Syllabus for Economics 308

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

Last Updated: 28 March 2015

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

Instructor:
Leigh Tesfatsion
Professor of Econ, Math, and ECpE
Department of Economics
Iowa State University
Ames, Iowa 50011-1070
http://www2.econ.iastate.edu/tesfatsi/
tesfatsi AT iastate.edu

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 http://www2.econ.iastate.edu/classes/econ308/tesfatsion/

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

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. 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:


Exercises:

** 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:


Required Readings:


Recommended Materials:

Other introductory source materials on CAS/ACE

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

Key In-Class Discussion Topics:


Exercise:

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


Required Readings:


Recommended Materials:

II. Complexity of Decentralized Market Economies

II.A Basic Market Concepts

Key In-Class Discussion Topics:


Exercise:

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


Required Readings:


Recommended Materials:

ACE-Related Research on Multi-Market Modeling

II.B Market Games

Key In-Class Discussion Topics:


Exercise:

** 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:


Recommended Materials:

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.

  • Visualcomplexity.com 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.