Syllabus for Economics 308x
Agent-Based Computational Economics (ACE)

Last Updated: 19 May 2003

IMPORTANT NOTE:

Starting in Spring 2004, the experimental ACE course Econ 308x became a regular course offering (Econ 308). The home page for Econ 308 can be accessed at http://www.econ.iastate.edu/classes/econ308/tesfatsion/ and the syllabus for Econ 308 can be accessed at http://www.econ.iastate.edu/classes/econ308/tesfatsion/syl308.htm Please see these Econ 308 sites for all further course updates.

Latest Econ 308x Course Offering: Spring 2003

Meeting Time and Place: TR 2:10-3:25pm, 119 East Hall

Instructor:

Leigh Tesfatsion

Professor of Economics and Mathematics

Department of Economics

Iowa State University

Ames, Iowa 50011-1070

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

tesfatsi@iastate.edu

Office Hours:

Heady 375, TR 3:25-5 and by appointment

Programming Assistant (Computer Demo/Exercise Prep):

Chris Cook (CS/CE)

C_Cook514@hotmail.com

Home Page for Economics 308x

ACE Website

ACE Interactive Computer Demos Site

Course Overview and Topic Outline:

A modern market-based economy is an example of a complex adaptive system, consisting of a decentralized collection of autonomous adaptive agents interacting over time in various market contexts. These massively parallel local interactions give rise to global regularities such as trade networks, socially accepted monies, market protocols, business cycles, and the common adoption of technological innovations. These global regularities feed back in turn into the determination of local interactions.

The recent advent of powerful computational tools, particularly object-oriented and agent-based programming, permits new approaches to the study of this complex two-way feedback between microstructure and macrostructure. The primary objective of this 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 economies modelled as evolving systems of autonomous interacting agents.

1. Introduction
   1. Complex Adaptive Systems and ACE
   2. The Complexity of Decentralized Market Economies
2. Development and Use of Agent-Based Computational Laboratories
3. Learning and the Embodied Mind
4. Financial Market Illustrations
5. Economic Networks
6. Learning and Network Effects: A Deeper Look
7. Labor Market Illustrations

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

Required Textbooks (Bookstore):

• David F. Batten, Discovering Artificial Economics: How Agents Learn and Economies Evolve, Westview Press, Boulder, Colorado, 2000, ISBN: 0-8133-9770-7. CLOSED RESERVE (ECON/SOC READING ROOM, HEADY HALL 368).

• Andy Clark, Being There: Putting Brain, Body, and World Back Together Again, MIT Press, Paperback Edition, 1998, ISBN: 0-262-53156-9. CLOSED RESERVE (ECON/SOC READING ROOM, HEADY HALL 368).

Recommended for Students Interested in Doing Original Programming and/or Experiments as Part of Their Class Project:

• Matt Weisfeld, The Object-Oriented Thought Process, SAMS Publishing (Division of Macmillan), Indianapolis, Indiana, 2000, ISBN: 0-672-31853-9 (paperback).
  This book is designed to help newcomers to object-oriented programming (OOP) to learn guidelines for solid class design, to master the major concepts of inheritance, composition, interfaces, and abstract classes, and to create components to use in building more sophisticated systems. The author motivates and illustrates his points by taking readers step by step through a real-world application -- a simple e-commerce application framework using Java interfaces and abstract classes.

  ACE Interactive Computer Demos Site

  The demos accessible at this site include both ACE demos and general complex adaptive systems (CAS) demos. The purpose of this site is to facilitate the understanding of the ACE/CAS methodology by permitting students to obtain hands-on experience running simple ACE/CAS experiments under different parameter settings with no original programming required and with rapid visual feedback of findings. The demos linked to date include: the Trade Network Game (TNG); the Santa Fe Artificial Stock Market (ASM); Avalanche (a value supply chain model); the Schelling Segregation Model; various NetLogo demos supplied by community users; cellular automata demos; Tierra; some of Karl Sim's interactive demos; and a variety of other artificial life demos.

  ACE-Related Computational Laboratories and Software

  This site includes a wide variety of links to programming language resources and tools currently in use for agent-based computational modeling.

