to commit with all other agents (i.e., φ is a measure of ''multilateral commitment'').

Note that if the dentist has the ability to perfectly commit with a particular agent (i.e., the dentist's θ=1), then my general lack of trustworthiness (i.e., the fact that both my θ and φ < 1) wouldn't matter. The dentist would be willing to accept my less than perfect IOU because she can simply issue her own IOUs anytime she wants to purchase something.

Suppose that no one else trusts the dentist either. In this case, the dentist can not issue her own IOUs (nor can she endorse my IOUs). Thus the only way the dentist can make purchases before the maturity date of my IOU is if she is paid with a bank IOU (and also, in return, the bank holds my IOU). The dentist gets more benefit from being paid with a bank IOU compared with my IOU. In the language of economics: my debt and bank debt are imperfect substitutes.

The bank’s IOU is used by me and the dentist to lubricate our transaction. Why? Because the bank’s IOU can freely circulate around the economy. Like blood, it is liquid. In fact, the bank's IOU is functionally equivalent to cash. But, unlike cash, it doesn’t come from outside the private system, it comes from inside. For this reason, bank debt is called "inside money". Quantitatively, "inside" money dwarfs "outside" (i.e., cash/coin or currency) money by more than almost 2 orders of magnitude.

== What is money? ==
Wikipedia's entry for [http://en.wikipedia.org/wiki/Money money] is a pretty good place to start.

'''Distinction between "inside" and "outside" money:''' When most people think of money, they have in mind cash/coin (i.e., currency) issued by the government. Currency is called "outside" money precisely because it is issued by the government which is by definition "outside" the private economic system. However, there can be many other forms of money that are created within the private economic system, such forms of money are called "inside" money. Richard Lagos from the Minneapolis FED has a nice short [http://www.minneapolisfed.org/research/sr/sr374.pdf paper] on the distinction between the two.

== What does money do? ==
I think of a market economy as being a (quite sophisticated) de-centralized optimization algorithm that maximizes the size of the "economic pie" (think output, GDP, etc) subject to resource constraints. Because agents in the economy have only local information about their economic environment, in order for the "algorithm" to work there needs to exist some mechanism for passing information about the relative scarcity of resources between agents. In market economies, prices are the mechanism for transmitting such information. In a way, prices are like the economy's central nervous system in that prices signal the needs of various parts of the economic body. Flow of money and private securities through the economy is analogous to the flow of blood: "money" dispatches resources to different parts of the economic body in response to price signals.

== 12 June 2012 ==
The following are notes from group discussion.
''' Consumption rule:''' Turtles are assumed to consume a fixed fraction, 1 - β (where 0 < β < 1 is a turtle's discount factor), of their start of period net-worth each period.

''' Investment opportunities:''' From time to time, turtles will encounter an investment opportunity (which is what will generate the demand for borrowing). Specifically, the model will assume that a turtle encounters an investment opportunity with exogenous probability, π which is assumed to be iid across both turtles and time. Investment projects require some fixed number of periods, say N, to complete and once started the turtle managing the project is required to remain fixed on his patch (implicitly we assume that the project cannot continue without the turtle's specific supervision).

'''Production Technology:''' Each agent is assumed to have access to an identical constant returns to scale production technology. Specifically model assumes that output is a function of capital: ''y = ak'', where ''a > 1.'' Note that ''y'' takes N periods to produce!

'''Finance:''' The scale of the project (i.e., the amount of ''y'' produced) will depend on the amount of capital available at the time the project is started, and the amount of capital depends on the amount of finance that the turtle can raise. A turtle with an investment opportunity has several options for finance:
* Self-finance: the turtle may invest his own funds in the project. If turtle has been a lender in the past, he may have assets (i.e., IOUs from other turtles) that he could sell. Additionally, turtle will also have savings (i.e., grain that has been collected but not consumed).
* Borrowed funds: the turtle may borrow funds from any other turtle (within his vision!) that does not have an investment project. Borrowing is subject to a collateral constraint which depends on the turtle's ability to commit bilaterally (i.e., turtle's θ) and on the inherent quality of the patch on which the project is being undertaken. Specifically, suppose that the patch has a ''max-grain-here'' =''G,'' then the turtle can borrow at most ''θG''.

When deciding whether or not to borrow, the turtle ''i'' compares his marginal benefit from borrowing an additional unit with the marginal cost of borrowing from turtle ''j'' and will want to borrow iff:

βiN(1 - θi)a ≥ Rj

The lending turtle ''j'' meanwhile will only be willing to lend iff the marginal benefit from lending exceeds his opportunity cost:

βjNθiRj ≥ max{1, Rk}

Note that the lender's opportunity cost allows for the possibility that he may have other turtles wishing to borrow funds from him! These two inequalities will pin down an interval in which the interest rate must fall if a bi-lateral agreement can be struck between turtle's ''i'' and ''j.'' Also, although a turtle who is managing a project must remained fixed on his patch, a lending turtle can move freely.

