Syllabus for Economics 308

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

Last Updated: 16 April 2014

Latest Course Offering: Spring 2009
Meeting Time: TR 11-12:20
Meeting Place: East Hall 111

Leigh Tesfatsion
Professor of Econ, Math, and ECpE
Department of Economics
Iowa State University
Ames, Iowa 50011-1070
tesfatsi AT

Office Hours:
Heady 375, Thursdays 12:30-3:40pm and by appointment

Econ 308 Homepage
Exam Policy Info
Exercise Policy Info
Course Project Info
ACE Website
ACE Demo Software
Course Overview
Topics, Readings, and Exercises

The Web

Course Overview

Modern economies are complex systems that can sometimes go awry --- witness the current financial crisis! How to get a handle on this complexity?

One approach is to model an economy computationally as a "virtual world" populated by interacting "agents." These agents can include people, social groupings, institutions, and/or biological and physical entities.

The developer of the virtual world specifies the initial states of the agents comprising the economy. One objective might be to study current empirical conditions. Another objective might be to study hypothetical conditions of interest in order to see what happens. Once the initial agent states are set, the virtual world runs forward in time driven by agent interactions, much like a bacteria culture grows in a laboratory petri dish.

Econ 308 introduces students to this exciting new virtual-world methodology for the study of economic systems. Tentatively scheduled course topics include:

As indicated at the following site, agent-based modeling is now supporting scientific research and technology for a wide variety of commercial applications:
50 Facts About Agent-Based Modeling (pdf,6M)

Topics, Readings, and Exercise Assignments


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. Some modifications to the required and/or recommended readings might be made as the course proceeds. Any such modifications will be announced in class and will be marked on the on-line syllabus with a "new" or "updated" icon for at least one week after the modification is made.

  1. Introduction
    1. What are Complex Adaptive Systems (CAS)?
    2. What is ACE?
    3. Hands-On Introduction to Agent-Based Computational Modeling
  2. The Complexity of Decentralized Market Economies
    1. Basic Market Concepts
    2. Market Games
  3. Learning and the Embodied Mind
    1. Illustrative Examples of Situated Learning
    2. Learning Representations
  4. Application: U.S. Electric Power Market Restructuring
  5. Application: Financial Markets
  6. Interaction on Fixed Networks
  7. Formation of Interaction Networks
  8. Empirical Validation of ACE Models

Appendix: General Course Project Information

I. Introduction

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

Key In-Class Discussion Topics:


** Exercise 1 (Individual): Introduction to the Schelling Segregation Model (pdf,28K), Due: Thursday, January 22nd, 11:00am.

** Exercise 2 (Team): So how do YOU think segregation should be measured? (pdf,27K), Due: Tuesday, February 3, 11:00am. Exercise team assignments can be accessed here.

Important Note: Please note that, as indicated at the top of the exercise assignment, late assignments will not be accepted -- no exceptions. An assignment is late if it is turned in after discussion of the exercise answers has commenced on the due date. If you cannot attend class on the due day, either give your exercise to a classmate for turning in or put your exercise under the instructor's office door (Heady 375) no later than 10:45am on the due date. Do not leave exercises in mailboxes or send them via email except by pre-arrangement, since they might not be received in time.

Required Readings:

Recommended Materials:

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

Key In-Class Discussion Topics:

  • What is ACE all about?
  • Illustrative example

Required Readings:

  • ** Leigh Tesfatsion, "A Brief Introduction to ACE" (pdf,178K). ON-LINE/CLASS PRESENTATION

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), a more advanced discussion of ACE presented at the VII Trento Summer School on ACE, July 2006. ON-LINE

  • * Duncan K. Foley, Chapter 1: "Introduction (pp. 23-72) (pdf,369K), in Peter S. Albin and Duncan K. Foley (Eds.), Barriers and Bounds to Rationality: Essays on Economic Complexity and Dynamics in Interactive Systems, Princeton Studies in Complexity, Princeton University Press, NJ, 1998, posted with permission of Princeton University Press.
    Abstract: Pages 23-72 of this wide-ranging introductory chapter by a seminal contributor to economic complexity theory covers the following topics: Economic complexity puzzles; economic models of fully rational behavior; definitions and measures of complexity; complexity in cellular automata; modeling of complex social and economic interactions; complexity, rationality, and social interaction; and towards a robust theory of action and society.

Other introductory source materials on CAS/ACE

I.C Hands-On Introduction to Agent-Based Computational Modeling

Key In-Class Discussion Topics:

  • What is "object-oriented programming (OOP)"?
  • What's the difference between an "object" and an "agent"?
  • Availability of software modeling tools for Agent-Based Modeling (ABM)
  • Which ABM software modeling tools are best for you?
  • Template ABM models for getting started
  • Should you use an Integrated Development Environment (IDE)?


