Toward an Agent-Based Computational Modeling of Bargaining Strategies in Double Auction Markets with Genetic Programming

Abstract

Using genetic programming, this paper proposes an agent- based computational modeling of double auction (DA) markets in the sense that a DA market is modeled as an evolving market of autonomous interacting traders (automated software agents). The specic DA market on which our modeling is based is the Santa Fe DA market ((12), (13)), which in structure, is a discrete-time version of the Arizona continuous- time experimental DA market ((14), (15)).