== Background reading ==
* [http://www.princeton.edu/~kiyotaki/papers/Evilistherootofallmoney.pdf Evil is the Root of all Money]: 2001 Clarendon Lecture given on money emerging as a solution to a bi-lateral and multi-lateral contracting problem. Origins of the θ - φ model of money.
* [http://www.princeton.edu/~kiyotaki/papers/Financial-Deepening.pdf Financial Deepening]: Gives a more succinct treatment of the θ - φ model of money.
* [https://pantherfile.uwm.edu/vesely/www/831/Kiyotaki%201989%20JPE.pdf On Money as a Medium of Exchange:] Another well known model of model in the economics literature that uses a [http://en.wikipedia.org/wiki/Search_theory search-theoretic] framework.
* Add a relevant paper...

Emergence of Money and Liquidity

2012-06-13T05:31:55Z

DPugh:

== Project objective==
The best way to think about money is not to think about money. Do not want to assume what we are trying to explain (i.e., we do not want to code "money" into the model!). With these caveats in mind, the objective of this project is to build an ABM demonstrating the emergence of money that builds off of the previous models of [http://en.wikipedia.org/wiki/Nobuhiro_Kiyotaki Nobu Kiyotaki], [http://en.wikipedia.org/wiki/John_Hardman_Moore John Moore], and [http://en.wikipedia.org/wiki/Randall_Wright Randall Wright]. All of these models take a broad view of money: "money" is any asset which is widely accepted as a medium of exchange (i.e., is highly "liquid").

== Motivating example ==
The following is taken from Kiyotaki and Moore (2001). Suppose I need to get my teeth cleaned. To get my teeth cleaned, I can just go to a dentist who will clean my teeth in exchange for payment. Assume that both the dentist and I have bank accounts, and for simplicity assume that both account balances are initially zero. When I pay for my teeth cleaning using a debit card, funds are immediately transferred from my account to the dentist's. Now my account has a negative balance (i.e., I have issued an IOU to the bank); and the dentist's account has a positive balance (i.e., the bank has issued an IOU to the dentist).

* Question: Instead of using my debit card, why didn't I simply issue one of my own IOUs to the dentist as payment? In other words, why does the bank need to intermediate the transaction between myself and the dentist? Simple answer is that the dentist doesn't trust me enough to repay the debt. In other words my ability to credibly commit to repaying the debt is imperfect. Let 0 < θ ≤ 1 be a measure of my ability to commit with a particular agent (i.e., θ is a measure of ''bilateral commitment''). There is a more subtle answer. The dentist may trust me to repay (particularly as she could threaten to do something nasty to my teeth the next time I needed them cleaned), but perhaps no one ''else'' trusts me (i.e., the dentist is unable to use my IOU for her own purchases). Let 0 < φ ≤ 1 be a measure of my ability to commit with all other agents (i.e., φ is a measure of ''multilateral commitment'').

Note that if the dentist has the ability to perfectly commit with a particular agent (i.e., the dentist's θ=1), then my general lack of trustworthiness (i.e., the fact that both my θ and φ < 1) wouldn't matter. The dentist would be willing to accept my less than perfect IOU because she can simply issue her own IOUs anytime she wants to purchase something.

Suppose that no one else trusts the dentist either. In this case, the dentist can not issue her own IOUs (nor can she endorse my IOUs). Thus the only way the dentist can make purchases before the maturity date of my IOU is if she is paid with a bank IOU (and also, in return, the bank holds my IOU). The dentist gets more benefit from being paid with a bank IOU compared with my IOU. In the language of economics: my debt and bank debt are imperfect substitutes.

The bank’s IOU is used by me and the dentist to lubricate our transaction. Why? Because the bank’s IOU can freely circulate around the economy. Like blood, it is liquid. In fact, the bank's IOU is functionally equivalent to cash. But, unlike cash, it doesn’t come from outside the private system, it comes from inside. For this reason, bank debt is called "inside money". Quantitatively, "inside" money dwarfs "outside" (i.e., cash/coin or currency) money by more than almost 2 orders of magnitude.

== What is money? ==
Wikipedia's entry for [http://en.wikipedia.org/wiki/Money money] is a pretty good place to start.

'''Distinction between "inside" and "outside" money:''' When most people think of money, they have in mind cash/coin (i.e., currency) issued by the government. Currency is called "outside" money precisely because it is issued by the government which is by definition "outside" the private economic system. However, there can be many other forms of money that are created within the private economic system, such forms of money are called "inside" money. Richard Lagos from the Minneapolis FED has a nice short [http://www.minneapolisfed.org/research/sr/sr374.pdf paper] on the distinction between the two.

