A graphical model for collective behavior learning using minority games

Abstract

The Minority Game (MG) is a simple game theory model for the collective behavior of agents in an idealized situation where they compete for some finite resource. In this paper, we assume that collective behavior is determined by the aggregation of individual actions of agents. This causal relation between collective behavior and individual actions is investigated. A graphical model is proposed to model the generative process of collective behavior using a group of agents whose actions are modeled by minority games. In this model, we can infer the individual behavior of the agents by training on the global information, and then make predictions about the future collective behavior. Experimental results on a set of stock indexes from the Chinese market and foreign exchange (FX) rates show that the new proposed model can effectively capture the rises and falls of market and be significantly better than a random predictor. This framework also provides a new data mining paradigm for analyzing collective data by modeling micro-level actions of agents using game theory models. © 2014 Springer International Publishing.