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.

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

9. Introduction to
Complex Systems

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

25. Natural Language Processing

26. Machine Learning

27. Network Science

28. Statistical Methods

29. Out of Equilibrium Models

30. Non Linearity

31. Scaling

32. Diffusion

33. Social Epidemiology

34. Information Theory

35. New Kind of Science

36. Computational Game Theory

37. Web Scrapping

38. Agent Based Models

39. Measuring Complexity

40. What is Complex Systems?

41. Complex Systems
Offers

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

47. Taxonomy of Approaches

48. Data
Analysis
Formal
Models
Complex Adaptive Systems

49. Data
Analysis
Formal
Models
Complex Adaptive 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

54. Data Analysis
statistical
models
and
methods
network
analytic
methods
text
as
data
Measuring
Complexity

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?

57. Complex Adaptive Systems
Data
Analysis
Formal
Models

58. Formal
Models

59. Formal Models
Other
computational
Models
network
models
Agent
Based
Modeling

60. Why Generate
Formal Models?

61. Formal Models
v.
Data

62. The Evaluation of
Counterfactuals

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

71. Barabási-Albert
Preferential Attachment

73. 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

74. all models are wrong
- but some are useful
-George E. P. Box

75. Final Thought

76. Scope of Resolution
Scale

77. https://vimeo.com/5368967