== What does money do? ==
I think of a market economy as being a (quite sophisticated) de-centralized optimization algorithm that maximizes the size of the "economic pie" (think output, GDP, etc) subject to resource constraints. Because agents in the economy have only local information about their economic environment, in order for the "algorithm" to work there needs to exist some mechanism for passing information about the relative scarcity of resources between agents. In market economies, prices are the mechanism for transmitting such information. In a way, prices are like the economy's central nervous system in that prices signal the needs of various parts of the economic body. Flow of money and private securities through the economy is analogous to the flow of blood: "money" dispatches resources to different parts of the economic body in response to price signals.

== 12 June 2012 ==
The following are notes from group discussion.
''' Consumption rule:''' Turtles are assumed to consume a fixed fraction, 1 - β (where 0 < β < 1 is a turtle's discount factor), of their start of period net-worth each period.

''' Investment opportunities:''' From time to time, turtles will encounter an investment opportunity (which is what will generate the demand for borrowing). Specifically, the model will assume that a turtle encounters an investment opportunity with exogenous probability, π which is assumed to be iid across both turtles and time. Investment projects require some fixed number of periods, say N, to complete and once started the turtle managing the project is required to remain fixed on his patch (implicitly we assume that the project cannot continue without the turtle's specific supervision).

'''Production Technology:''' Each agent is assumed to have access to an identical constant returns to scale production technology. Specifically model assumes that output is a function of capital: ''y = ak'', where ''a > 1.'' Note that ''y'' takes N periods to produce!

'''Finance:''' The scale of the project (i.e., the amount of ''y'' produced) will depend on the amount of capital available at the time the project is started, and the amount of capital depends on the amount of finance that the turtle can raise. A turtle with an investment opportunity has several options for finance:
* Self-finance: the turtle may invest his own funds in the project. If turtle has been a lender in the past, he may have assets (i.e., IOUs from other turtles) that he could sell. Additionally, turtle will also have savings (i.e., grain that has been collected but not consumed).
* Borrowed funds: the turtle may borrow funds from any other turtle (within his vision!) that does not have an investment project. Borrowing is subject to a collateral constraint which depends on the turtle's ability to commit bilaterally (i.e., turtle's θ) and on the inherent quality of the patch on which the project is being undertaken. Specifically, suppose that the patch has a ''max-grain-here'' =''G,'' then the turtle can borrow at most ''θG''.

When deciding whether or not to borrow, the turtle ''i'' compares his marginal benefit from borrowing an additional unit with the marginal cost of borrowing from turtle ''j'' and will want to borrow iff:

βiN(1 - θi)a ≥ Rj

The lending turtle ''j'' meanwhile will only be willing to lend iff the marginal benefit from lending exceeds his opportunity cost:

βjNθiRj ≥ max{1, Rk}

Note that the lender's opportunity cost allows for the possibility that he may have other turtles wishing to borrow funds from him! These two inequalities will pin down an interval in which the interest rate must fall if a bi-lateral agreement can be struck between turtle's ''i'' and ''j.'' Also, although a turtle who is managing a project must remained fixed on his patch, a lending turtle can move freely.

Most recent version of the Netlogo version of the model can be found [[money | here]].

== Background reading ==
* [http://www.princeton.edu/~kiyotaki/papers/Evilistherootofallmoney.pdf Evil is the Root of all Money]: 2001 Clarendon Lecture given on money emerging as a solution to a bi-lateral and multi-lateral contracting problem. Origins of the θ - φ model of money.
* [http://www.princeton.edu/~kiyotaki/papers/Financial-Deepening.pdf Financial Deepening]: Gives a more succinct treatment of the θ - φ model of money.
* [https://pantherfile.uwm.edu/vesely/www/831/Kiyotaki%201989%20JPE.pdf On Money as a Medium of Exchange:] Another well known model of model in the economics literature that uses a [http://en.wikipedia.org/wiki/Search_theory search-theoretic] framework.
* Add a relevant paper...

Emergence of Money and Liquidity

2012-06-10T21:54:28Z

DPugh: Created page with '== Project objective== The best way to think about money is not to think about money. Do not want to assume what we are trying to explain (i.e., we do not want to code "money" i…'

Complex Systems Summer School 2012-Projects & Working Groups

2012-06-10T15:14:36Z

DPugh: /* Systemic Risk in Financial Networks and/or an ABM of money/liquidity */

{{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. Also, here's a [http://posterhall.org/igert2012/posters/218 fun 3-minute video] of the effect I'm personally the most interested in, with a special appearance from The Princess Bride.

=== 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 - 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 [http://tuvalu.santafe.edu/events/workshops/index.php/Thomas_Fennewald 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.

Dan: Here's a really relevant paper I've come across "intervening to achieve cooperative ecosystem management" JASSS http://jasss.soc.surrey.ac.uk/4/2/4.html

=== 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 [at] ias.unu.edu or find me in class. [[Innovation Group Project]]

=== 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.

The [[Spatial-Stochastic]] group is writing up their ideas to share here and look for overlap and coupling.

