activity
19982005
most citedUsing Artificial Market Models to Forecast Financial Time-Series

5 citations · 12 across the 13 of their papers we have counts for

collaborators

18 papers

physics.soc-ph20055 cited

Using Artificial Market Models to Forecast Financial Time-Series

Nachi Gupta, Raphael Hauser, Neil F. Johnson

We discuss the theoretical machinery involved in predicting financial market movements using an artificial market model which has been trained on real financial data. This approach…

cond-mat.dis-nn2005

Decision Making, Strategy dynamics, and Crowd Formation in Agent-based models of Competing Populations

K. P. Chan, Pak Ming Hui, Neil F. Johnson

The Minority Game (MG) is a basic multi-agent model representing a simplified and binary form of the bar attendance model of Arthur. The model has an informationally efficient phas…

cond-mat.dis-nn2005

Transitions in collective response in multi-agent models of competing populations driven by resource level

Sonic H. Y. Chan, T. S. Lo, P. M. Hui +1

We aim to study the effects of controlling the resource level in agent-based models. We study, both numerical and analytically, a Binary-Agent-Resource (B-A-R) model in which a…

cond-mat.other2005

Many-Body Theory for Multi-Agent Complex Systems

Neil F. Johnson, David M. D. Smith, Pak Ming Hui

Multi-agent complex systems comprising populations of decision-making particles, have wide application across the biological, informational and social sciences. We uncover a formal…

cond-mat.dis-nn20041 cited

Evolution Management in a Complex Adaptive System: Engineering the Future

David M. D. Smith, Neil F. Johnson

We examine the feasibility of predicting and subsequently managing the future evolution of a Complex Adaptive System. Our archetypal system mimics a competitive population of mecha…

cond-mat.dis-nn2004

Plateaux formation, abrupt transitions, and fractional states in a competitive population with limited resources

H. Y. Chan, T. S. Lo, P. M. Hui +1

We study, both numerically and analytically, a Binary-Agent-Resource (B-A-R) model consisting of N agents who compete for a limited resource 1/2<L/N <1, where L is the maximum avai…