VII Trento Summer School

Agent-Based Computational Economics (ACE):
Growing Economies from the Bottom Up

Supporting Materials for
Lectures by Co-Director Leigh Tesfatsion

Last Updated: 23 July 2006

Co-Director Contact Information:

Leigh Tesfatsion

http://www.econ.iastate.edu/tesfatsi/

tesfatsi AT iastate.edu

Exercise Info Course Project Info ACE Website ACE Demo Software RepastJ Guide (Agent-Based Modeling Toolkit) ACE Course (Self-Study eBook)

Course Overview Topics, Readings, & Exercises

Course Overview

A modern market-based economy is a complex adaptive system. Vast numbers of geographically-distributed individuals and social groupings interact over time through markets and other institutions. They struggle to survive and, if possible, to prosper, by learning how to compete and cooperate in appropriate measure. These micro interactions give rise to regularities at the level of society as a whole, such as trade networks, socially accepted monies, and the common adoption of technological innovations. In turn, social regularities feed back into the determination of micro interactions.

Recent developments in computer modeling, in particular object-oriented and agent-oriented programming tools, permit new approaches to the study of this complex two-way feedback between micro interactions and social regularities. The primary objective of this intensive course is to introduce, motivate, and explore through concrete applications the potential usefulness of one such approach -- Agent-based Computational Economics (ACE) -- the computational study of economic processes modeled as dynamic systems of interacting agents.

Topics, Readings, and Exercise Assignments

PLEASE NOTE:

Required readings are marked below with two asterisks (**). Highly recommended readings are listed with a single asterisk (*) and other recommended are listed with no asterisk.

1. Introduction
   1. What are Complex Adaptive Systems (CAS)?
   2. What is ACE?
2. The Complexity of Decentralized Market Economies
3. Design and Use of Computational Laboratories
4. Learning and the Embodied Mind
   1. Illustrative Examples of Situated Learning
   2. Learning Representations
5. Financial Market Illustrations
6. Economic Interaction on Fixed Networks
7. Endogenous Network Formation: Labor Market Illustration
8. Real-World Application: Electricity Restructuring Project
9. Empirical Validation of ACE Models

Appendix: Possible Course Project Topic Areas (with Linked Resource Sites)

I. Introduction

I.A What are Complex Adaptive Systems (CAS)?

Key In-Class Discussion Topics:

• What is a complex system? a complex adaptive system?
• Illustrative examples (Bak's Sand Pile Model, Schelling's Segregation Model,...)
• Experimental design: Basic concepts and terminology

** Exercise 1 (Team Exercise): Introduction to the Schelling Segregation Model (pdf,27K).

** Exercise 2 (Team Exercise): Conducting Experiments with Chris Cook's Schelling Demo Model (pdf,27K).

Required Readings:

• ** Tamás Vicsek, "Complexity: The Bigger Picture" (pdf,71K), Nature, Vol. 418, 11 July 2002, p. 131. ON-LINE
  Abstract: In this short essay, Vicsek describes how computer simulation fits into the scientific enterprise. The goal is to "capture the principal laws behind the exciting variety of new phenomena that become apparent when the many units of a complex system interact."

  ** Andy Clark, "Preface: Deep Thought Meets Fluent Action" (pp. xi-xiii) and "Introduction: A Car with a Cockroach Brain" (pp. 1-8), in Being There: Putting Brain, Body, and World Together Again, MIT Press, 1998 (paperback).

  Note: If at all possible, make time to savor this entire delightful book!

  ** David F. Batten, "Preface" plus Chapter 1:"Chance and Necessity" (pdf preprint-no figures,247K) plus Chapter 8: "Artificial Economics" (pdf preprint-no figures,173K) in Discovering Artificial Economics: How Agents Learn and Economies Evolve, Perseus Books, Westview Press, 2000, plus Leigh Tesfatsion, "Notes on Batten Chapter 1, plus Glossary of Terms" (html). ON-LINE

  IMPORTANT NOTE: The Batten book is unfortunately out of print. However, a pdf file for the entire Batten book (including figures) can be accessed here (pdf,17M).

