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Apply to EITM More information Schedule Institutions and Institutional Analysis -Week 2 Experimentation in the Social and Behavioral Sciences -Week 3 Complexity: Computational Models and Social Networks -Week 4
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Syllabus – Draft
Empirical Implications of Theoretical Models (EITM): Complexity, Computational Models and Social Networks
Leads: Scott de Marchi (Duke) James H. Fowler (UC-SD)
Guests: Betsy Sinclair (Chicago) Charles Taber (SUNY-SB)
Summary EITM, for many social scientists, involves developing a correspondence between game theory (i.e., the theoretical models half of the acronym) and parametric statistical work (i.e., the empirical implications half). Implicit in this formulation is the idea that game theory, as an encoding, represents rational play, and can accommodate most phenomena of interest. The challenge is that many game theoretic models lack clear empirical referents; thus, better tools are needed to test the results of these models. This approach to EITM is certainly useful, but will not by and large be the focus of the third week. Instead, we will be taking the road less traveled. For us, theoretical models will most often be generated using computational experiments written in a programming language like R, C, or Perl. For computational and complex systems models, deductive tractability is sacrificed for more verisimilitude, richer models with a greater number of testable implications, and the incorporation of dynamics (e.g., even if there is an equilibrium, how does a population of agents reach it – if ever?). Computational models are particularly attractive for those who study complex network phenomena. Scientists are increasingly realizing that the interconnected nature of human social relationships presents special challenges that are frequently intractable in a sparse game-theoretic setting. At the same time, the increasing availability of large-scale social network data provides an opportunity to study political phenomena at both the micro and macro level. The main goal of the week will be to consider questions that are not normally asked within the confines of the game theoretic tradition, and consider what types of analytic and statistical methods are required to answer them. Accordingly, much of our time will be spent learning new skills required for computational modeling and social network analysis. Readings + Themes. Before you start
our week, we recommend:
Programming
June 29 (Sunday). Theme: Basic Programming Skills
June 30 (Monday).
Theme: EITM = Combining methods. Leads: Scott de Marchi and James
Fowler.
9 – 10, Introduction by Fowler 10 – 12, Overview of Computational Models (and their place in methods generally) 1 – 3, All you need to know about non-parametric statistics, equivalence classes, optimization, and other sundry topics 3 – ?, Group Work on first assignment & meetings
July 1 (Tuesday).
Theme: Computational Models Applied to Elections. Lead: Charles
Taber.
Recommended:
9 – 11, Group presentations of first homework assignment 12 – 4, Elections, computational style 4 – ?, Group Meetings
July 2 (Wednesday).
Theme: Social Network Theory. Lead: Betsy Sinclair.
Recommended:
9 – 12, Introduction to social networks 1 – 3, Applications of social networks part 1 3 – 4, Group Meetings
July 3 (Thursday).
Theme: Applied Social Network Theory. Lead: James Fowler.
9 – 12, Applications of networks to social science part 2 1– 4, Group work & meetings
July 4 (Friday). Theme: Computational Models of Social Network Phenomena in Political Science
9 – 11, Group presentations 11 – 12, Concluding remarks and discussion (Fowler) 12 – 1, Concluding remarks and discussion (de Marchi) 2 – 4, Meetings and personal project time
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Department of Political Science, 326 Perkins Library, Box 90204, Duke University, Durham, NC 27708. Phone 919.660.4300 -- Fax 919.660.4330 |
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