Nonlinear Dynamics and Heterogeneous Interacting Agents

Editors: Prof. Dr. Thomas Lux, Eleni Samanidou, Dr. Stefan Reitz



Economic application of nonlinear dynamics, microscopic agent-based modelling, and the use of artificial intelligence techniques as learning devices of boundedly rational actors are among the most exciting interdisciplinary ventures of economic theory over the past decade. This volume provides us with a most fascinating series of examples on "complexity in action" exemplifying the scope and explanatory power of these innovative approaches.

Table of contents (21 chapters)

The Implementation of the Turing Tournament: A Report

Expectations Structure in Asset Pricing Experiments

Learning in a “Basket of Crabs”: An Agent-Based Computational Model of Repeated Conservation Auctions

On the Benefit of Additional Information in Markets with Heterogeneously Informed Agents — an Experimental Study

Crowd Effects in Competitive, Multi-Agent Populations and Networks

Local Minority Game and Emergence of Efficient Dynamic Order

Agents with Heterogeneous Strategies Interacting in a Spatial IPD

Complexity Leads to Benefits: Pareto-Improving Chaos in a Heterogeneous Duopoly Market

On Novelty and Heterogeneity

'Collective Innovation’ in a Model of Network Formation with Preferential Meeting

Population Learning in Random Games with Endogenous Network Formation

Growth and Coalition Formation

The Topology of Shareholding Networks

A New Model of Labor Dynamics: Ultrametrics, Okun's Law, and Transient Dynamics

A Finitary Characterization of the Ewens Sampling Formula

Statistical Properties of Absolute Log-Returns and a Stochastic Model of Stock Markets with Heterogeneous Agents

Asset Price Dynamics and Diversification with Heterogeneous Agents

An Asset Pricing Model with Adaptive Heterogeneous Agents and Wealth Effects

The Red Queen Principle and the Emergence of Efficient Financial Markets: An Agent Based Approach

Price Formation in an Artificial Market: Limit Order Book Versus Matching of Supply and Demand

Fraudulent Agents in an Artificial Financial Market