Chapter 27 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.