  ** Leigh Tesfatsion, "Possible definitions for `Complex System' and `Complex Adaptive System'" (pdf,19K). ON-LINE

  ** Leigh Tesfatsion, "Introduction to Cellular Automata" (pdf,576K). ON-LINE

  ** Leigh Tesfatsion, "Implementing Per Bak's Sand Pile Model as a Cellular Automaton" (pdf,50K). ON-LINE

Recommended Materials:

• * Game of Life, Sand Pile Model, and Schelling Segregation Model: Demonstration Software (html) ON-LINE
  Interactive computational frameworks for running hands-on experiments with Per Bak's Sand Pile Model and Thomas Schelling's Segregation Model can be found at this site.

  * Leigh Tesfatsion, "Experimental Design: Basic Concepts and Terminology" (pdf,43K). ON-LINE

  * For a wonderful introduction to computational aspects of complex systems, including fractals, chaos, cellular automata, neural networks, and a helpful glossary of terms, I highly recommend Gary William Flake, The Computational Beauty of Nature (MIT Press, Cambridge, MA, 1998). Detailed information about Flake's book, along with source code and other supporting materials, can be found here.

  * Thomas Schelling, "Introduction (Chapter 1, pp. 9-43)," from Micromotives and Macrobehavior, W. W. Norton and Company, New York, 1978.

  Classic work by Thomas Schelling, one of the seminal contributors to the field of agent-based computational social science.

  Nathan Winslow, "Introduction to Self-Organized Criticality and Earthquakes" (html,12 pages), discussion paper, Department of Geological Sciences, University of Michigan, 1997. ON-LINE

  Winslow outlines a cellular automaton model of Per Bak's sand pile model in which the value of each cell is interpreted as the average gradient of the sand pile at that cell site.

I.B What is Agent-based Computational Economics (ACE)?

Key In-Class Discussion Topics:

• What is ACE all about?
• Illustrative examples

Required Readings:

• ** Leigh Tesfatsion, "Introduction to ACE" (pdf,120K). ON-LINE
  This presentation provides a brief discussion of the ACE methodology.

Recommended Materials:

• * Robert Axelrod and Leigh Tesfatsion, "A Guide for Newcomers to Agent-Based Modeling in the Social Sciences" (html). ON-LINE

• * Rob Axtell, "Agent-Based Computing in Economics" (pdf,256K), presented at the VII Trento Summer School, July 2006. ON-LINE

• * Joshua M. Epstein and Robert Axtell, Growing Artificial Societies: Social Science from the Bottom Up (Book Details), Brookings Institution Press, Washington D.C., and MIT Press, Cambridge, MA, 1996.

• * Leigh Tesfatsion, "Agent-Based Computational Economics: Modeling Economies as Complex Adaptive Systems" (pdf preprint,71K), Information Sciences, Volume 149 (2003), 263-269. ON-LINE

• Leigh Tesfatsion and Kenneth L. Judd, Handbook of Computational Economics: Vol. 2 Agent-Based Computational Economics, Handbooks in Economics Series, North-Holland, Elsevier, Amsterdam, the Netherlands, 2006, 904pp.
  • Preface, Table of Contents, and Abstracts
    
  • Publisher Book and Order Information

Other introductory source materials on CAS/ACE

II. Complexity of Decentralized Market Economies

Key In-Class Discussion Topics:

• The circular flow underlying decentralized market economies
• Key types of market players
• Key types of market organization
• Strategic interaction in decentralized market economies
• Taking agent autonomy seriously in multi-market modeling
• Modeling the coevolution of market behaviors and market institutions

** Exercise 3 (Team Exercise): Construction and Analysis of Market Demand and Supply Functions (pdf,25K).

** Exercise 4 (Team Exercise): Game Theory Analysis of Strategic Market Pricing, (pdf,45K).

Required Readings:

• ** Leigh Tesfatsion, "ACE Market Research: Illustrative Examples" (pdf,301K). ON-LINE

• ** Leigh Tesfatsion, "Market Organization with Price-Setting Agents" (html,8K). ON-LINE

