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Insuring Risk-Averse Agents

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Algorithmic Decision Theory (ADT 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5783))

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Abstract

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.

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Author information

Authors and Affiliations

  1. Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada

    Greg Hines & Kate Larson

Authors
  1. Greg Hines
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  2. Kate Larson
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Editor information

Editors and Affiliations

  1. Department of Pure and Applied Mathematics, University of Padova, Via Trieste 63, 35121, Padova, Italy

    Francesca Rossi

  2. LAMSADE-CNRS, University of Paris Dauphine, Place du Maréchal De Lattre de Tassigny, 75775, Paris Cedex 16, France

    Alexis Tsoukias

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© 2009 Springer-Verlag Berlin Heidelberg

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Hines, G., Larson, K. (2009). Insuring Risk-Averse Agents. In: Rossi, F., Tsoukias, A. (eds) Algorithmic Decision Theory. ADT 2009. Lecture Notes in Computer Science(), vol 5783. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04428-1_26

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  • DOI: https://doi.org/10.1007/978-3-642-04428-1_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04427-4

  • Online ISBN: 978-3-642-04428-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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