ICPSR - Complex Systems Models in the Social Sciences - Lecture 1 - Professor Daniel Martin Katz
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This document provides an overview of a course on complex systems models in the social sciences. It discusses: - The structure of the course, which will include lectures, labs, theoretical building blocks, empirical investigations, and applied case studies. - The background and goals of the instructor, which include providing an introduction to computational and agent-based modeling approaches, a foundation in implementation, and contrasting various modeling approaches. - An introduction to key concepts in complex systems, including bottom-up modeling, heterogeneous agents, self-organization, non-linearity and emergence. - Taxonomies of modeling approaches that will be covered, including data analysis, formal models, and complex adaptive systems modeling.
ICPSR - Complex Systems Models in the Social Sciences - Lecture 1 - Professor Daniel Martin Katz
- 1. Complex Systems Models in the Social Sciences (Lecture I) daniel martin katz illinois institute of technology chicago kent college of law @computationaldanielmartinkatz.com computationallegalstudies.com
- 2. Structure of this Course
- 3. Lecture - 9:00am - 10:00am Lab - 5:00pm - 6:00pm Structure of this Course CC Little Michigan Lab @ Helen Newberry Hall
- 4. Theoretical Building Blocks Empirical Investigations Implementation Applied Cases in Social, Political & Economic Systems Lecture - 9:00am - 10:00am Lab - 5:00pm - 6:00pm Structure of this Course Michigan Lab @ Helen Newberry Hall CC Little
- 5. My Background Associate Professor of Law IIT-Chicago Kent College of Law Former NSF IGERT Fellow, University of Michigan Center for the Study of Complex Systems PhD Political Science & Public Policy University of Michigan JD University of Michigan Law School
- 6. Blog Run with Michael Bommarito II Jon Zelner
- 7. Course slides will be Posted Here!
- 8. Goals for the Class Provide Introduction to Computational and Agent Based Approaches to Modeling Provide a Solid Foundation in Implementation Game Theoretic, Agent Based Models, Network Models, Ecological Models, etc. Contrast Various Approaches Highlighting Benefits and Drawbacks Be a Good Consumer of 3rd Party Implementation Actually Implement Models Using Appropriate Software
- 10. Key Features of Complex Systems Bottom up versus Top Down Emphasizes dependancies between actors Heterogeneous rather than Homogenous Agents Complexity and CAS is not chaos theory Emphasizes learning and adaptation by actors
- 11. Complex Systems Emphasizes Simple behavioral rules generating complex and unforeseen outcomes Self - organization / lack of top down control Non-linearity, Emergence, Positive Feedback
- 12. Equilibrium and its Discontents? Is an analytical solution up to the challenge? What qualitative justification can be offered for assuming something is a fixed point attractor? Is a representative agent model appropriate? Does the solution concept scale to the scope of the problem? CAS Focuses upon out of equilibrium solutions
- 13. Equilibrium and its Discontents? When describing what would later be called the nash equlibrium to john von neumann in 1949, von Neumann famously dismissed it with the words, “That’s trivial, you know. That’s just a fixed point theorem.” “A Beautiful Mind” By Sylvia Nasar (1998) clearly overstated but it is worth remembering that a fixed point based solution has limitations
- 14. Brief Introduction to Agent Based Modeling
- 15. Complex Systems and Agent Based Modeling Agent Based Models are an Approach to Study Complex Adaptive Systems However, the study of complex systems embraces a number of theoretical and empirical approaches ABM’s are only one particular manner to execute the study of complex systems
- 16. Grand Father of Agent Based Modeling Arguably the Most Important Mind of the 20th Century Invented Game Theory Helped Develop Atomic Bomb Developed the Architecture of the Computer
- 17. 2005 Nobel Prize Winner Argues for Bottom Up Approach to Modeling In “Micromotives & Macrobehavior” Outlines the Famous Schelling Segregation Model (aka the ‘Tipping’ Model) Father of Agent Based Modeling
- 18. Other Important Contributors John H. Conway Developed the “game of life” a simple cellular automaton Life is a universal cellular automaton capable of emulating any turing machine Simple rules can generate Complex Environments “Game of Life” offers lots of Important Complex Systems Principles
- 19. Other Important Contributors Robert Axelrod One of the top cited social scientists in world Has made many contributions to the field of agent based modeling http://www-personal.umich.edu/~axe/research_papers.html Consult His Papers Page: Axelrod & Tesfatsion Guide to Agent Based Models: http://econ2.econ.iastate.edu/tesfatsi/abmread.htm
- 20. Other Important Contributors Joshua Epstein, Robert Axtell, John H. Holland
- 21. A Few Major Institutes & Centers
- 23. The Study of Complex Systems includes
- 24. Sociophysics
- 26. Machine Learning
- 27. Network Science
- 29. Out of Equilibrium Models
- 30. Non Linearity
- 31. Scaling
- 32. Diffusion
- 35. New Kind of Science
- 37. Web Scrapping
- 40. What is Complex Systems?
- 42. A Set of Tools
- 43. that allow us to perhaps better understand
- 44. The Dynamics Underlying the Behavior of
- 45. Social, Political and Economics Systems
- 50. Data Analysis
- 51. This is the Era of “Big Data” Decreasing Data Storage Costs Increasing Computing Power Fundamentally Altering the Scope of Scientific Inquiry
- 52. Highlighting the Data Deluge 2008 2009 2010
- 53. The Case for a Computational Approach Complex Systems Output large amounts of Information Need Methods that Scale to the Size and Scope of this Body of Information
- 55. Data Analysis statistical models and methods network analytic methods text as data Measuring Complexity More To Come On All of These Topics as the Course Continues
- 56. What is Complex Adaptive Systems?
- 58. Formal Models
- 63. The Evaluation of Alternative ‘States of the world’
- 64. Cannot not be Exclusively Data Driven
- 65. A Few Examples ...
- 66. Theoretical Models and Computational Simulations schelling’s segregation model Axelrod’s Evolution of Cooperation model
- 67. Counterfactual What if Congress/President had *not* voted for the TARP?
- 69. We are interested in the Data Generating Processes
- 70. For Example, Formal Network Models
- 74. all models are wrong - but some are useful -George E. P. Box
- 75. Final Thought