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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Univ Nacl Sur, INMABB CONICET, San Andres 800, RA-8000 Bahia Blanca, Buenos Aires, ArgentinaUniv Nacl Sur, INMABB CONICET, San Andres 800, RA-8000 Bahia Blanca, Buenos Aires, Argentina
Fioriti, Andres
Hernandez-Chanto, Allan
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Univ Queensland, St Lucia, Qld 4072, AustraliaUniv Nacl Sur, INMABB CONICET, San Andres 800, RA-8000 Bahia Blanca, Buenos Aires, Argentina
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Univ Sci & Technol, Int Inst Finance, Sch Management, Hefei, Anhui, Peoples R ChinaUniv Sci & Technol, Int Inst Finance, Sch Management, Hefei, Anhui, Peoples R China
Zhang, Juzhi
Sethi, Suresh P.
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Univ Texas Dallas, Naveen Jindal Sch Management, Eugene McDermott Prof Operat Management, Richardson, TX 75083 USAUniv Sci & Technol, Int Inst Finance, Sch Management, Hefei, Anhui, Peoples R China
Sethi, Suresh P.
Choi, Tsan-Ming
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Natl Taiwan Univ, Coll Management, Dept & Grad Inst Business Adm, Roosevelt Rd, Taipei 10617, TaiwanUniv Sci & Technol, Int Inst Finance, Sch Management, Hefei, Anhui, Peoples R China
Choi, Tsan-Ming
Cheng, T. C. E.
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Hong Kong Polytech Univ, Fac Business, Hung Hom, Kowloon, Hong Kong, Peoples R ChinaUniv Sci & Technol, Int Inst Finance, Sch Management, Hefei, Anhui, Peoples R China