ICPSR - Complex Systems Models in the Social Sciences - Lecture 1 - Professor Daniel Martin Katz

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ICPSR - Complex Systems Models in the Social Sciences - Lecture 1 - Professor Daniel Martin Katz

  1. 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. 2. Structure of this Course
  3. 3. Lecture - 9:00am - 10:00am Lab - 5:00pm - 6:00pm Structure of this Course CC Little Michigan Lab @ Helen Newberry Hall
  4. 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. 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. 6. Blog Run with Michael Bommarito II Jon Zelner
  7. 7. Course slides will be Posted Here!
  8. 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
  9. 9. Introduction to Complex Systems
  10. 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. 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. 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. 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. 14. Brief Introduction to Agent Based Modeling
  15. 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. 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. 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. 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. 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. 20. Other Important Contributors Joshua Epstein, Robert Axtell, John H. Holland
  21. 21. A Few Major Institutes & Centers
  22. 22. The Study of Complex Systems includes
  23. 23. Sociophysics
  24. 24. Natural Language Processing
  25. 25. Machine Learning
  26. 26. Network Science
  27. 27. Statistical Methods
  28. 28. Out of Equilibrium Models
  29. 29. Non Linearity
  30. 30. Scaling
  31. 31. Diffusion
  32. 32. Social Epidemiology
  33. 33. Information Theory
  34. 34. New Kind of Science
  35. 35. Computational Game Theory
  36. 36. Web Scrapping
  37. 37. Agent Based Models
  38. 38. Measuring Complexity
  39. 39. What is Complex Systems?
  40. 40. Complex Systems Offers
  41. 41. A Set of Tools
  42. 42. that allow us to perhaps better understand
  43. 43. The Dynamics Underlying the Behavior of
  44. 44. Social, Political and Economics Systems
  45. 45. Taxonomy of Approaches
  46. 46. Data Analysis Formal Models Complex Adaptive Systems
  47. 47. Data Analysis Formal Models Complex Adaptive Systems
  48. 48. Data Analysis
  49. 49. This is the Era of “Big Data” Decreasing Data Storage Costs Increasing Computing Power Fundamentally Altering the Scope of Scientific Inquiry
  50. 50. Highlighting the Data Deluge 2008 2009 2010
  51. 51. 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
  52. 52. Data Analysis statistical models and methods network analytic methods text as data Measuring Complexity
  53. 53. 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
  54. 54. What is Complex Adaptive Systems?
  55. 55. Complex Adaptive Systems Data Analysis Formal Models
  56. 56. Formal Models
  57. 57. Formal Models Other computational Models network models Agent Based Modeling
  58. 58. Why Generate Formal Models?
  59. 59. Formal Models v. Data
  60. 60. The Evaluation of Counterfactuals
  61. 61. The Evaluation of Alternative ‘States of the world’
  62. 62. Cannot not be Exclusively Data Driven
  63. 63. A Few Examples ...
  64. 64. Theoretical Models and Computational Simulations schelling’s segregation model Axelrod’s Evolution of Cooperation model
  65. 65. Counterfactual What if Congress/President had *not* voted for the TARP?
  66. 66. We are interested in the Data Generating Processes
  67. 67. For Example, Formal Network Models
  68. 68. Barabási-Albert Preferential Attachment
  69. 69. Other computational Models network models Agent Based Modeling Complex Adaptive Systems Data Analysis Formal Models statistical models and methods network analytic methods text as data Measuring Complexity
  70. 70. all models are wrong - but some are useful -George E. P. Box
  71. 71. Final Thought
  72. 72. Scope of Resolution Scale
  73. 73. https://vimeo.com/5368967

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