Course Activities:

• Instructor and student-moderated discussions of assigned readings and materials
• Survey and demonstrations of available computational tools suitable for ACE applications
• Hands-on experience using computational laboratories to conduct ACE experiments
• In-class presentations of student projects

Topic Coverage

PLEASE NOTE: Required readings are marked below with two asterisks **. Readings marked with a single asterisk * are recommended but not required. Readings designated as "Closed Reserve" are on closed reserve at the Economics and Sociology Reading Room in Heady Hall, Room 368. They can be signed out during the day for two hour periods or signed out at the end of the day for overnight use. The Reading Room has a xerox machine for the general use of library patrons.

I.Introduction

A. Complex Adaptive Systems and ACE

Key In-Class Discussion Topics:

• What is a complex system? a complex adaptive system?
• What is ACE all about?
• Illustrative examples (Bak's Sand Pile Model, Schelling's Segregation Model, the Trade Network Game,...)

Take-Home Exercise:

• ** Team Exercise 1: Introduction to the Schelling Segregation Model (pdf,28K)
  Due Jan 30. Reference Readings: Batten (Chapter 1) and Schelling (Chapter 4 of Micromotives and Macrobehavior), HAND-OUTS

Required and Recommended Readings:

• ** David Batten (text), Preface (pp. xiii-xv) and Chapter 1: "Chance and Necessity" (pp. 1-43), plus L. Tesfatsion, "Notes on Batten Chapter 1" (html). HAND-OUTS
• ** Leigh Tesfatsion, "Agent-Based Computational Economics: Growing Economies From the Bottom Up" (pdf,216K), Artificial Life, Volume 8, No. 1, 2002, 55-82. HAND-OUT (ABBREVIATED VERSION)
• ** Andy Clark (text), Preface: "Deep Thought Meets Fluent Action" (pp. xi-xiii) and "Introduction: A Car with a Cockroach Brain" (pp. 1-8)
• ** 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.] HAND-OUT
• ** Leigh Tesfatsion, "Comparing and Contrasting Bak's Sand Pile Model and the Schelling Segregation Model" (pdf,56K), Department of Economics, ISU, January 9, 2003. HAND-OUT

• * Sunny Y. Auyang, "Synthetic Analysis of Complex Systems I - Theories" (html), circa 2001.
  In this interesting essay, Auyang examines definitions of complex systems proposed by researchers from various disciplines and argues that these definitions share important common elements. One important distinction not stressed by the author, however, is that living agents in social and biological systems (as opposed to inanimate elements in purely physical systems) can exert some degree of anticipatory preferential control over the formation and evolution of their relational networks.]

  * Jonathan Rauch, "Seeing Around Corners" (html), The Atlantic Monthly, April 2002, pp. 35-48.

  Rauch surveys early work on the computational modeling of artificial societies. Interested readers can also view animations in QuickTime format of some of the artificial societies discussed in Rauch's article.

  * Leigh Tesfatsion, "Powerpoint ACE Tutorial" (1697K,ppt).

  * Leigh Tesfatsion, "A Trade Network Game with Endogenous Partner Selection," pp. 249-269 in H. M. Amman, B. Rustem, and A. B. Whinston (eds.), Computational Approaches to Economic Problems, Kluwer Academic Publishers, 1997. A pre-print [ (ps,151K) or (pdf,401K)] is available.

  This study develops a Trade Network Game (TNG) framework for studying the interplay between evolutionary game dynamics and preferential partner selection in buyer-seller markets. The TNG consists of successive generations of resource-constrained traders who choose and refuse trade partners on the basis of continually updated expected payoffs, engage in risky trades modelled as two-person games, and evolve their trade strategies over time. For additional information regarding TNG research articles and software, see the TNG Home Page.