• ** Leigh Tesfatsion, "Market Basics for Price-Setting Agents" (pdf,80K). ON-LINE

• ** Leigh Tesfatsion, "Game Theory: Basic Concepts and Terminology" (pdf,34K). ON-LINE

• ** Leigh Tesfatsion, "Notes on Price Discovery with Price-Setting Agents" (pdf,103K). ON-LINE

Recommended Materials:

• Amy R. Greenwald and Jeffrey Kephart, "Shopbots and Pricebots", Sixteenth International Joint Conference on AI, Stockholm, Sweden, August 1999, pp. 506-511. ON-LINE
  This brief conference paper summarizes some of the authors' recent research on "shopbots," agents that automatically search the Internet for goods and services on behalf of consumers, and "pricebots," agents that set prices so as to maximize the profits of firms. Copies of this and related research articles are available from Amy Greenwald's publications site linked to her home page.

  John McMillan, Reinventing the Bazaar: A Natural History of Markets, W. W. Norton & Co., 2002.

  From the author: "New ideas in economics, and some old ones, are used in the chapters that follow to dissect exotic, innovative, and everyday marketplaces - some in physical space, others in cyberspace. How do markets work? What can they do? What can't they do. These are the questions I will address."

  The Alliance for Innovative Manufacturing (AIM) at Stanford University maintains an interesting site titled How Everyday Things Are Made (html). ON-LINE

  The site provides manufacturing video (virtual factory tours) covering the manufacturing processes for over forty types of common products (cars, planes, chocolate, glass bottles, etc.). These videos stress the extraordinary degree of coordination among input suppliers, producers, and distributors required to bring to market even seemingly simple products such as a jelly bean.

ACE-Related Research on Multi-Market Modeling

III. Design and Use of Computational Laboratories

Key In-Class Discussion Topics:

• What is "object-oriented programming (OOP)"?
• Is object orientation a natural way to think about complex systems?
• What's the difference between an "object" and an "agent"?
• Why computational laboratories?
• Example: The Trade Network Game (TNG) Laboratory
• In what ways might the comp lab modeling of social (multiple agent) systems require special capabilities and features?

Required Readings:

• ** Leigh Tesfatsion, "Introduction to Agent-Oriented Programming" (pdf presentation,110K). ON-LINE
  This tutorial briefly discusses basic object-oriented programming (OOP) concepts, what is agent-oriented programming (AOP), and how AOP compares and contrasts with OOP. It also briefly discusses how AOP can be implemented via computational laboratories, using the Trade Network Game (TNG) Laboratory for concrete illustration.

  ** Rob Axtell, "Platforms for Agent-Based Computational Economics" (pdf,35K), presented at the VII Trento Summer School, July 2006. ON-LINE

  ** Matt Weisfeld, "Introduction to Object-Oriented Concepts" Chapter 1 (pp. 8-31) in The Object-Oriented Thought Process, SAMS Books, Macmillan, 2000.

Recommended Materials:

• The Trade Network Game: Demonstration Software (html). ON-LINE

• * Nigel Gilbert and Pietro Terna, "How to Build and Use Agent-Based Models in Social Science" (pdf,27pp), Discussion Paper, May 18, 1999. ON-LINE
  This essay discusses computational modelling as a third way of building social science models. Specific advice is given regarding how to build environments, represent agents, and specify agent behavioral rules. An object-oriented programming approach is stressed.

  Nicholas R. Jennings, "On Agent-Based Software Engineering" (pdf,257K), Artificial Intelligence 117 (2000), 277-296, copyright © 2002 Elsevier Science B.V. All rights reserved. ON-LINE

  Abstract: "Agent-based computing represents an exciting new synthesis both for Artificial Intelligence (AI) and, more generally, Computer Science. It has the potential to significantly improve the theory and the practice of modeling, designing, and implementing computer systems... The standpoint of this analysis is the role of agent-based software in solving complex, real-world problems. In particular, it will be argued that the development of robust and scalable software systems requires autonomous agents that can complete their objectives while situated in a dynamic and uncertain environment, that can engage in rich, high-level social interactions, and that can operate within flexible organizational structures."

