This paper presents an approach to automated mechanism design in the domain of double auctions. We describe a novel parameterized space of double auctions, and then introduce an evolutionary search method that searches this space of parameters. The approach evaluates auction mechanisms using the framework of the TAC Market Design Game and relates the performance of the markets in that game to their constituent parts using reinforcement learning. Experiments show that the strongest mechanisms we found using this approach not only win the Market Design Game against known, strong opponents, but also exhibit desirable economic properties when they run in isolation.
Jinzhong Niu, Kai Cai, Simon Parsons, Maria Fasli, and Xin Yao. A grey-box approach to automated mechanism design. Journal of Electronic Commerce Research and Applications, 11(1):24–35, January-February 2012.