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

  * Dan Ashlock (ISU Math Department) has generated fascinating graphical depictions of self-organized criticality in Bak's Sand Pile Model

Other introductory source materials

B. The Complexity of Decentralized Market Economies

Key In-Class Discussion Topics:

• What is a market?
• How do prices get determined in real-world markets?
• What strategic interaction problems arise in real-world markets?
• Taking agent autonomy seriously in market modeling
• Modeling the coevolution of market behaviors and market institutions

In-Class Exercise:

• Bilateral Trade Experiment: An Apple Market

Required and Recommended Readings:

• ** L. Tesfatsion, "Market Basics for Price-Setting Agents" (pdf,80K). HAND-OUT
• ** L. Tesfatsion, "Market Organization with Price-Setting Agents" (html,8K) HAND-OUT
• ** L. Tesfatsion, "Notes on Price Discovery with Price-Setting Agents" (pdf,110K) HAND-OUT
• ** Leigh Tesfatsion, "Game Theory: Basic Concepts and Terminology" (pdf,34K) HAND-OUT

• * Amy R. Greenwald and Jeffrey Kephart, "Shopbots and Pricebots", Sixteenth International Joint Conference on AI, Stockholm, Sweden, August 1999, pp. 506-511. [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.]
• * Truman Bewley, Why Wages Don't Fall During a Recession, Harvard University Press, Cambridge, MA 1999.
• * Alan Blinder, Elie Cenetti, David Lebow, and Jeremy Rudd, Asking About Prices, Rusell Sage Foundation, New York, 1998.

II.Development and Use of Agent-Based 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 is the definition of an agent?
• What is the difference between an object and an agent?
• Do objects in standard OOP applications qualify as agents?
• In what ways might the modeling of social (multiple agent) systems require new software capabilities and features?
• What is meant by an emergent property of a system?
• What is special about emergence in social systems?
• How can agents be designed?
• Running agent-based computational experiments: Basic concepts and terminology
• What criteria can be used to evaluate the performance of agent-based computational models?

Take-Home Exercise:

• ** Team Exercise 2: Running Experiments with the Schelling Segregation Model (pdf,27K)
  Due: February 13, 2003. Reference Materials: Leigh Tesfatsion, "Running ACE Experiments...", (Feb 4th Hand-Out), and Chris Cook, Interactive Computer Demo for the Schelling Segregation Model. The demo can be downloaded from the ACE Interactive Computer Demos Site. In addition, the demo has been installed in the computer lab in Heady 64/68. You can view screen shots here for: the main animation screen, the options screen, the happiness rules setting screen, and the output data screen.

Required and Recommended Readings:

• ** Matt Weisfeld, The Object-Oriented Thought Process, Ibid., Chapter 1: "Introduction to Object-Oriented Concepts" (pp. 8-31), HAND-OUT
• ** Nigel Gilbert and Pietro Terna, "How to Build and Use Agent-Based Models in Social Science" (pdf,27pp), Discussion Paper, May 18, 1999.

  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.] HAND-OUT

• ** Leigh Tesfatsion, "Running ACE Experiments: Basic Concepts and Terminology" (pdf,41K), February 10, 2003.

• * 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. A preprint is available [ (pdf,578K -- text only), (Figures html/gifs,4K)]. HAND-OUT

  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. The TNG Lab is targeted for the Microsoft Windows desktop. The TNG Lab is both modular and extensible and has a clear, easily operated graphical user interface. It permits visualization of the formation and evolution of trade networks by means of real-time animations. Data tables and charts reporting descriptive performance statistics are also provided in real time. The capabilities of the TNG Lab are demonstrated by means of labor market experiments. The latest version of the TNG Lab together with tutorial materials can be downloaded as freeware at the TNG Homepage.

• Leigh Tesfatsion, TNG Lab Parameter List (pdf,19K)

  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.

• A TNG Lab Tutorial was prepared by David McFadzean for presentation at a 1999 conference (GECCO'99). This tutorial is optimized for viewing with Microsoft Internet Explorer.

  Important Clarifying Note: This tutorial describes the basic design features of the TNG Lab. However, at the time this tutorial was prepared, the TNG Lab was motivated in terms of "pure workers," "pure employers," and "worker-employers" (agents who can either work for others or hire agents to work for them), and the animation physics for the network visualization was hardwired. The current version of the TNG Lab -- described in the 2001 IEEE Transactions article linked above -- is motivated more generally in terms of buyers, sellers, and dealers (agents who can buy and/or sell), and it incorporates an animation physics for the network visualization that users can adapt to their particular problem requirements by means of various parameter settings.

