Syllabus for Economics 308
Agent-Based Computational Economics (ACE):
Growing Economies from the Bottom Up
- Last Updated: 30 April 2008
- Latest Course Offering: Spring 2008
- Meeting Time: TR 11-12:20
- Meeting Place: East Hall 111
- 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 AT iastate.edu
- Office Hours:
- Heady 375, Thursdays 12:40-2:40pm and by appointment
Course Overview
- Econ 308 is a fun course stressing a "virtual reality" approach to the
study of economic processes.
- Modern economies are complicated systems encompassing
large numbers of geographically-distributed individuals
and social groupings interacting through markets and
other forms of institutions. How to get a handle on
this complexity?
-
One approach is to model an economy computationally as
a dynamic system of interacting "agents." These agents
includes social entities
such as people and families, institutional entities such
as corporations and legal systems, physical entities such
as landscapes and highway systems, and biological entities
such as crops and livestock.
- The modeler specifies the
initial states of the agents comprising an economy either to match empirical
conditions or to match hypothesized conditions whose
possible effects the modeler wants to test. The modeler
then steps back to observe and record what happens as the economy runs forward
in time as a "virtual world" driven by agent interactions.
- 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)
-
Econ 308 introduces students to this exciting new methodology.
The exact topic selection and depth of coverage will depend on student
interests and backgrounds. Tentatively scheduled course topics are indicated below.
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.
-
- Introduction
- What are Complex Adaptive Systems (CAS)?
- What is ACE?
- Hands-On Introduction to Agent-Based Computational Modeling
- The Complexity of Decentralized Market
Economies
- Learning and the Embodied Mind
- Illustrative Examples of Situated Learning
- Learning Representations
- Financial Market Illustrations
- Interaction on Fixed Networks
- Formation of Interaction Networks
- Real-World Application: Electricity Restructuring
- 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:
- What is a complex system? a complex adaptive system?
- Illustrative examples (Cellular automata, Bak's Sand Pile Model, Schelling's
Segregation Model,...)
- Experimental design: Basic concepts and terminology
- Exercises:
-
**
Exercise 1 (Indiv/Team): Introduction to the Schelling Segregation Model
(pdf,21K). Due: Tuesday, January 22, 2008, 11:00am.
-
**
Exercise 2 (Indiv/Team): So How Do *YOU* Think "Segregation" Should Be Measured? (Running systematic segregation experiments using Chris Cook's Schelling Demo)
(pdf,26K). Due: Tuesday, January 29, 2008, 11:00am.
- 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). HAND-OUT
- Note: If at all possible, some day make time to savor this entire
delightful book!
- ** Leigh Tesfatsion, "Notes on Batten Chapter 1 - stress on the Glossary of Terms"
(html). ON-LINE
- ** Leigh Tesfatsion, "Possible definitions for `Complex System' and
`Complex Adaptive System'"
(pdf,19K). ON-LINE/CLASS PRESENTATION
-
** Leigh Tesfatsion, "Introduction to Cellular Automata"
(pdf,1M). ON-LINE/CLASS PRESENTATION
-
** Leigh Tesfatsion,
"Implementing Per Bak's Sand Pile Model
as a Two-Dimensional Cellular Automaton"
(pdf,50K). ON-LINE/CLASS PRESENTATION
-
** Leigh Tesfatsion,
"Experimental Design: Basic Concepts and Terminology"
(pdf,45K). 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 John Conway's Game of Life, Per Bak's Sand Pile Model, and Thomas Schelling's Segregation
Model (as well as cellular automata more generally) can be found at this site.
- * 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.
- 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,17MB).
-
(3/20/08)
*
Peter Albin, Preface (pp. xiii-xxxi)
(pdf,146K)
and Duncan K. Foley, Chapter 1: "Introduction (pp. 3-22)
(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: This book preface and pages 3-22 of this wide-ranging introductory chapter by two seminal contributors to economic complexity theory discuss possible automata-theoretic resolutions to economic complexity puzzles and an overview of dynamical systems in both the social and physical sciences.
