Visualizing Search-Spaces for Evolved Hybrid Auction Mechanisms

A sequence of previous papers has demonstrated that a genetic algorithm (GA) can be used to automatically discover new optimal auction mechanisms for automated electronic marketplaces populated by software-agent traders. Significantly, the new auction mechanisms are often unlike traditional mechanisms designed by humans for human traders; rather, they are peculiar hybrid mixtures of established styles of mechanism. Qualitatively similar results (i.e., non-standard hybrid mechanism designs being evolved) have been demonstrated for Cliff’s ZIP trader algorithm and also for Gode & Sunder’s ZI-C traders, provoking the possibility that such hybrid markets may be optimal for any marketplace populated entirely by artificial trader-agents. The financial implications of this work could potentially be measured in billions of dollars. In an attempt to elucidate why these evolved hybrid markets outperform traditional human-designed mechanisms, this paper presents results from thousands of repetitions of the GA experiments. These data allow 2-d projections of the 10-d real-space fitness landscape to be made, which inter alia illustrate a surprisingly high sensitivity in the relationship between the fitness evaluation function and the resulting landscape.