• R. H. Sander, D. Schreiber, and J. Doherty, "Empirically Testing a Computational Model: The Example of Housing Segregation" (pdf,53K), UCLA working paper, 2000.

  Using the Swarm simulation environment, this paper constructs a computational model of racial housing segregation that extends the Schelling Segregation Model. Preference functions are derived from empirical data on neighborhood racial composition and from a variety of other factors conjectured to be important in housing decisions. The model is used to examine the contemporary debate about the nature and causes of housing segregation.

• Paul M. Torrens, "New Tools for Simulated Housing Choices" (pdf,943K), presented at the Special Fannie Mae Foundation Session: Housing and the New Economy, Washington, D.C., May 2001.

  This paper presents a framework for urban geographic simulation that infuses approaches derived from geocomputation and complexity (e.g, Schelling-type cellular automata multi-agent models) with standard techniques that have been tried and tested in operational land-use and transport simulation.

Other source materials related to computational laboratories

III. Learning and the Embodied Mind

Key In-Class Discussion Topics:

• 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 automated market agents (e.g., pricebot sellers, shopbot buyers) necessarily mimic the cognitive processes of real people?
• Illustrative learning representations (e.g., reinforcement learning, Q-learning, genetic algorithms (GAs), GA-classifier systems, artificial neural networks, ...)
• How do people in multi-agent decision contexts make trade-offs between selfishness and a concern for fair play?
• Are "minds" 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)?
• What can be inferred from Rodney Brooks' observation that "elephants don't play chess"?

In-Class Experiments/Exercises:

• "So How Do You Play the Iterated Prisoner's Dilemma Game?"
• "Constructing computational economic agents who learn" (pdf,48K)

Take-Home Exercises:

• ** Team Exercise 3: Conducting Experiments with a Two-Stage Beer Production Game (pdf,44K)
  Due: March 4, 2003. Reference Materials: Leigh Tesfatsion, "Running ACE Experiments...", (Feb 4th Hand-Out) and "Game Theory: Basic Concepts and Terminology (Feb 18th Hand-Out), plus assigned Section III readings by Batten (Chapter 2), Sigmund et al., and Hofstadter.

  ** Exercise 4: Interim Course Project Reports (pdf,8K), due Thursday, April 3, 2:10pm. HAND-OUT

Required and Recommended Readings:

• ** David Batten (text), Chapter 2: "On the Road to Know-Ware" (pp. 45-79) HAND-OUT
• ** Douglas Hofstadter, "Computer Tournaments of the Prisoner's Dilemma Suggest How Cooperation Evolves", Scientific American May 1983, 18-26, plus Leigh Tesfatsion, "Notes on Axelrod's IPD Tournaments" (pdf,36K). HAND-OUTS
• ** Leigh Tesfatsion, "Notes on Learning (pdf,165K). HAND-OUT
• ** Melanie Mitchell, "Genetic Algorithms: An Overview", Complexity, Volume 1, Number 1, 1995, pp. 31-39. [Introduction to genetic algorithms (GAs) useful for understanding the GA-classifier form of agent learning in the Santa Fe Stock Artificial Stock Market covered in the next section of the course.] HAND-OUT
• ** Karl Sigmund, Ernst Fehr, and Martin A. Nowak, "The Economics of Fair Play", Scientific American Vol. 83, January 2002, 83-87, plus Leigh Tesfatsion, "Notes on Sigmund, Fehr, and Nowak" (pdf,23K). HAND-OUTS
• ** Andy Clark (text), Chapter 1: "Autonomous Agents: Walking on the Moon" (pp. 11-33), plus Leigh Tesfatsion, "Notes on Clark Chapter 1" (html).
• ** Andy Clark (text), Chapter 2: "The Situated Infant" (pp. 35-51), plus Leigh Tesfatsion, "Notes on Clark Chapter 2" (html).
• ** Andy Clark (text), Chapter 3: "Mind and World: The Plastic Frontier" (pp. 53-69), plus Leigh Tesfatsion, "Notes on Clark Chapter 3" (html).
• ** Andy Clark (text), Chapter 4: "Collective Wisdom, Slime-Mold Style" (pp. 71-84), plus Leigh Tesfatsion "Notes on Clark Chapter 4" (html).