==== Entrenchment and rhythms ====

One idea that I had was to look at the entrenchment properties of various systems. This is an universal phenomena that arises from nonlinear mechanisms interacting with a fluctuation environment and appears most often in animal and plant physiological rhythms (e.g. circadian rhythms, sleep cycles) and result in predictable oscillations that can also sometimes be forced into stable/unstable states by noise (in the case of humans, this can result in disease). I would like to see if there are any mechanisms that produce similar behavior at the ecosystem level based on structural or species/functional diversity, especially in climates where the energy/water input is non-uniform. The "noise" in this case could be natural or anthropogenic disturbances. I think this can be generalized into many different types of systems. If you have an idea on this, please shoot me an email at xue.feng@duke.edu

=== 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 ===
* '''Project page moved''' to '''[[Robustness of complex networks (project)]]'''

=== Systemic Risk in Financial Networks and/or an ABM of money/liquidity===

'''Systemic Risk in Financial Networks:''' Hypothesis: the motive to diversify risk at the level of the individual agent (i.e., for an agent to increase its connectivity) will increase systemic risk (by systemic risk I mean vulnerability of the system to widespread collapse). Point of departure is the [http://en.wikipedia.org/wiki/Forest-fire_model Forest Fire] model from statistical physics.

Key difference(s) between the physics version of the Forest Fire model, and the "economics" version of the Forest Fire model I have in mind are:
* Tree growth probability (which determines network structure) must be endogenous. Agents must be able to choose which other agents to link with.
* Probability of lightning strikes (i.e., defaults on specific loans) must also be endogenous.

I think that financial networks might exhibit self-organizing criticality in the sense that diversification will reduce the probability of lightning strikes (i.e., defaults), however over time systemic risk builds up as a result of the diversification which means that eventually a small number of lightning strikes might be enough to bring the entire system down.

'''ABM of the emergence of Money:'''
Basically, I would be interested in building an ABM of the emergence of money based around the following economic models of money developed by Nobu Kiyotaki and John Moore:
* [[File:Kiyotaki_and_Moore_(2001).pdf | Evil is the Root of all Money]]
* [[File:Financial-Deepening.pdf | Financial Deepening]]

These models take a broad view of money: "money" is any asset which is widely accepted as a medium of exchange. In these models agents manage projects which require capital investment now in order to generate a return at some point in the future and agents must trade financial promises (think debt contracts) in order to obtain the needed investment. Two key parameters of is these models (which are assumed COMMON to all agents in the above models in order to maintain analytical tractibility) are 0 < theta < 1 and 0 < phi < 1. Theta is the fraction of the future return from the project that an agent can promise to repay in the future in exchange for investment now. Phi is the fraction of the face value of a debt contract (which by construction is a contract between two agents) that can be re-sold to a third agent.

Hypothesis: In an ABM where agents differ in terms of both theta and phi, the promises of only a small number of agents will be widely traded (i.e., will serve as money).

For more information about the project check out the [[Emergence of Money and Liquidity]].

=== Price-time dynamics of contracts traded on prediction markets===
Prediction markets have been shown to outperform traditional methods of polls and opinion surveys in forecasting future events. I am interested in exploring the price-time dynamics of contracts traded on prediction markets to better understand how they are able to aggregate individual opinion to establish collective insight.

Several questions that I’m curious to probe further:
* How do ‘information shocks’ generated by news sources influence price-time trajectories?
* Can features of the underlying dynamics be characterized using a simple model?
* What is the minimum number of traders required for an accurate prediction?
On a separate note, I invite you to share your opinion regards whether “China will win the most medals at the 2012 London Olympics”, by logging into the following [https://csss12.inklingmarkets.com/user/login site] (please send me your email address and I will send you the login details).

If you haven't used a prediction market before don't worry -just follow the instructions provided in the site to 'buy' and 'sell' contracts according to your expectation.

If you are interested in the discussing any of the above questions or have other ideas related to prediction markets please get in touch with me at: sanith at mitre dot org

Useful Links:
* [[File:Predictionmarkets.pdf | Intro to Prediction Markets -Wolfers and Zitzewitz]]
* [[File:InfoMarkets(book).pdf | Prediction Markets -Hahn and Tetlock]]
* [http://pybrain.org Neural networks using Python]

=== Internal models: what do they do and how are they built?===
In the past decade(s) Bayesian statistics has come to dominate empirical science. Consequently, the significance of prior beliefs for guiding inference has become widely accepted. But how do we map the concept of prior beliefs onto natural systems? I argue that the composition of organisms realize internal models of their environment. These internal models manifest as structured behavior, which we scientists describe as reflecting prior beliefs or bias. In humans you have reflexes at one extreme and the influence of memories upon behavior at the other. It is an open question how these internal models are instantiated in biological systems. Are they structural motifs in neural networks? Protein networks within cells? Concepts such as memory, storage, and recall provide relevant bridges between the statistical formulation of these ideas and the physics of computation, but these are jumping off points at best.

