The Trade Network Game: A Computational Laboratory for the Study of Agent-Based Markets

Agent-based computational economics (ACE) is the computational study of economies modelled as evolving decentralized systems of automous interacting agents. The Trade Network Game (TNG) is a particular ACE framework that has been designed for studying the formation and evolution of trade networks in buyer-seller markets. The TNG is currently being applied to the study of market power, hysteresis (path dependency effects), and excess earnings heterogeneity in labor markets. In this talk I will begin with a brief overview of the ACE methodology and the TNG framework. I will then illustrate the use of the TNG framework by discussing various experimental findings regarding the evolution of market power in an evolutionary labor market with strategically interacting work suppliers and employers.

Leigh Tesfatsion's slides (PDF)

David McFadzean's TNGLab demo

Table of Contents of TNGLab Demo

TNGLab Demonstration

ACE Software Support Issues

Simulation Toolkit Design

SimBioSys

Simulation Dynamics

TNG Lab: Settings screen

TNG Lab: Results screen

TNG Lab: Chart screen

TNG Lab: Animation

TNG Lab: Physics

Physics illustrated

Case 1: Workersí Market

Case 1: Workersí Market

Case 1: Workersí Market

Author: Leigh Tesfatsion
Department of Economics
Iowa State University

Leigh Tesfatsion received the Ph.D. degree in economics, with a minor in mathematics, from the University of Minnesota in 1975. She joined the Department of Economics at the University of Southern California in 1975, where she subsequently was promoted to associate and full professor. In 1990 she accepted a position as professor of economics at Iowa State University, with a courtesy appointment as a professor of mathematics. Her recent research has focused on agent-based computational economics (ACE), the computational study of economies modelled as evolving decentralized systems of autonomous interacting agents. In particular, she has developed an ACE framework for studying the formation and evolution of trade networks under alternative market structures. This framework is currently being applied to the study of market power, hysteresis, and excess heterogeneity in labor markets and to the study of restructuring in electric power markets. Her past research includes work on financial intermediation, human capital investment, decision making under uncertainty, game theory, adaptive control, automatic differentiation, adaptive homotopy continuation, nonlinear filtering, artificial neural networks, and a multicriteria flexible least squares (FLS) technique for model specification and estimation that has been incorporated into the statistical packages GAUSS and SHAZAM. This research has been reported in over sixty articles in economics, mathematics, statistics, and systems science journals. She currently serves as co-editor in charge of the Complexity-at-Large section of the journal Complexity as well as an associate editor for the Journal of Public Economic Theory, the IEEE Transactions on Evolutionary Computation, and Applied Mathematics and Computation. In January 1997 she was elected to a three-year term on the Advisory Council for the Society for Computational Economics. Since 1993 she has helped to organize and run a weekly interdisciplinary workshop at Iowa State University, now called the ISU Complex Adaptive Systems Workshop. She also maintains an ACE Web site that features resources of interest to complex adaptive systems researchers in general and ACE researchers in particular. This site has been designated as a select learning resource by the Scout Report for Business and Economics (January 29, 1998).