• * W. Brian Arthur, "Inductive Reasoning and Bounded Rationality: The El Farol Problem" (html) American Economic Review (Papers and Proceedings) 84, 1994, 406-411. [This is the seminal paper on the El Farol Problem discussed by Batten in Chapter 2.]
• * I. K Gurney (University of Sheffield, U.K) maintains a set of online notes on "Artificial Neural Networks" (html) useful for gaining a deeper understanding of the materials covered in Clark Chapter 3.
• * 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.
  This paper studies the potential impact on prices of the increasingly widespread reliance 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. It also studies the price dynamics that might ensue from various mixtures of automated agents, the potential use of machine learning algorithms to improve profits, and more generally the interplay among learning, optimization, and dynamics in agent-based information economies.

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

  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

IV. 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 and Recommended Readings:

• ** David Batten (text), Chapter 7: "Coevolving Markets" (pp. 209-245) HAND-OUT
• ** Leigh Tesfatsion, "Notes on the Santa Fe Artificial Stock Market Model (html), Department of Economics, ISU, April 20, 2003. HAND-OUT

• * Blake LeBaron, "Building the Santa Fe Artificial Stock Market" (pdf,19pp,126K), Working Paper, Brandeis University, June 2002.
  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, "Introduction to Financial Markets" (html), Department of Economics, ISU, March 3, 2002.

  * Leigh Tesfatsion, "Information, Bubbles, and the Efficient Markets Hypothesis" (html), Department of Economics, ISU, March 18, 2002.

Other source materials related to ACE financial modeling

V. Economic 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)?
• How do agents learn in networks? Does learning through interaction necessarily promote cooperation?
• Is economic interaction a catalyst for economic change? In particular, do economic networks exhibit "autocatalytic" properties?
• Empirical Issue: Is the global economy becoming strongly interactive? If so, is this a good thing?
• Empirical Issue: Will the recent creation of a single euro currency zone among twelve European countries have any important network effects?

Required and Recommended Readings:

• ** David Batten (text), Chapter 3: "Sheep, Explorers, and Phase Transitions" (pp. 81-115), plus L. Tesfatsion, "Notes on Batten Chapter 3" (html). HAND-OUTS
• * Steven H. Strogatz, "Exploring Complex Networks" Nature, Volume 410, 8 March 2001. [Includes a summary of his work with Duncan Watts on "small-world networks" that has started a major new field of research within network theory.] HAND-OUT

• * David Batten (text), Chapter 4: "The Ancient Art of Learning by Circulating" (pp. 117-138), plus L. Tesfatsion, "Notes on Batten Chapter 4" (html). HAND-OUTS
• * Craig Reynolds (Sony, Research and Development Group) maintains a web site titled Boids featuring his simulations of flocking creatures called "boids." His basic flocking model consists of three simple steering behaviors possessed by each individual boid that govern how each boid maneuvers itself based on the positions and velocities of its nearby flockmates. As illustrated by the Java applets at this site, the model results in amazingly life-like collective flocking dynamics. Also available at this site is a pointer to work by Reynolds on an interactive flocking model.
• * Andy Clark (text), Chapter 5: "Evolving Robots" (pp. 87-102)
• * Andy Clark (text), Chapter 6: "Emergence and Explanation" (pp. 103--128)

Other source materials related to networks

VI. Learning and Network Effects: A Deeper Look

Key In-Class Discussion Topics:

• What role does "scaffolding" (e.g., external institutions, policies, and protocols) play in supporting "intelligent" market outcomes?
• Under what circumstances might learning/network effects prevent market structure (and other forms of scaffolding) from being a reliable predictor of market efficiency and market power?
• What potential roles do increasing returns, path-dependence, and lock-in play in economic processes? How empirically important are these effects?
• How can network effects be represented in ACE models (e.g., using cellular automata)?
• How can ACE models be used to rigorously test hypotheses about learning/network effects?

