lars-erik cederman and luc girardin center for comparative and international studies (cis) swiss...

Lars-Erik Cederman and Luc GirardinCenter for Comparative and International Studies (CIS)

Swiss Federal Institute of Technology Zurich (ETH)http://www.icr.ethz.ch/teaching/compmodels

Advanced Computational Modelingof Social Systems

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Today‘s agenda

• Course goals• Introduction to ABM• Course logistics

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Course goals

• Study the principles of agent-based modeling

• Survey applications to the social sciences

• Develop your own computational model of a social system

• Prerequisite: Programming skills

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Four types of models

Analyticalfocus:

Systemicvariables

Micro-mechanis

ms

Modeling language:Deductive Computational

4. Agent-based

modeling

3. Rationalchoice

1. Analytical macro models

2. Macro-simulation

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1. Analytical macro models

• Equilibrium conditions or 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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Disaggregated modeling

Organizations of agents

Animate agents

Data

Artificial world

Observer

Inanimate agents

If <cond>

then <action1>

else <action2>

If <cond>

then <action1>

else <action2>

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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 contingencyExample: 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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A third way of doing science

1. Deduction– Derive theorems from assumptions

2. Induction– Find patterns in empirical data

3. Simulation– Start with explicit assumptions

(deduction)– Generate data suitable for analysis

(induction)

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Empirical understanding

• Why have particular large-scale regularities evolved and persisted, even when there is little top-down control?

• Examples: standing ovations, trade networks, socially accepted monies, mutual cooperation based on reciprocity, and social norms

• ABM: seek causal explanations grounded in the repeated interactions of agents operating in specified environments

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Normative understanding

• How can agent-based models be used as laboratories for the discovery of good designs?

• Examples: design of auction systems, voting rules, and law enforcement

• ABM: evaluate whether designs proposed for social policies, institutions, or processes will result in socially desirable system performance over time

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Heuristic

• How can greater insight be attained about the fundamental causal mechanisms in social systems?

• Examples: city segregation (or “tipping”) model developed by Thomas Schelling

• The large-scale effects of interacting agents are often surprising because it can be hard to anticipate the full consequences of even simple forms of interaction

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Methodological advancement

• How to provide ABM researchers with the methods and tools they need of social systems through controlled computational experiments?

• Examples: methodological principles, programming tools, visualization techniques

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A methodological approach

• ABM is a methodological approach that could ultimately permit two important developments:– The rigorous testing, refinement, and

extension of existing theories that have proved to be difficult to formulate and evaluate using standard statistical and mathematical tools

– A deeper understanding of fundamental causal mechanisms in multi-agent systems whose study is currently separated by artificial disciplinary boundaries

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Logistics

• Performance evaluation– Class participation– Class presentation– Term paper

• Readings– On our server

• Class home page: http://www.icr.ethz.ch/teaching/compmodels

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Course schedule– 29.03.2005: Introduction and logistics

• Concepts– 05.04.2005: Complexity theory– 12.04.2005: Artificial life and intelligence– 19.04.2005: Network models

• Applications– 26.04.2005: Traffic Project memo due!– 03.05.2005: Economy– 10.05.2005: Sociology– 17.05.2005: Conflict

• Empirical methods– 24.05.2005: Validation– 31.05.2005: GIS

• Student presentations– 07.06; 14.06; 21.06; 28.06.2005

• Final paper due July 5, 2005

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Complexity theory

A model of the Internet

The Santa Fe Institute

“Boids”

Complex adaptive systems exhibit properties that emerge from local interactions among many heterogeneous agents mutually constituting their own environment

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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