In this paper we explicitly model risk aversion in multiagent interactions. We propose an insurance mechanism that be can used by risk-averse agents to mitigate against risky outcomes and to improve their expected utility. Given a game, we show how to derive Pareto-optimal insurance policies, and determine whether or not the proposed insurance policy will change the underlying dynamics of the game (i.e., the equilibrium). Experimental results indicate that our approach is both feasible and effective at reducing risk for agents. © 2009 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Hines, G., & Larson, K. (2009). Insuring risk-averse agents. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5783 LNAI, pp. 294–305). https://doi.org/10.1007/978-3-642-04428-1_26
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