IEEE Evolutionary Computation

Task Force on Finance and Economics

Evolutionary computation has been established as a useful tool for studying economics and finance. Example applications include financial forecasting, real and artificial stock markets creation, micro-behaviour analysis, stock trading and portfolio optimization, market dynamics, game theory, risk analysis and many other areas. All of these areas are built on firm economic foundations. However, the applicability of most economic theories is limited by their simplifying assumptions. Advances in computing, in both hardware and algorithms, enable researchers to study economics and finance with a completely different approach. For example, one may attempt to recognize patterns in complex systems, simulate complex agents' behaviour in market environments, study the interaction of complex strategies, study algorithmic strategies in game theory, analyze volatility in financial markets, etc. 

The purpose of this Task Force is to promote evolutionary computation research and applications in economics and finance, including, but not limited to, the above mentioned areas. This Task Force is organized under the IEEE Neural Networks Society (NNS) Technical Committee on Evolutionary Computation


Members of the Task Force:
Chair: Edward Tsang (CCFEA and Computer Science, Essex University, UK)
Martin Bichler (Internet-based Information Systems (IBIS), Germany)
Shu-Heng Chen (AI-ECON Center and Economics Department, Taiwan)
Michael Dempster (Judge Institute, Cambridge University, UK)
Jerzy Korczak (Maths and Computing, Louis Pasteur, University of Strasbourg, France)
Thomas Lux (Economics, Kiel University, Germany)
Sheri Markose (CCFEA and Economics, Essex University, UK)
Peter Ross (School of Computing, Napier University, UK)
Sonia Schulenburg (Centre for Emergent Computing, Napier University, UK)

This page is maintained by:
Professor Edward Tsang
Department of Computer Science,
University of Essex,
Wivenhoe Park, Colchester, CO4 3SQ, UK