Difference between revisions of "CSSS 2010 Santa Fe-Readings"

From Santa Fe Institute Events Wiki

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* [[Media:Smith_comp_lang.pdf‎|Opportunities in quantitative historical linguistics]]
* [[Media:Smith_comp_lang.pdf‎|Opportunities in quantitative historical linguistics]]

* [[Media:Maddieson_comp_lang.pdf‎|Evolutionary Linguistics]]
* [[Media:Maddieson_comp_lang.pdf‎|Typological patterns in language and their relation to linguistic history]]

Revision as of 13:19, 16 June 2010

CSSS Santa Fe 2010

Please review readings before lectures. Supplemental material may be posted as well.

Background Readings

Please see the Background Readings page for assignments.

Dan Rockmore

Dan's Intro Lecture
Draft intro to stat learning primer

Tanmoy Bhattacharya

Inference in Historical Processes

Liz Bradley

Tom Carter

Here is a link to a page with various background readings -- I'll be talking about some of this material, watch the wiki for days/times

Iain Couzin

Simon DeDeo

For the two lectures on "Physics of Reasoning" --

more advanced --

If you only have time to read one, read Jaynes! If you are unable to access any of these articles, feel free to e-mail me at

Owen Densmore

Steve Guerin and I will present modeling with NetLogo. It will have two parts, one a self-paced wiki tutorial, the second a set of real world examples of modeling we've used professionally.

Read the first page of the tutorial. Then (attempt!) to download, install, and look at the Model Library.

Peter Dodds

Nathan Eagle

Doug Erwin

Duncan Foley

Laura Fortunato

Greg Leibon

John Harte

Jure Leskovec

1) -- models and link prediction in large social networks:

2) -- tracking information diffusion, finding influencers and detecting disease outbreaks in networks

3) -- models of networks with positive and negative ties

Cosma Shalizi

Adaptation and Self-Organizing Systems

Artificial Intelligence

Cellular Automata and Lattice Gases

Statistical/Computational Mechanics

Neurons and Cognition


Eric Smith

Alfred Hübler

Scott Pauls

Links forthcoming!

  • Lecture on using the PDM on voting data.
  • Code package
  • Sample voting data