My suspicion is that part of the challenge is we don’t have a clear understanding of what benefits internals models impart to organisms beyond some general statement about resolving uncertainty. This is compounded by the fact that we probably wouldn’t recognize an internal model if we saw one. This is why I find work over the past decade upon self-localizing and mapping (SLAM) systems to be so interesting. To my knowledge, these are the first man-made systems designed with the objective of imparting complex internal models to artificial systems. The Mars rover is a SLAM system. The driverless car depends critically upon a SLAM system. The successes, and failures, of these systems have exposed the complexity of functionalities we use so naturally that they evade our notice. These include differentiating between static and dynamic elements of our environment as well as ascribing our sensations to external or internal causes. In the least, the design of these systems offer first order models for what an internal model of non-trivial complexity might look like.

I’m interested in exploring the role of internal models as well as how they are embedded in natural systems. I welcome you to join to me to discuss these ideas in the seminar room after 3 on Wednesday and after 4 on Thursday. Maybe we'll go to the coffee shop if that makes more sense. Feel free to email me at: jlong29@gmail.com (John Long)

=== Rain-Climate-Agriculture Interactions ===

We are trying to think about the influence of different precipitation/climate regimes on farming strategies and crop prices. In places where water is the limiting factor for crop growth, changes in precipitation intensity, or in precipitation patterns are likely to dramatically affect the choices that farmers could make in order to produce more, or simply to survive. How will they react under different regimes? Which are the best strategies? We are meeting tomorrow afternoon (Sunday): if you're interested in it, you're very welcome to join us! fabio.cresto-aleina@zmaw.de and fgreb@uni-goettingen.de

Complex Systems Summer School 2012-Blog

2012-06-07T20:18:23Z

DPugh: /* Dynamical Systems: R and Python */

{{Complex Systems Summer School 2012}}

Use this page as an informal forum to share your opinion and discuss anything at CSSS'12.

Students are encouraged to share their observations, insights, and opinions about daily lecture content as well as extracurricular activities.

Post your own links to notes, interesting articles, and anything else you think might contribute to the program.

== Schooling interactions with non-square boundary ==
The Monterey Bay Aquarium has an [http://www.youtube.com/watch?v=xZKxfvi9h0c [anchovy tank]] shaped like two nested sections of cones (you stand inside it). Boid-like behavior is pretty easy to see in the fish, particularly because the shape of the tank interferes with the school. I couldn't decide if the tank was actually inducing turbulence in the fish.

== Dynamical Systems: R and Python ==
I wrote some Python code for simulating deterministic dynamical systems and used it to play around with the Logistic map from yesterday's lectures. The Python class I wrote for simulating generic dynamical systems (admittedly only those with a single state variable!) can be found [https://sites.google.com/site/beyondmicrofoundationscoderepo/home/python/dynamics here] (I would have posted to the code to the wiki directly but apparently one cannot upload .py files). I have also written a [http://beyondmicrofoundations.blogspot.com/2012/06/monday-4-june-2012-at-2012-csss.html blog] post demonstrating some of the functionality of the Python code.

Also, there are several R packages that seem to implement the algorithms from the TISEAN program that we were introduced to yesterday. The packages are [http://cran.r-project.org/web/packages/RTisean/index.html RTisean], [http://cran.r-project.org/web/packages/tseriesChaos/index.html tseriesChaos], and [http://cran.r-project.org/web/packages/tsDyn/index.html tsDyn] and can be found on CRAN.

Enjoy,

D. Pugh

PS: I am very much a newbie to Python, and any suggestions on improving my coding are much appreciated!

I have written another blog [http://beyondmicrofoundations.blogspot.com/2012/06/5-6-june-2012-at-2012-csss.html post] and some more Python code for implementing parts of the TISEAN labs that we have been assigned. Code can be found here [https://sites.google.com/site/beyondmicrofoundationscoderepo/home/python/dynamics here]. Eventually, the code will contain solutions to the all of the labs and homework.

== Python wrapper for TISEAN ==

Thanks for sharing your python code David.

Incidentally there is also a Python wrapper for TISEAN available
[http://www.stud.fernuni-hagen.de/q4576411/andreas_2/computer/projekte/tisean.html here]. Not sure how good it is, but might be worth checking out.

Sanith

Bandelier Trip 2012

2012-06-07T01:59:45Z

DPugh: /* STILL NEEDS A SEAT! */

{{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

4. Oleksandr Ivanov

5. Ben Althouse

6. Georg Weber

7. Oscar Patterson

8. David Pugh

Complex Systems Summer School 2012-Projects & Working Groups

2012-06-06T13:06:02Z

DPugh: /* Systemic Risk in Financial Networks and/or an ABM of money/liquidity */

{{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.

The [[Spatial-Stochastic]] group is writing up their ideas to share here and look for overlap and coupling.

