Human-Agent Auction Interactions: Adaptive-Aggressive Agents Dominate

We report on results from experiments where human traders interact with software-agent traders in a real-time asynchronous continuous double auction (CDA) experimental economics system. Our experiments are inspired by the seminal work reported by IBM at IJCAI 2001, where it was demonstrated that software-agent traders could consistently outperform human traders in real-time CDA markets. IBM tested two trading-agent strategies, ZIP and a modified version of GD, and in a subsequent paper they reported on a new strategy called GDX that was demonstrated to outperform GD and ZIP in agent-vs.-agent CDA competitions, on which basis it was claimed that GDX ``...may offer the best performance of any published CDA bidding strategy''. In this paper, we employ experiment methods similar to those pioneered by IBM to test the performance of Vytelingum's ``Adaptive Aggressive'' (AA) algorithmic traders. The results presented here confirm Vytelingum's claim that AA outperforms ZIP, GD, and GDX in agent-vs-agent experiments. We then present the first results from testing AA against human traders in human-vs.-agent CDA experiments, and demonstrate that AA's performance against human traders is superior to that of ZIP, GD, and GDX. We therefore claim that, on the basis of the available evidence, AA may offer the best performance of any published bidding strategy.