Background:
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.
Organization:
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 http://www.ieee-nns.org/ec/.
Activities:
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)