** Exercise 3 (Individual - Pass/Fail): "Hands-On Introduction to Agent-Based Modeling" (pdf,27K), Due: Tuesday, February 17, 11:00am.

Required Readings:

  • ** Leigh Tesfatsion, "Introduction to Agent-Oriented Programming" (pdf presentation,110K). ON-LINE/CLASS PRESENTATION
    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 applications can be implemented via computational laboratories, using the Trade Network Game (TNG) Laboratory for concrete illustration.

  • ** 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

Recommended Materials:

  • * The Trade Network Game Lab (C++/VB, open source), demonstration software for market games, trade network formation, & GA learning, by McFadzean, Stewart, and Tesfatsion (homepage)

  • * Sugarscape (MASON/Java, open source), Epstein/Axtell sugar & spice trading game, implemented by Tony Bigbee (homepage)

  • * Matt Weisfeld, "Introduction to Object-Oriented Concepts" Chapter 1 in The Object-Oriented Thought Process, SAMS Books, Macmillan, Second Edition, 2003. HAND-OUT

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

  • * Steven F. Railsback, Steven L. Lytinen, and Stephen K. Jackson, StupidModel: A Template Model for ABM Platforms (html).
    Site Description: The "StupidModel" template is implemented in five different platforms: NetLogo; RepastJ; MASON; Java Swarm; and Objective C Swarm. Although relatively simple, StupidModel includes many commonly used features of agent-based modeling (ABM) platforms. Sixteen versions of StupidModel are implemented for each platform, beginning with a bare bones version and ending with a relatively sophisticated version that involves two agent types, a full agent life cycle (birth, reproduction, predation, and death), and a habitat with data read from an input file. Each implementation is made available as freeware with accompanying implementation notes. The authors include at this site a concise description of the basic StupidModel Formulation that takes the reader step by step through the 16 template versions. In addition, the authors provide a pointer to a paper titled "Agent-Based Simulation Platforms: Review and Development Recommendations" (Simulation, Vol. 82, No. 9, 2006) that reviews and compares the five ABM platforms and seeks to identify key development priorities both for these specific ABM platforms and for ABM platforms in general.

  • * Important Update to Railsback et al.
    Alan G. Isaac, "The ABM Template Models: A Reformulation with Reference Implementations" (html), Journal of Artificial Societies and Social Simulation 14 (2) 5, March 2011.
    Abstract: The author refines the Railsback et al. template models for agent-based modeling and offers new reference implementations. He also addresses some issues of design, flexiblility, and ease of use that are relevant to the choice of an agent-based modeling platform.

  • * William Rand, Agent-Based Modeling Platforms: A Practical Introduction (pdf,4.9MB), presented at ISU, January 30, 2007.

  • ABM General Software and Toolkits

  • ABM Computational Laboratories and Demonstration Software

II. Complexity of Decentralized Market Economies

II.A Basic Market Concepts

Key In-Class Discussion Topics:

  • Modeling decentralized market economies
  • Key types of market players
  • Key types of market structures
  • Construction of demand and supply functions
  • Competitive vs. Strategic Pricing


** ANSWER OUTLINE for Exercise 4 (Individual, 20 Points): "Competitive Versus Strategic Pricing" (pdf,123K), Due: Tuesday, February 24, 11:00am.

Required Readings:

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

  • ** Leigh Tesfatsion, "Market Basics for Price-Setting Agents" (pdf,422K). ON-LINE

  • ** Leigh Tesfatsion, "Modeling Behavior, Learning, and Interaction Networks in Dynamic Market Economies: An Agent-Based Computational Approach" (pdf,325K). ON-LINE/CLASS PRESENTATION

  • ** Leigh Tesfatsion, "Illustrations of Demand & Supply Schedule Construction" (pdf,168K). ON-LINE/CLASS PRESENTATION

Recommended Materials:

  • The Alliance for Innovative Manufacturing (AIM) at Stanford University maintains How Everyday Things Are Made, (html), a fascinating site that provides manufacturing video (virtual factory tours) covering the complicated intricately-coordinated manufacturing processes for over forty types of common products (jelly beans, cars, planes, chocolate, glass bottles, etc.).

ACE-Related Research on Multi-Market Modeling

I.B Market Games

Key In-Class Discussion Topics:

  • Basic game theory concepts
  • Market games among multiple learning traders
  • Can market structure substitute for trader rationality?


** Exercise 5 (Team/Individual, 14 Points): "Zero-Intelligence Market Trading Exercise" in three versions corresponding to three different agent-based toolkits, as follows:

  1. MASON version (25K)
  2. RepastJ version (38K)
  3. NetLogo version (25K)
See the following site for Ex 5 Team Assignments Exercise 5 is Due: Tuesday, March 10, 11:00am.

