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 specific 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]).