  David McFadzean, Deron Stewart, and Leigh Tesfatsion, "A Computational Laboratory for Evolutionary Trade Networks", IEEE Transactions on Evolutionary Computation, Volume 5, Number 5, October 2001, pages 546-560 (pdf preprint,244K). ON-LINE

  Abstract:This study presents, motivates, and illustrates the use of the Trade Network Game Laboratory (TNG Lab), a computational laboratory for the investigation of evolutionary trade network formation among strategically interacting buyers, sellers, and dealers with learning capabilities. The TNG Lab together with tutorial materials can be downloaded as freeware at the TNG Homepage.

  Leigh Tesfatsion, "TNG Lab Parameter List" (pdf,19K). ON-LINE

  Abstract:This TNG Lab guide gives explanations and illustrative settings for the parameter values entered by the user on the settings screen for the TNG Lab graphical user interface (GUI). The entire range of feasible settings for each of these parameter values is also indicated.

Other source materials related to computational laboratories

IV. Learning and the Embodied Mind

IV.A Illustrative Examples of Situated Learning

Key In-Class Discussion Topics:

• How do people learn in dynamic strategic multi-agent situations with "behavioral uncertainty" (i.e., with uncertainty regarding the actions other agents will take)?
• How do people in dynamic strategic multi-agent situations make trade-offs between selfishness and a concern for fair play?

** In-Class Experiment: So how do YOU play the IPD?

** In-Class Experiment: One for all and all for one -- maybe!

** Exercise 5 (Team Exercise): Conducting Experiments with Chris Cook's Axelrod Tournament Demo, (pdf,39K).

Required Readings:

• ** Leigh Tesfatsion, "Notes on Axelrod's IPD Tournaments" (pdf,481K). ON-LINE

• ** Leigh Tesfatsion, "Overview of Sigmund, Fehr, and Nowak, `The Economics of Fair Play'" (pdf,23K). ON-LINE

Recommended Materials:

• * Chris Cook's Axelrod Tournament Demonstration Software (html)

• * Douglas Hofstadter, "Computer Tournaments of the Prisoner's Dilemma Suggest How Cooperation Evolves", Scientific American May 1983, 18-26.

• * Martin A. Nowak, Karen M. Page, and Karl Sigmund, "Fairness versus Reason in the Ultimatum Game" (pdf,91K), Science, Volume 289, September 8, 2000. ON-LINE

• * Karl Sigmund, Ernst Fehr, and Martin A. Nowak, "The Economics of Fair Play", Scientific American Vol. 83, January 2002, 83-87.

IV.B Learning Representations

Key In-Class Discussion Topics:

• What can be inferred from Rodney Brooks' observation that "elephants don't play chess"?
• Are the "minds" of real people best viewed as disembodied logical reasoning devices with appended information stores (as in traditional artificial intelligence), or as controllers for embodied activity (as in evolutionary psychology)?
• Illustrative learning representations (e.g., reinforcement learning, Q-learning, genetic algorithms (GAs), GA-classifier systems, artificial neural networks, ...)
• How should learning be represented for economic agents?
• If you had to construct a computational firm or consumer capable of functioning profitably over time within a computational market economy, how would you do it?
• Should the cognitive processes of computational agents necessarily mimic the cognitive processes of real people?
• Can "zero intelligence" agents outperform learning agents (e.g., people) in highly structured markets such as double auctions?

In-Class Exercise

• ** Leigh Tesfatsion, "Constructing Computational Economic Agents Who Learn" (pdf,53K). ON-LINE

** Exercise 6 (Team Exercise): Conducting Genetic Algorithm Learning Experiments with the Trade Network Game (TNG) Laboratory (pdf,48K).