Required and Recommended Readings:

• ** Andy Clark (text), Chapter 9: "Minds and Markets" (pp. 179-192), plus L. Tesfatsion, "Notes on Clark Chapter 9" (html). HAND-OUTS

• * Andy Clark (text), Chapter 10: "Language, The Ultimate Artifact" (pp. 193-218)
• * David Batten (text), Chapter 5: "Networks, Boosters, and Self-Organizing Cities" (pp. 139-175, stress on pp. 170-175)
• * David Batten (text), Chapter 8: "Artificial Economics" (pp. 247-268)

Other source materials (edited volumes of readings)

VII. Labor Market Illustrations

Key In-Class Discussion Topics:

• To what extent are job search and matching outcomes among workers and employers determined by labor institutions? by learning/network effects?
• To what extent are worksite relations among workers and employers over time determined by labor institutions? by learning/network effects?
• To what extent are market efficiency and market power relations in labor markets determined by labor institutions? by learning/network effects?
• How do cultural norms affect labor market processes? For example, to what extent are social networks important for job search and matching in different cultures?
• Empirical Conundrums: For example, what explains the large variance in wage earnings and in employment histories across observationally equivalent workers?

Required and Recommended Readings

• ** Leigh Tesfatsion, "Concentration, Capacity, and Market Power in an Evolutionary Labor Market" (pdf,344K), pages 1033-1040 in Evolution at Work for the New Millenium, Proceedings of the 2000 Congress on Evolutionary Computation, Volume II, IEEE, Inc., N.J., 2000, pages 1033-1040. [This conference paper provides a summary overview of the detailed experimental findings reported in the following study.] HAND-OUT

• * Leigh Tesfatsion, "Structure, Behavior, and Market Power in an Evolutionary Labor Market with Adaptive Search," Journal of Economic Dynamics and Control 25 (2001), 419-457. A preprint (pdf,778K) is available.
  Abstract: This study uses an ACE labor market framework to undertake a systematic experimental investigation of the relationship between market structure and market power in an evolutionary labor market. For each tested market structure, workers and employers repeatedly seek preferred worksite partners based on continually updated expected utility, engage in efficiency-wage worksite interactions modelled as prisoner's dilemma games, and evolve their worksite behaviors over time. A key finding is the presence of strong learning and network effects. Each tested market structure maps into a "spectral" distribution of observed interaction networks exhibiting one dominant attractor (frequent network pattern) with one or two weaker attractors (less frequent network patterns). Market structure is strongly predictive for the relative market power of workers and employers across all network attractors, but the magnitudes of the market power levels attained by workers and employers vary widely across the network attractors.

  * Richard B. Freeman, "War of the Models: Which Labour Market Institutions for the 21st Century?" (pdf,24pp), Labour Economics Vol. 5, No. 1, 31 March 1998, 1--24. [Entertaining accessible account of key controversies affecting labor market policy in particular and macroeonomics in general. Discusses new theoretical and empirical tools that seem particularly well suited for the analysis of labor issues, including the building of "artificial agent simulated societies."]

  * Richard Rogerson, "Theory Ahead of Language in the Theory of Unemployment", Journal of Economic Perspectives 11 (1997), 73-92.

Other source materials related to ACE labor market modeling

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

Important Note: Please start browsing and exploring possible topics for your student projects as soon as possible. Suggestions for possible student project topic areas (with linked resource sites) are listed below. I am available during office hours, by appointment, and anytime by email to provide guidance if desired. Preliminary outlines for student project proposals will be scheduled for in-class presentation in February and must be turned in for final go-ahead instructor approval no later than Thursday, February 27th. Final write-ups for student projects are due the last day of class. Please visit the Project Information Site for more detailed information regarding course projects.

• Introduction to the ACE Methodology
• Learning and the Embodied Mind
• Evolution of Norms
• Economic Networks and Spatial Economics
• Technological Change and Economic Growth
• Agricultural and Natural Resource Issues
• Financial Market Issues
• Labor Market Issues
• Restructured Electricity Markets
• Design of Auctions and Other Market Forms
• Automated Markets with Software Agents
• Network, Path-Dependence, and Lock-In Effects
• Formation and Design of Organizations
• Political Economy Issues
• Constructing and Using ACE Laboratories
• Parallel Experiments with Real and Computational Economic Agents

Copyright © 2003 Leigh Tesfatsion. All Rights Reserved.