Principles of agent-based modeling Computational Modeling of Social - - PowerPoint PPT Presentation

• Prof. Lars-Erik Cederman

ETH - Center for Comparative and International Studies (CIS) Seilergraben 49, Room G.2, lcederman@ethz.ch Christa Deiwiks, CIS Room E.3, deiwiks@icr.gess.ethz.ch http://www.icr.ethz.ch/teaching/compmodels

Introduction to Computational Modeling of Social Systems

Introduction to Computational Modeling of Social Systems

Principles of agent-based modeling

Week 3

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Today’s agenda

• Prehistory
• Other types of models
• Principles of agent based modeling
• Categories of ABM models
• The pros and cons of ABM

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Historical Lineages of ABM

Source: Nigel Gilbert

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Von Neumann’s theory of cellular automata

• Cellular automata are

discrete dynamical systems that model complex behavior based

• n simple, local rules

animating cells on a lattice

Invented by John von Neumann

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Game of Life

• First practical CA invented

by John Conway in the late 1960s

• Later popularized by

Martin Gardner

• A dead cell with 3 live

neighbors comes to life

• A live cell with 2 or 3

neighbors stays alive

• Otherwise the cell dies

Stephen Wolfram Expert on CAs

John Conway

Simple rules:

http://www.ibiblio.org/lifepatterns/

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Four types of models

Analytical focus: Systemic variables Micro- mechanisms Modeling language: Deductive Computational

• 4. Agent-based

modeling

• 3. Rational

choice

• 1. Analytical

macro models

• 2. Macro-

simulation

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• 1. Analytical macro models
• Equilibrium conditions
• r systemic variables

traced in time

• Closed-form, and often

based on differential equations

• Examples: macro

economics and traditional systems theory

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• 2. Macro simulation
• Dynamic systems, tracing

macro variables over time

• Based on simulation
• Systems theory and Global

Modeling

Jay Forrester, MIT

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• 3. Rational choice modeling
• Individualist reaction to

macro approaches

• Decision theory and game

theory

• Analytical equilibrium

solutions

• Used in micro-economics

and spreading to other social sciences

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• 4. Agent-based modeling
• ABM is a computational methodology that allows

the analyst to create, analyze, and experiment with, artificial worlds populated by agents that interact in non-trivial ways

• Bottom-up
• Computational
• Builds on CAs and DAI

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Complex Adaptive Systems

A CAS is a network exhibiting aggregate properties that emerge from primarily local interaction among many, typically heterogeneous agents mutually constituting their own environment.

Emergent properties Large numbers of diverse agents Local and/or selective interaction Adaptation through selection Endogenous, non-parametric environment

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Microeconomics ABM

Analytical

• Synthetic approach

Equilibrium

• Non-equilibrium theory

Nomothetic

• Generative method

Variable-based Configurative ontology

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Analytical Synthetic approach

• Hope to solve problems through

strategy of “divide and conquer”

• Need to make ceteris paribus

assumption

• But in complex systems this

assumption breaks down

• Herbert Simon: Complex systems

are composed of large numbers of parts that interact in a non-linear fashion

• Need to study interactions explicitly

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Equilibrium Non-equilibrium theory

• Standard assumption in the social

sciences: “efficient” history

• But contingency and positive

feedback undermine this perspective

• Complexity theory and non-

equilibrium physics

• Statistical regularities at the

macro level despite micro-level contingency Example: Avalanches in rice pile

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Nomothetic

• Generative method
• Search for causal regularities
• Hempel’s “covering laws”
• But what to do with complex

social systems that have few counterparts?

• Scientific realists explain

complex patterns by deriving the mechanisms that generate them

• Axelrod: “third way of doing

science”

• Epstein: “if you can’t grow it, you

haven’t explained it!”

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Variable-based Configurative ontology

• Conventional models are variable-

based

• Social entities are assumed

implicitly

• But variables say little about social

forms

• A social form is a configuration of

social interactions and actors together with the structures in which they are embedded

• ABM good at endogenizing

interactions and actors

• Object-orientation is well suited to

capture agents

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Emergent social forms

1. Interaction patterns 2. Property configurations 3. Dynamic networks

• 4. Actor structures

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• 1. Emergent interaction patterns

actor actor actor actor actor actor actor actor actor

• Models of “emergent
• rder” producing

configurations

• Axelrod (1984, chap. 8):

“The structure of cooperation”

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• 2. Emergent property

configurations

• Models of “emergent structure”

constituted as property configruations

• Example: Schelling’s

segregation model; Carley 1991; Axelrod 1997

• See Macy 2002 for further

references

actor actor actor actor actor actor actor actor actor actor actor actor actor actor actor actor actor actor

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• 3. Emergent dynamic networks
• Most computational

models treat networks as exogenous

• Recent exceptions:

– Albert and Barabási’s scale- free networks – Economics and evolutionary game theory: e.g. Skyrms and Pemantle

frequency degree d d-α

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• 4. Emergent actor structures
• Computational models

normally assume the actors to be given

• Exceptions:

– Axelrod’s model of new political actors – Axtell’s firm-size model – Geopolitical models in the Bremer & Mihalka tradition

• Emergence?

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Why use agent-based modeling?

ABM helps fill the gap between formal but restrictive models and wide-ranging but imprecise qualitative frameworks Contingency and counterfactuals Mechanisms in space and time Intangible concepts

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The limits of ABM?

ad hoc assumptions? failure to yield predictions? fragility of results? lack of cumulation?

Horgan, John. 1995. "From Complexity to Perplexity." Scientific American 272: 104-9. Binmore, Ken. 1998. Review of The Complexity of Cooperation. JASSS 1: http://jasss.soc.surrey.ac.uk/1/1/review1.html See also references in Rosser (recommended reading)