Required Readings:

• ** Leigh Tesfatsion, "Learning Algorithms: Illustrative Example" (pdf,719K). ON-LINE

• ** Leigh Tesfatsion, "Notes on Learning" (pdf,157K). ON-LINE

• ** Nick Vriend, "An Illustration of the Essential Differences Between Individual and Social Learning and its Consequences for Computational Analyses" (pdf,245K), Journal of Economic Dynamics and Control 24 (2000), 1-19. ON-LINE

Recommended Materials:

• The Trade Network Game: Demonstration Software (GA Learning) (html)

• * David F. Batten, Chapter 2:"On the Road to Know-Ware" (pdf preprint-no figures,224K), in Discovering Artificial Economics: How Agents Learn and Economies Evolve, Perseus Books, Westview Press, 2000. ONLINE

• * Andy Clark, Chapter 1: "Autonomous Agents: Walking on the Moon" (pp. 11-33), Chapter 3: "Mind and World: The Plastic Frontier" (pp. 53-69), and Chapter 4: "Collective Wisdom, Slime-Mold Style" (pp. 71-84), op. cit..

• * Leigh Tesfatsion, "Notes on Clark Chapter 1" (html), "Notes on Clark Chapter 3" (html), and "Notes on Clark Chapter 4" (html). ON-LINE

• * Dave Cliff and Janet Bruten, "Shop 'Til You Drop II: Collective Adaptive Behavior of Simple Autonomous Trading Agents in Simulated `Retail' Markets" (pdf,257K). ON-LINE
  Can computational traders outperform human traders in certain types of markets? If so, what types, and how much "intelligence" does it take?

  * Dhananjay K. Gode and Shyam Sunder, "Allocative Efficiency of Markets with Zero-Intelligence Traders: Markets as a Partial Substitute for Individual Rationality" (pdf,1.4M), Journal of Political Economy, Vol. 101, No. 1, 1993, 119-137.

  Jeffrey O. Kephart, James E. Hanson, and Amy R. Greenwald, "Dynamic Pricing by Software Agents", Computer Networks, Special Issue on Trends and Research in e-Commerce, Vol. 32(6), 2000, pp. 731-752. ON-LINE

  Douglass C. North, "Economics and Cognitive Science" (pdf,18K), Working Paper, Washington University at St. Louis, 1996. ON-LINE

  This paper focuses on a key unresolved puzzle (also addressed by Andy Clark): How do humans evolve "scaffolding" (internal belief systems and external institutions) to reduce the uncertainty coming from the strategic interaction of human beings in economic, political, and social market situations? Douglass North is the 1993 recipient of the Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel.

Other source materials related to learning

V. Financial Market Illustrations

Key In-Class Discussion Topics:

• What makes financial assets/markets special?
• What is the "efficient markets hypothesis (EMH)"?
• Do sophisticated (informed) traders necessarily drive "noise traders" from financial markets?
• How should the market mechanisms governing financial asset trading be modelled?
• How should the learning processes of financial traders be represented?
• How should the "fitness" of financial traders be measured? What drives the co-evolution of these fitnesses over time?
• What are the basic "empirical stylized facts" for financial markets? How well are these stylized facts captured by standard financial models? By agent-based financial models?

Required Readings:

• ** Leigh Tesfatsion, "The Santa Fe Artificial Stock Market: Overview" (pdf,69K) ON-LINE

• ** Rob Axtell, "ACE Financial Market Modeling" (pdf,82K), presented at the VII Trento Summer School, July 2006. ON-LINE

Recommended Materials:

• Santa Fe Stock Market Demonstration Software (html,46K)

• * David F. Batten, Chaper 7: "Coevolving Markets" (pdf preprint-no figures,247K), in Discovering Artificial Economics: How Agents Learn and Economies Evolve, Perseus Books, Westview Press, 2000. ON-LINE

• * Blake LeBaron, "Building the Santa Fe Artificial Stock Market" (pdf,19pp,126K), Working Paper, Brandeis University, June 2002. ON-LINE
  Abstract: This brief summary provides an insider's look at the construction of the Santa Fe Artificial Stock Market (ASM) model. The perspective considers the many design questions that went into building the model from the perspective of a decade of experience with agent-based financial markets. The model is assessed based on its overall strengths and weaknesses.