=== 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|300px|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]].

==== Relevant literature ====
* [http://www.barabasilab.com/pubs/CCNR-ALB_Publications/199910-15_Science-Emergence/199910-15_Science-Emergence.pdf BA Scale-free network]
* [http://people.maths.ox.ac.uk/maini/PKM%20publications/195.pdf How to generate Scale-free modular network using preferential attachment]
* [http://www.barabasilab.com/pubs/CCNR-ALB_Publications/200007-27_Nature-ErrorAttack/200007-27_Nature-ErrorAttack.pdf Error and attack tollerance of complex networks]
* [http://www.cis.upenn.edu/~mkearns/teaching/NetworkedLife/hierarchical.pdf Hierarchical organization in complex networks]
* [http://arxiv.org/pdf/cond-mat/0402009v1.pdf Scale-free networks with tunable degree distribution exponents]
* [http://arxiv.org/pdf/cond-mat/0110452v1.pdf Scale free networks with tunable clustering]
* [http://vw.indiana.edu/netsci06/conference/Ng_Structural.pdf Structural Robustness of Complex networks]
* [http://arxiv.org/pdf/cond-mat/0205405.pdf Assortative mixing in networks]
* [http://www.cis.upenn.edu/~mkearns/teaching/NetworkedLife/prefatt.pdf Mean field theory to study scale-free networks]
* [http://www.graphanalysis.org/SIAM-PP08/Leskovic.pdf Kroneker Graphs]
* [http://www.lem.sssup.it/WPLem/files/2011-07.pdf Exact maximum-likelihood method to detect patterns in real networks]
* Add a relevant paper...

=== Systemic Risk in Financial Networks and/or an ABM of money/liquidity===

'''Systemic Risk in Financial Networks:''' Hypothesis: the motive to diversify risk at the level of the individual agent (i.e., for an agent to increase its connectivity) will increase systemic risk (by systemic risk I mean vulnerability of the system to widespread collapse). Point of departure is the [http://en.wikipedia.org/wiki/Forest-fire_model Forest Fire] model from statistical physics.

Key difference(s) between the physics version of the Forest Fire model, and the "economics" version of the Forest Fire model I have in mind are:
* Tree growth probability (which determines network structure) must be endogenous. Agents must be able to choose which other agents to link with.
* Probability of lightning strikes (i.e., defaults on specific loans) must also be endogenous.

I think that financial networks might exhibit self-organizing criticality in the sense that diversification will reduce the probability of lightning strikes (i.e., defaults), however over time systemic risk builds up as a result of the diversification which means that eventually a small number of lightning strikes might be enough to bring the entire system down.

'''ABM of the emergence of Money:'''
Basically, I would be interested in building an ABM of the emergence of money based around the following economic models of money developed by Nobu Kiyotaki and John Moore:
* [[File:Kiyotaki_and_Moore_(2001).pdf | Evil is the Root of all Money]]
* [[File:Financial-Deepening.pdf | Financial Deepening]]

These models take a broad view of money: "money" is any asset which is widely accepted as a medium of exchange. In these models agents manage projects which require capital investment now in order to generate a return at some point in the future and agents must trade financial promises (think debt contracts) in order to obtain the needed investment. Two key parameters of is these models (which are assumed COMMON to all agents in the above models in order to maintain analytical tractibility) are 0 < theta < 1 and 0 < phi < 1. Theta is the fraction of the future return from the project that an agent can promise to repay in the future in exchange for investment now. Phi is the fraction of the face value of a debt contract (which by construction is a contract between two agents) that can be re-sold to a third agent.

Hypothesis: In an ABM where agents differ in terms of both theta and phi, the promises of only a small number of agents will be widely traded (i.e., will serve as money).

If anyone might be interested in working on these projects, send me and email: drobert.pugh at gmail dot com

=== Price-time dynamics of contracts traded on prediction markets===
Prediction markets have been shown to outperform traditional methods of polls and opinion surveys in forecasting future events. I am interested in exploring the price-time dynamics of contracts traded on prediction markets to better understand how they are able to aggregate individual opinion to establish collective insight.

Several questions that I’m curious to probe further:
* How do ‘information shocks’ generated by news sources influence price-time trajectories?
* Can features of the underlying dynamics be characterized using a simple model?
* What is the minimum number of traders required for an accurate prediction?
On a separate note, I invite you to share your opinion regards whether “China will win the most medals at the 2012 London Olympics”, by logging into the following [https://csss12.inklingmarkets.com/user/login site] (please send me your email address and I will send you the login details).

If you haven't used a prediction market before don't worry -just follow the instructions provided in the site to 'buy' and 'sell' contracts according to your expectation.