Required Readings:

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

  • ** Leigh Tesfatsion, "ACE Market Game Examples" (pdf,289K). ON-LINE/CLASS PRESENTATION

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

  • ** John Duffy, Notes on Gode-Sunder Zero-Intelligence Traders (pdf,719K). ON-LINE/CLASS PRESENTATION

Recommended Materials:

  • * Zero-Intelligence Trading Demo (NetLogo), by Mark McBride (homepage) ON-LINE

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

III. Learning and the Embodied Mind

III.A Illustrative Examples of Situated Learning

Key In-Class Discussion Topics:

Exercises: TBA

Required Readings:

Recommended Materials:

III.B Learning Representations

Key In-Class Discussion Topics:

Take-Home Exercises:

Required Readings:

Recommended Materials:

Other source materials related to learning

IV. Application: U.S. Electric Power Market Restructuring

Key In-Class Discussion Topics:

Required Readings:

Recommended Materials:

Other source materials related to ACE Electricity Research

General resources on electricity restructuring

V. Application: Financial Markets

Key In-Class Discussion Topics:

Required Readings:

Recommended Materials:

Other source materials related to ACE financial modeling

VI. 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,2.3MB). ON-LINE/CLASS PRESENTATION
    NOTE: These presentation slides summarize and graphically illustrate key points from the "Introduction to Networks" notes linked below.

  • ** Leigh Tesfatsion, "Introduction to Networks" (html). ON-LINE
    Abstract: These notes provide rigorous definitions for basic structural characterizations of networks (e.g., degree, clustering, shortest 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, 2000) and Wilhite (2001), both linked below.

  • ** Leigh Tesfatsion, "Notes on Wilhite (2001)" (pdf,236K). ON-LINE/CLASS PRESENTATION
    NOTE: These presentation slides summarize key points from the article by Wilhite (2001), linked below.

  • ** Allen Wilhite (2001), "Bilateral Trade and `Small-World' Networks" (pdf,181K), Computational Economics, 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 in which profit-seeking traders sequentially engage in trade partner search, negotiation, and trading. 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:

  • * Wilhite Small-World Trade Network Demo (NetLogo), by Mark McBride (homepage)

  • * 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

  • * Steven H. Strogatz, Exploring Complex Networks (pdf,589K), Nature, Vol. 410, 8 March 2001, pp. 268-276.

  • maintains an intriguing site devoted to the visual exploration of real-world complex networks. ON-LINE

Other source materials related to ACE network research

VII. Formation of Interaction Networks

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,246K). ON-LINE/CLASS PRESENTATION

Recommended Materials:

  • * The Trade Network Game (TNG) Laboratory (C++/VB, open source), includes run-time visualization of trader network formation, by McFadzean, Stewart, and Tesfatsion (homepage)

  • * Albert-László Barabási, "Network Overview" (pdf,70M), 2006 Keynote Address. (Caution: Large download)
    Professor Albert-László Barabási (Department of Physics, Notre Dame, Indiana) directs a research group focusing on the emergence and evolution of networks in various contexts (e.g., metabolic and genetic networks, actor networks, collaborative networks). This fun slide presentation provides a vivid visual summary of some of their key findings to date.

  • * 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

Other source materials related to ACE labor research

General resource site on network formation

VIII. Empirical Validation of ACE Models

Key In-Class Discussion Topics:

  • Verification for ACE models: How to verify an ACE model is carrying out operations in the way the modeler intends?
  • [G.E.P. Box (1979)]: "All models are wrong, but some are useful." Must the intended purpose of a model be known before meaningful empirical validation can proceed?
  • Empirical validation for ACE models: input validation (operational validity), descriptive output validation, and predictive output validation
  • What is iterative participatory modeling (companion modeling)?

Required Readings:

  • ** Leigh Tesfatsion, "Notes on the Empirical Validation of ACE Models" (pdf,176K). ON-LINE/CLASS PRESENTATION

Recommended Materials:

  • * Paul Windrum, Giorgio Fagiolo, and Alessio Moneta, Empirical Validation of Agent-Based Models: Alternatives and Prospects (html), Journal of Arificial Societies and Social Simulation, Vol. 10, no. 2,8, March 31, 2007.
    Abstract: This paper addresses the problem of finding the appropriate method for conducting empirical validation in ACE models. The paper has two primary objectives: (1) to identify key issues facing ACE economists 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: General Course Project Information

Students are strongly encouraged to begin consideration of possible course project topics as soon as possible.

Please visit the Course Project Information Site for detailed information regarding course projects, including a list of course projects selected by Econ 308 students in previous years. I am available during office hours, by appointment, and anytime by email to provide guidance if desired.

Preliminary outlines for student project proposals must be turned in to the instructor during the first week following Spring break and must receive go-ahead instructor approval by the end of March. Final write-ups for student project reports are due the last day of class.

Copyright © Leigh Tesfatsion. All Rights Reserved.