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,172K). ON-LINE/CLASS PRESENTATION
- Recommended Materials:
-
The Trade Network Game Lab: Demonstration Software (Market Games, Network Formation, GA Learning)
(html)
- * 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
-
(3/20/08)
*
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)?
- Exercises:
-
** First In-Class Student-Moderated Discussion:
- Agent-Oriented Programming Vs. Object-Oriented Programming: Is There Any Real Difference?"
(pdf,24K),
scheduled for February 5,2008.
-
**
Take-Home Exercise 3 (Individual, Pass-Fail): Hands-On Introduction to Agent-Based Modeling Platforms
(pdf,31K).
Due: Tuesday, February 12, 2008, 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
- Other Recommended Materials:
- * Matt Weisfeld, "Introduction to Object-Oriented
Concepts" Chapter 1 (pp. 8-31) in The Object-Oriented Thought Process,
SAMS Books, Macmillan, 2000. 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"
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.
- * 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
- Key In-Class Discussion Topics:
- The circular flow underlying decentralized market economies
- Key types of market players
- Key types of market organizations
- Strategic interaction in decentralized market economies
- Taking agent autonomy seriously in multi-market modeling
- Modeling the coevolution of market behaviors and market institutions
- Can "zero intelligence" (randomly behaving) agents perform well
in highly structured markets such as double auctions?
- Exercises:
-
** Second In-Class Student-Moderated Exercise:
Double-Auction Markets as a Partial Substitute for Rationality?"
(pdf,26K),
scheduled for February 21, 2008.
-
**
ANSWER OUTLINE for Take-Home Exercise 4 (Individual): Construction and Analysis of Market Demand
and Supply Functions
(pdf,249K).
Due: February 19, 2007, 11:00am.
-
**
Take-Home Exercise 5 (Ind/Team): Zero Intelligence Market Trading Exercise - JAVA VERSION
(pdf,23K).
Due: Tuesday, Feb 26, 11:00am.
- IMPORTANT NOTE: Choose EITHER to do THIS JAVA exercise OR to do the FOLLOWING NETLOGO exercise, not both!
-
**
Take-Home Exercise 5 (Ind/Team): Zero Intelligence Market Trading Exercise - NETLOGO VERSION
(pdf,23K).
Due: Tuesday, Feb 26, 11:00am.
- IMPORTANT NOTE: Choose EITHER to do THIS NETLOGO exercise OR to do the ABOVE JAVA exercise, not both!
- Required Readings:
-
** 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,
"Market Organization with Price-Setting Agents"
(html,8K). ON-LINE/CLASS PRESENTATION
-
** Leigh Tesfatsion, "Market Basics for Price-Setting Agents"
(pdf,422K). ON-LINE/CLASS PRESENTATION
-
** Leigh Tesfatsion, "Illustration of Demand & Supply Schedule Construction"
(pdf,153K). 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
-
** Leigh Tesfatsion,
"Game Theory: Basic Concepts and Terminology"
(pdf,34K). ON-LINE/CLASS PRESENTATION
-
** Leigh Tesfatsion,
"Price Discovery with Price-Setting Agents (Market Games)"
(pdf,103K). ON-LINE/CLASS PRESENTATION
- Recommended Materials:
-
* Mark McBride's Zero-Intelligence Trading Demo (Java Applet/NetLogo Model)
(html) ON-LINE
- 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
III. Learning and the Embodied Mind
III.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?
- Exercises:
- ** In-Class Experiment: One for all and all for one -- maybe!
-
** Third In-Class Student Moderated Discussion:
"Robots, Slime Mold, and Learning?
(pdf,26K), Scheduled for 11-12 noon, March 6, 2008
- * Conducting Experiments with Chris Cook's Axelrod Tournament Demo
(pdf,39K).
- Required Readings:
-
** Leigh Tesfatsion,
"Notes on Axelrod's IPD Tournaments"
(pdf,473K). ON-LINE/CLASS PRESENTATION
- ** Douglas Hofstadter, "Computer Tournaments of the Prisoner's Dilemma
Suggest How Cooperation Evolves",
Scientific American
May 1983, 18-26. HAND-OUT
- Recommended Materials:
-
* Chris Cook's Axelrod Tournament Demonstration Software
(html)
-
* Robert Axelrod (1984), The Evolution of Cooperation (Chapters 1,2,9)
(pdf,3.6M),
Basic Books Inc., New York, NY.