If you are interested in the discussing any of the above questions or have other ideas related to prediction markets please get in touch with me at: sanith at mitre dot org

=== Internal models: what do they do and how are they built?===
In the past decade(s) Bayesian statistics has come to dominate empirical science. Consequently, the significance of prior beliefs for guiding inference has become widely accepted. But how do we map the concept of prior beliefs onto natural systems? I argue that the composition of organisms realize internal models of their environment. These internal models manifest as structured behavior, which we scientists describe as reflecting prior beliefs or bias. In humans you have reflexes at one extreme and the influence of memories upon behavior at the other. It is an open question how these internal models are instantiated in biological systems. Are they structural motifs in neural networks? Protein networks within cells? Concepts such as memory, storage, and recall provide relevant bridges between the statistical formulation of these ideas and the physics of computation, but these are jumping off points at best.

My suspicion is that part of the challenge is we don’t have a clear understanding of what benefits internals models impart to organisms beyond some general statement about resolving uncertainty. This is compounded by the fact that we probably wouldn’t recognize an internal model if we saw one. This is why I find work over the past decade upon self-localizing and mapping (SLAM) systems to be so interesting. To my knowledge, these are the first man-made systems designed with the objective of imparting complex internal models to artificial systems. The Mars rover is a SLAM system. The driverless car depends critically upon a SLAM system. The successes, and failures, of these systems have exposed the complexity of functionalities we use so naturally that they evade our notice. These include differentiating between static and dynamic elements of our environment as well as ascribing our sensations to external or internal causes. In the least, the design of these systems offer first order models for what an internal model of non-trivial complexity might look like.

I’m interested in exploring the role of internal models as well as how they are embedded in natural systems. I welcome you to join to me to discuss these ideas in the Cafeteria after 3 on Wednesday and after 4 on Thursday. Feel free to email me at: jlong29@gmail.com (John Long)

File:Kiyotaki and Moore (2001).pdf

2012-06-06T13:03:25Z

DPugh:

File:Financial-Deepening.pdf

2012-06-06T13:02:29Z

DPugh:

Complex Systems Summer School 2012-Projects & Working Groups

2012-06-05T21:51:02Z

DPugh:

{{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]].

=== Systemic Risk in Financial Networks and/or an ABM of money/liquidity===

'''Systemic Risk in Financial Networks:''' Hypothesis: the motive to diversify risk at the level of the individual agent (i.e., for an agent to increase its connectivity) will increase systemic risk (by systemic risk I mean vulnerability of the system to widespread collapse). Point of departure is the [http://en.wikipedia.org/wiki/Forest-fire_model Forest Fire] model from statistical physics.

Key difference(s) between the physics version of the Forest Fire model, and the "economics" version of the Forest Fire model I have in mind are:
* Tree growth probability (which determines network structure) must be endogenous. Agents must be able to choose which other agents to link with.
* Probability of lightning strikes (i.e., defaults on specific loans) must also be endogenous.

I think that financial networks might exhibit self-organizing criticality in the sense that diversification will reduce the probability of lightning strikes (i.e., defaults), however over time systemic risk builds up as a result of the diversification which means that eventually a small number of lightning strikes might be enough to bring the entire system down.

'''ABM of the emergence of Money:'''
Basically, I would be interested in building an ABM of the emergence of money based around the following economic models of money developed by Nobu Kiyotaki and John Moore:
* [www.princeton.edu/~kiyotaki/papers/Evilistherootofallmoney.pdf Evil is the Root of all Money]
* [www.princeton.edu/~kiyotaki/papers/Financial-Deepening.pdf Financial Deepening]

These models take a broad view of money: "money" is any asset which is widely accepted as a medium of exchange. In these models agents manage projects which require capital investment now in order to generate a return at some point in the future and agents must trade financial promises (think debt contracts) in order to obtain the needed investment. Two key parameters of is these models (which are assumed COMMON to all agents in the above models in order to maintain analytical tractibility) are 0 < theta < 1 and 0 < phi < 1. Theta is the fraction of the future return from the project that an agent can promise to repay in the future in exchange for investment now. Phi is the fraction of the face value of a debt contract (which by construction is a contract between two agents) that can be re-sold to a third agent.

Hypothesis: In an ABM where agents differ in terms of both theta and phi, the promises of only a small number of agents will be widely traded (i.e., will serve as money).

