a novel method for automatic strategy acquisition in n-player non-zero-sum games
We present a novel method for automatically acquiring strategies for the double auction by combining evolutionary optimization together with a principled game-theoretic analysis. Previous studies in this domain have used standard co-evolutionary algorithms, often with the goal of searching for the "best" trading strategy. However, we argue that such algorithms are often ineffective for this type of game because they fail to embody an appropriate game-theoretic solution-concept , and it is unclear, what, if anything, they are optimizing. In this paper, we adopt a more appropriate criterion for success from evolutionary game-theory based on the likely adoption-rate of a given strategy in a large population of traders, and accordingly we are able to demonstrate that our evolved strategy performs well.
adaptation and learning multi-agent evolution auctions and electronic markets