Abstract
We address the problem of creating agents that can succeed in environments where adapting to the norms of the market or society is a significant factor to performing well. We focus on the Social Ultimatum Game, a multi-agent multi-round extension of the Ultimatum Game, a classical game-theoretic problem which has been studied for decades due to the variability of human behavior it elicits. We create ten societies of agents based on five classes of agent behavior to create a diverse test environment. We used Amazon Mechanical Turk to evaluate how human participants adapt to these various societies, serving as a baseline for agent-based approaches. Current approaches primarily succeed in societies similar to their own and fail elsewhere. We construct a new type of adaptive agent, based on single-step marginal-value optimization, and show that it outperforms humans across these varying agent societies when playing the Social Ultimatum Game.
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Kim, E., Chang, YH., Maheswaran, R., Ning, Y., Chi, L. (2013). Agent Adaptation across Non-ideal Markets and Societies. In: David, E., Kiekintveld, C., Robu, V., Shehory, O., Stein, S. (eds) Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets. AMEC TADA 2012 2012. Lecture Notes in Business Information Processing, vol 136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40864-9_7
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DOI: https://doi.org/10.1007/978-3-642-40864-9_7
Publisher Name: Springer, Berlin, Heidelberg
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