If anyone might be interested in working on these projects, send me and email: drobert.pugh at gmail dot com

Complex Systems Summer School 2012-Blog

2012-06-05T15:01:12Z

DPugh: /* Dynamical Systems: R and Python */

Complex Systems Summer School 2012-Blog

2012-06-05T14:59:22Z

DPugh:

Alfred Hubler's Nonlinear Dynamics Lab 2012

2012-06-03T23:19:49Z

DPugh: /* Thursday, June 7, 6:00pm */

{{Complex Systems Summer School 2012}}

==Thursday, June 7, 6:00pm==

1. Sarah Tweedt 
2. Georg F. Weber 
3. Georg M. Goerg 
4. Cameron Smith 
5. Mikkel Vestergaard 
6. Friederike Greb 
7. Fabio Cresto Aleina 
8. Benji zusman 
9. Elena del Val 
10.Riccardo Fusaroli 
11. Nick Allgaier 
12.John Long 
13. Sepehr Ehsani 
14. David Pugh 
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==Friday, June 8 7:00am==
PLEASE NOTE THAT THIS IS AN EARLY MORNING CLASS! 
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==Monday, June 11, 6:00pm==
1. Hidetoshi Inamine 
2. Dan Wu 
3. Xiaoli Dong 
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==Tuesday, June 12, 6:00pm==
1. Marco Duenas

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David Pugh

2012-06-01T15:42:36Z

DPugh:

[[Image:David Pugh.jpg]]

== Who am I? ==
Graduated class of 2005 from the College of William and Mary with a BS in Mathematics. Spent a few years as a professional actor, before "settling down" into a job as a senior research analyst for the Institute for Physical Sciences (IPS). IPS was basically a think-tank that used insights from complexity science to build models of political and economic systems (it was also where I was first learned of the existence of SFI!). I received my MSc in Economics from the University of Edinburgh in 2009. Worked a bit as a senior research analyst for Centra Technologies, before receiving PhD funding to return to the University of Edinburgh in 2010. I am currently in the second year of my PhD.

== Research Interests... ==
I think that financial markets are best thought of as being complex adaptive systems whose agents (i.e., households, firms, traders, investors, financial intermediaries, etc) are engaged in economic activity as part of a inter-connected, dynamic network of financial contracts. Here are a couple of ideas for projects that I would be keen to develop with the help of SFI staff and other CSSS participants:

* Can credit and liquidity constraints generate [http://en.wikipedia.org/wiki/Volatility_clustering volatility clustering]? Basically, I would like to develop a computational model that incorporates key ideas from the financial contracting literature and generates the volatility clustering that we observe in the data.
* Systemic risk in financial networks. Why do financial crises seem to be an [http://www.reinhartandrogoff.com/ endemic] feature of market economies? Do financial markets sow the seeds of their own destruction? In particular, does diversification (a rational risk management strategy for an individual agent) actual increase systemic risk under certain circumstances? My point of departure for answering these questions is a classic model of self-organized criticality, the [http://en.wikipedia.org/wiki/Forest-fire_model forest fire] model.

My modeling strategy is inherently inter-disciplinary in nature and relies heavily on previous work on nonlinear dynamics, machine learning, network theory, and computer modeling and simulation in ''R'' and ''Python''. More info about my research interests can be found on my [http://beyondmicrofoundations.blogspot.com/ blog].

File:David Pugh.jpg

2012-06-01T15:41:36Z

DPugh:

David Pugh

2012-06-01T15:27:57Z

DPugh: Created page with ' == Who am I? == Graduated class of 2005 from the College of William and Mary with a BS in Mathematics. Spent a few years as a professional actor, before "settling down" into a …'

== Who am I? ==
Graduated class of 2005 from the College of William and Mary with a BS in Mathematics. Spent a few years as a professional actor, before "settling down" into a job as a senior research analyst for the Institute for Physical Sciences (IPS). IPS was basically a think-tank that used insights from complexity science to build models of political and economic systems (it was also where I was first learned of the existence of SFI!). I received my MSc in Economics from the University of Edinburgh in 2009. Worked a bit as a senior research analyst for Centra Technologies, before receiving PhD funding to return to the University of Edinburgh in 2010. I am currently in the second year of my PhD.

== Research Interests... ==
I think that financial markets are best thought of as being complex adaptive systems whose agents (i.e., households, firms, traders, investors, financial intermediaries, etc) are engaged in economic activity as part of a inter-connected, dynamic network of financial contracts. Here are a couple of ideas for projects that I would be keen to develop with the help of SFI staff and other CSSS participants:

* Can credit and liquidity constraints generate [http://en.wikipedia.org/wiki/Volatility_clustering volatility clustering]? Basically, I would like to develop a computational model that incorporates key ideas from the financial contracting literature and generates the volatility clustering that we observe in the data.
* Systemic risk in financial networks. Why do financial crises seem to be an [http://www.reinhartandrogoff.com/ endemic] feature of market economies? Do financial markets sow the seeds of their own destruction? In particular, does diversification (a rational risk management strategy for an individual agent) actual increase systemic risk under certain circumstances? My point of departure for answering these questions is a classic model of self-organized criticality, the [http://en.wikipedia.org/wiki/Forest-fire_model forest fire] model.

My modeling strategy is inherently inter-disciplinary in nature and relies heavily on previous work on nonlinear dynamics, machine learning, network theory, and computer modeling and simulation in ''R'' and ''Python''. More info about my research interests can be found on my [http://beyondmicrofoundations.blogspot.com/ blog].

File:L1010039.JPG

2012-06-01T15:03:32Z

DPugh: