icpsr - complex systems models in the social sciences - lecture 1 - professor daniel martin katz
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Complex Systems Models in the Social Sciences
(Lecture I)
Daniel Martin KatzMichigan State University
College of Law
Structure of this Course
Lecture - 9:00am - 10:00am
Lab - 5:00pm - 6:00pm
Structure of this Course
CC Little Michigan Lab@ Helen Newberry Hall
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
My Background
Assistant Professor of LawMichigan State University
Former NSF IGERT Fellow,University of Michigan
Center for the Study of Complex Systems(2009-2010)
PhDPolitical Science & Public Policy
University of Michigan(2011)
JDUniversity of Michigan
Law School(2005)
Blog Run with
Michael Bommarito II
JonZelner
Course slides will be
Posted Here!
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
Introduction to Complex Systems
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
Complex Systems Emphasizes
Simple behavioral rules generating complex and unforeseen outcomes
Self - organization / lack of top down control
Non-linearity, Emergence, Positive Feedback
Equilibrium and its Discontents?
Is an analytical solution up to the challenge?
What qualitative justification can be offeredfor 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
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
Brief Introduction to Agent Based Modeling
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
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
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
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
Other Important ContributorsRobert 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
Other Important Contributors
Joshua Epstein, Robert Axtell, John H. Holland
A Few Major Institutes & Centers
The Study ofComplex Systems
includes
Sociophysics
Natural Language Processing
Machine Learning
Network Science
Statistical Methods
Out of Equilibrium Models
Non Linearity
Scaling
Diffusion
Social Epidemiology
Information Theory
New Kind of Science
Computational Game Theory
Web Scrapping
Agent Based Models
Measuring Complexity
What is Complex Systems?
Complex Systems Offers
A Set of Tools
that allow us
to perhaps better understand
The Dynamics Underlyingthe Behavior of
Social, Political and Economics Systems
Taxonomy of Approaches
Data Analysis
FormalModels
Complex Adaptive Systems
Data Analysis
FormalModels
Complex Adaptive Systems
Data Analysis
This is the Era of “Big Data”
Decreasing Data Storage Costs
Increasing Computing Power
Fundamentally Altering the Scope of Scientific Inquiry
Highlighting the Data Deluge
2008 2009 2010
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
Data Analysis
statisticalmodels
and methods
network analytic methods
text as
data
Measuring Complexity
Data Analysis
statisticalmodels
and methods
network analytic methods
text as
data
Measuring Complexity
More To Come On All of These Topics as the Course Continues
What is Complex Adaptive Systems?
Complex Adaptive Systems
Data Analysis
FormalModels
FormalModels
Formal Models
Othercomputational
Models
network models
AgentBased
Modeling
Why Generate Formal Models?
Formal Models v.
Data
The Evaluation of Counterfactuals
The Evaluation of Alternative
‘States of the world’
Cannot not be Exclusively Data Driven
A Few Examples ...
Theoretical Models and Computational Simulations
schelling’s segregation model
Axelrod’s Evolution of Cooperation model
We are interested in theData Generating Processes
For Example, Formal Network Models
Barabási-Albert Preferential Attachment
Othercomputational
Models
network models
AgentBased
Modeling
Complex Adaptive Systems
Data Analysis
FormalModels
statisticalmodels
and methods
network analyticmethods
text as
data
Measuring Complexity
More To Come Tomorrow!
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