  * Leigh Tesfatsion, "Stock Market Basics" (pdf,277K). ON-LINE

  * Leigh Tesfatsion, "Rational Expectations, the Efficient Market Hypothesis, and the Santa Fe Artificial Stock Market Model" (pdf,856K). ON-LINE

  * Leigh Tesfatsion, "Detailed Notes on the Santa Fe Artificial Stock Market Model" (html). ON-LINE

Other source materials related to ACE financial modeling

VI. Economic Interaction on Fixed Networks

Key In-Class Discussion Topics:

• What might be inferred from the observation by Craig Reynolds that "a flock is not a big bird"?
• Distinguishing between "simple" and "complex" economic systems
• Under what circumstances can robust point predictions of economic outcomes be obtained from a knowledge of initial economic structure, ignoring network effects? And when might network effects be important for the prediction of economic outcomes?
• How can graph theory be used to quantitatively represent and analyze economic interaction networks?
• What type of systematic phase transition do random graphs undergo as their connectivity increases?
• Do socioeconomic networks exhibit any kind of systematic phase transition as their connectivity increases?
• Why all the recent excitement about "small-world networks" (locally dense networks with global reach)?

Required Readings:

• ** Leigh Tesfatsion, "Introductory Notes on the Structural and Dynamical Analysis of Networks" (pdf,2.3M). On-LINE
  NOTE: These presentation slides summarize and graphically illustrate key points from the "Introduction to Networks" notes linked below.

  ** Leigh Tesfatsion, "Notes on Wilhite (2001)" (pdf,236K). ON-LINE

  NOTE: These presentation slides summarize key points from the article by Wilhite (2001), linked below.

  ** Leigh Tesfatsion, "Introduction to Networks" (html). ON-LINE

  Abstract: These notes provide definitions for basic structural characterizations of networks (e.g., degree, clustering, path length). Also discussed are phase transitions in random graphs, the concept of a "small world network," and the possible application of small-world networks to the study of trade interactions. The Key references are Batten, Chapter 3, op. cit. and Wilhite (2001), both linked below.

  ** David F. Batten, Chapter 3: "Sheeps, Explorers, and Phase Transitions" (pdf preprint-no figures,203K), in Discovering Artificial Economics: How Agents Learn and Economies Evolve, Perseus Books, Westview Press, 2000. ON-LINE

  ** Allen Wilhite (2001), "Bilateral Trade and `Small-World' Networks," Computational Economics (pdf,181K), Vol. 18, No. 1, August, pp. 49-64. The published article is also available at SpringerLink. ON-LINE

  Abstract: Wilhite develops an agent-based computational model of a bilateral exchange economy. He uses this model to explore the consequences of restricting trade to different types of networks, including a "small-world network" with both local connectivity and global reach. His key finding is that small-world networks provide most of the market-efficiency advantages of completely connected networks while retaining almost all of the transaction cost economies of locally connected networks.

Recommended Materials:

• Steven H. Strogatz, "Exploring Complex Networks" Nature (pdf,589K), Volume 410, 8 March 2001. ON-LINE
  Abstract: This article includes a summary of Strogatz's work with Duncan Watts on "small-world networks" that has started a major new field of research within network theory.

Other source materials related to ACE network research

VII. Endogenous Network Formation: Labor Market Illustration

Key In-Class Discussion Topics:

• In what economic situations are interactions determined randomly over time?
• In what economic situations are interactions determined preferentially over time by choice and refusal of trade partners based on past experiences?
• What difference might it make if econonomic interactions are randomly versus preferentially determined?
• A labor market study illustrating preferential network formation among workers and employers with learning capabilities
• Representation and visualization of network formation: How should it be done?

Required Readings:

• ** Leigh Tesfatsion, "Notes on Network Formation" (pdf,406K). ON-LINE

• ** Leigh Tesfatsion, "Illustrative Application: Labor Institutions and Market Performance" (pdf,117K) ON-LINE

Recommended Materials:

• The Trade Network Game: Demonstration Software (Network Formation) (html)

• * David F. Batten, Chapter 4:"The Ancient Art of Learning by Circulating" (pdf preprint - no figures, 167K), in Discovering Artificial Economics: How Agents Learn and Economies Evolve, Westview Press, Boulder, Colorado, 2000, plus Leigh Tesfatsion, "Notes on Batten Chapter 4, Plus Glossary of Terms" (html). ON-LINE

• * Andy Clark, Chapter 9: "Minds and Markets" (pp. 179-192).