III.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?
- Take-Home Exercises:
- Required Readings:
-
** Leigh Tesfatsion, "Learning Algorithms: Illustrative Examples"
(pdf,853K). ON-LINE/CLASS PRESENTATION
-
** Leigh Tesfatsion, "Notes on Learning"
(pdf,132K). ON-LINE
- Recommended Materials:
-
The Trade Network Game Lab: Demonstration Software (GA Learning)
(html)
-
Reinforcement Learning: A User's Guide (Bill Smart, Wash U, St. Louis)
(pdf,430K)
-
"A Comparative Study of Roth-Erev and Modified Roth-Erev Reinforcement Learning Algorithms
for Uniform-Price Double Auctions" (Mridul Pentalli, ISU, March 2008)
(pdf,6.5M)
- * 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
- * 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
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 Readings:
-
** Leigh Tesfatsion, "Stock Market Basics"
(pdf,277K). ON-LINE/CLASS PRESENTATION
-
** Leigh Tesfatsion, "Rational Expectations, the Efficient Market
Hypothesis, and the Santa Fe Artificial Stock Market Model"
(pdf,856K). ON-LINE/CLASS PRESENTATION
-
** Silvano Cincotti, "Some Stock Market Stylized Facts"
(pdf,615K) ON-LINE
-
** Leigh Tesfatsion, "The Santa Fe Artificial Stock Market: Overview"
(pdf,99K) ON-LINE
- Recommended Materials:
-
Santa Fe Stock Market Demonstration Software
(html,46K)
- * Rob Axtell, "ACE Financial Market Modeling"
(pdf,82K),
presented at the VII Trento Summer School on ACE, July 2006. ON-LINE
- * 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,123K),
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,
"Detailed Notes on the Santa Fe Artificial Stock Market Model"
(html). ON-LINE
-
Other source materials related to ACE financial modeling
V. 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:
-
(4/27/08)
** 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.
-
(4/26/08)
** 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. 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:
- * 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
-
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
VI. 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
- ** Leigh Tesfatsion, "Illustrative Application: Labor Institutions and
Market Performance"
(pdf,117K). ON-LINE/CLASS PRESENTATION
- Recommended Materials:
-
The Trade Network Game Lab: 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 related to ACE labor research
-
General resource site on network formation
VII. Real-World Application: Electricity Restructuring
- Key In-Class Discussion Topics:
- How are U.S. wholesale power markets currently being restructured?
- How might ACE frameworks be used to test the efficiency, reliability, and fairness of the designs
being proposed for restructured wholesale power markets?
- Required Readings:
- ** Leigh Tesfatsion,
"Agent-Based Test Beds for Power Industry Research, Teaching, and Training"
(pdf,971K). ON-LINE/CLASS PRESENTATION
- NOTE: The above presentation, to be given in class, constitutes
the required reading. A preprint of the article on which this presentation is based (Li, Sun and Tesfatsion, 2008, Proceedings, IEEE PES GM) can be accessed
here (pdf, 981K).
- Recommended Materials:
-
AMES Market Package (Java): A Free Open-Source Test Bed for the Agent-Based Modeling of Electricity Systems
(html)
-
Other source materials related to ACE Electricity Research
-
General resources on electricity restructuring
VIII. Empirical Validation of ACE Models
- Key In-Class Discussion Topics:
- 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?
- How can input validation (operational validity) be ensured for ACE models?
- How can descriptive output validation be ensured for ACE models?
- How can predictive output validation be ensured for ACE models?
- How might the empirical validation of ACE models be aided by an iterative participatory
modeling approach?
- How can ACE researchers provide a summary reportings of model validation results?
- How can ACE researchers ensure the robustness of model validation findings?
- How can ACE researchers ensure the accumulation of empirically supported findings?
- 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 © 2008 Leigh Tesfatsion. All Rights Reserved.
