Computational Modeling of Collective Human Behavior: The Example of Financial Markets

  • Authors:

  • Andy Kirou;Błażej Ruszczycki;Markus Walser;Neil F. Johnson

  • Affiliations:

  • Department of Physics, University of Miami, Coral Gables, USA FL 33124;Department of Physics, University of Miami, Coral Gables, USA FL 33124;Landesbank Baden-Württemberg, Stuttgart, Germany 70173;Department of Physics, University of Miami, Coral Gables, USA FL 33124

  • Venue:
  • ICCS '08 Proceedings of the 8th international conference on Computational Science, Part I
  • Year:
  • 2008

Quantified Score

Hi-index 0.00

Visualization

Abstract

As a result of the increased availability of higher precision spatiotemporal datasets, coupled with the realization that most real-world human systems are complex, a new field of computational modeling is emerging in which the goal is to develop minimal models of collective human behavior which are consistent with the observed real-world dynamics in a wide range of systems. For example, in the field of finance, the fluctuations across a wide range of markets are known to exhibit certain generic stylized facts such as a non-Gaussian `fat-tailed' distribution of price returns. In this paper, we illustrate how such minimal models can be constructed by bridging the gap between two existing, but incomplete, market models: a model in which a population of virtual traders make decisions based on common global information but lack local information from their social network, and a model in which the traders form a dynamically evolving social network but lack any decision-making based on global information. We show that a combination of these two models --- in other words, a population of virtual traders with access to both global and local information --- produces results for the price return distribution which are closer to the reported stylized facts. Going further, we believe that this type of model can be applied across a wide range of systems in which collective human activity is observed.