• * Leigh Tesfatsion, "Notes on Clark Chapter 9" (html). ON-LINE

Other source materials ACE labor research

General resource site on network formation

VIII. Real-World Application: Electricity Restructuring Project

Key In-Class Discussion Topics:

• How are U.S. wholesale power markets currently being restructured?
• How might ACE frameworks be used to test the reliability of the designs being proposed for restructured wholesale power markets?

Required Readings:

• ** Leigh Tesfatsion, "Dynamic Testing of Wholesale Power Market Designs: An Open-Source Agent-Based Framework" (pdf,544K). ON-LINE
  NOTE: The above presentation, to be given in class, constitutes the required reading. The working paper (Sun and Tesfatsion, 2006) on which this presentation is based can be accessed at here (pdf, 1.89MB).

Other source materials on ACE electricity research

General resources on electricity restructuring

IX. Empirical Validation of ACE Models

Key In-Class Discussion Topics:

• How might ACE market design models be empirically validated?
• How might other types of ACE models be empirically validated?

Required Readings:

• ** Leigh Tesfatsion, "Notes on the Empirical Validation of ACE Models" (pdf,176K).

Recommended Materials:

• * Olivier Barreteau et al. (2003), "Our Companion Modelling Approach" (html,8pp.), Journal of Artificial Societies and Social Simulation (JASSS), Vol. 6, No. 1, 31 March 2003. ON-LINE (electronic journal)
  Abstract: This article discusses an iterative participatory approach to the modeling of complex systems, referred to as "companion modeling." The companion modeling approach envisions multidisciplinary researchers and stakeholders engaging together in repeated looping through a four-stage cycle: field study and data analysis; role-playing games; agent-based model design and implementation; and intensive computational experiments. The new aspect of companion modeling relative to other participatory modeling approaches is the emphasis on modeling as an open-ended collaborative learning process. The modeling objective is to help stakeholders manage complex problems over time through a continuous learning process rather than to attempt the delivery of a definitive problem solution.

  * Giorgio Fagiolo, Paul Windrum, and Alessio Moneta, "Empirical Validation of Agent-Based Models: A Critical Survey" (pdf,435K), LEM Working Paper 2006/14, Laboratory of Economics and Management, Sant'Anna School of Advanced Studies, Pisa, Italy, May 2006. ON-LINE

  Abstract: This paper addresses the problem of finding the appropriate method for conducting empirical validation of ACE models. The paper has two primary objectives: (1) to identify key issues facing ACE researchers engaged in empirical validation; and (2) to critically appraise the extent to which alternative approaches deal with these issues.

Other source materials on the empirical validation of ACE models

Appendix: Possible Course Project Topic Areas
(with Linked Resource Sites)

Important Note: Participants in the VII Trento Summer School are strongly encouraged to begin consideration of possible course project topics as soon as possible. Please visit the Course Project Information Site for more detailed information regarding course projects, including a list of course projects selected by students in my ACE course (Econ 308) at Iowa State University in previous years. For those wishing to explore course project topics not included at this site, a more general collection of possible topic areas (with linked resource sites) is provided below.

• Introduction to the ACE Methodology

• Agent-Based Computational Laboratories

• Learning and the Embodied Mind

• Evolution of Economic Institutions and Social Norms

• Evolution of Economic Networks

• Industrial Organization

• Technological Change and Economic Growth

• Financial Market Issues

• Agricultural and Natural Resource Issues

• Labor Market Issues

• Restructured Electricity Markets

• Design of Auctions and Other Market Forms

• Automated Markets

• Organization of Firm and Markets

• Multi-Market Modeling

• Network, Path-Dependence, and Lock-In Effects

• Political Economy

• Parallel Experiments with Real and Computational Economic Agents

• Empirical Validation of Agent-Based Computational Models

Copyright © 2006 Leigh Tesfatsion. All Rights Reserved.