Simulation analysis for centipede game focusing on interaction between asymmetry agents

Abstract

In a centipede game, players may make decision based on multiple criteria, not only amount of payoff of the game, by trial and error mechanism. This paper conducts agent-based simulation analysis by using neural network and genetic algorithms. In simulation experiments, information amounts, long-term view about cumulative payoff, and asymmetric agents and their interaction are considered.