If multi-agent learning is the answer, what is the question?
Artificial Intelligence
Computing correlated equilibria in multi-player games
Journal of the ACM (JACM)
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Insurance services in multi-agent systems
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
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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.