Market Design Using Agent-Based Models

This chapter explores the state of the emerging practice of designing markets by the use of agent-based modeling, with special reference to electricity markets and computerized (on-line) markets, perhaps including real-life electronic agents as well as human traders. The paper first reviews the use of evolutionary and agent-based techniques of analyzing market behaviors and market mechanisms, and economic models of learning, comparing genetic algorithms with reinforcement learning. Ideal design would be direct optimization of an objective function, but in practice the complexity of markets and traders' behavior prevents this, except in special circumstances. Instead, iterative analysis, subject to design criteria trade-offs, using autonomous self-interested agents, mimics the bottom-up evolution of historical market mechanisms by trial and error. The chapter highlights ten papers that exemplify recent progress in agent-based evolutionary analysis and design of markets in silico, using electricity markets and on-line double auctions as illustrations. A monopoly sealed-bid auction is examined in the tenth paper, and a new auction mechanism is evolved and analyzed. The chapter concludes that, as modeling the learning and behavior of traders improves, and as the software and hardware available for modeling and analysis improves, the techniques will provide ever greater insights into improving the designs of existing markets, and facilitating the design of new markets.