Decomposition-integral: unifying Choquet and the concave integrals

This paper introduces a novel approach to integrals with respect to capacities. Any random variable is decomposed as a combination of indicators. A prespecified set of collections of events indicates which decompositions are allowed and which are not. Each allowable decomposition has a value determined by the capacity. The decomposition-integral of a random variable is defined as the highest of these values. Thus, different sets of collections induce different decomposition-integrals. It turns out that this decomposition approach unifies well-known integrals, such as Choquet, the concave and Riemann integral. Decomposition-integrals are investigated with respect to a few essential properties that emerge in economic contexts, such as concavity (uncertainty-aversion), monotonicity with respect to stochastic dominance and translation-covariance. The paper characterizes the sets of collections that induce decomposition-integrals, which respect each of these properties.

[1]  Christophe Labreuche,et al.  Bi-capacities - II: the Choquet integral , 2005, Fuzzy Sets Syst..

[2]  José Heleno Faro,et al.  Pricing rules and Arrow–Debreu ambiguous valuation , 2010 .

[3]  D. Schmeidler Integral representation without additivity , 1986 .

[4]  Sérgio Ribeiro da Costa Werlang,et al.  Nash equilibrium under knightian uncertainty: breaking down backward induction (extensively revised version) , 1993 .

[5]  Christophe Labreuche,et al.  Bi-capacities -- Part I: definition, Möbius transform and interaction , 2007, ArXiv.

[6]  A. Chateauneuf,et al.  Choquet Pricing for Financial Markets with Frictions , 1996 .

[7]  David Schmeidleis SUBJECTIVE PROBABILITY AND EXPECTED UTILITY WITHOUT ADDITIVITY , 1989 .

[8]  Jiankang Zhang,et al.  Subjective ambiguity, expected utility and Choquet expected utility , 2002 .

[9]  M. T. Lamata,et al.  A unified approach to define fuzzy integrals , 1991 .

[10]  Jung-Hsien Chiang,et al.  Choquet fuzzy integral-based hierarchical networks for decision analysis , 1999, IEEE Trans. Fuzzy Syst..

[11]  I. Gilboa Expected utility with purely subjective non-additive probabilities , 1987 .

[12]  J. Šipoš,et al.  Integral with respect to a pre-measure , 1979 .

[13]  P. Wakker Characterizing optimism and pessimism directly through comonotonicity , 1990 .

[14]  A. Tversky,et al.  Prospect theory: an analysis of decision under risk — Source link , 2007 .

[15]  A. Chateauneuf Comonotonicity axioms and rank-dependent expected utility theory for arbitrary consequences , 1999 .

[16]  Klaus Nehring Capacities And Probabilistic Beliefs: A Precarious Coexistence , 1999 .

[17]  Christophe Labreuche,et al.  Bi-capacities -- Part II: the Choquet integral , 2007, ArXiv.

[18]  Salvatore Greco,et al.  The Choquet integral with respect to a level dependent capacity , 2011, Fuzzy Sets Syst..

[19]  A. Tversky,et al.  Prospect Theory : An Analysis of Decision under Risk Author ( s ) : , 2007 .

[20]  Ehud Lehrer Partially-Specified Probabilities: Decisions and Games , 2006 .

[21]  N. Shilkret Maxitive measure and integration , 1971 .

[22]  Ehud Lehrer,et al.  A New Integral for Capacities , 2005 .

[23]  A. D. Waegenaere,et al.  Choquet pricing and equilibrium , 2003 .

[24]  菅野 道夫,et al.  Theory of fuzzy integrals and its applications , 1975 .

[25]  P. Wakker,et al.  Nonmonotonic Choquet Integrals , 2001 .

[26]  Shaun S. Wang,et al.  Axiomatic characterization of insurance prices , 1997 .

[27]  Kin Chung Lo,et al.  Equilibrium in Beliefs under Uncertainty , 1996 .

[28]  G. Choquet Theory of capacities , 1954 .

[29]  Christophe Labreuche,et al.  Bi-capacities - I: definition, Möbius transform and interaction , 2005, Fuzzy Sets Syst..

[30]  D. Ellsberg Decision, probability, and utility: Risk, ambiguity, and the Savage axioms , 1961 .

[31]  Ehud Lehrer,et al.  Partially-Specified Probabilities: Decisions and Games , 2006 .

[32]  Michel Grabisch,et al.  A decade of application of the Choquet and Sugeno integrals in multi-criteria decision aid , 2010, Ann. Oper. Res..

[33]  A. Tversky,et al.  Prospect theory: analysis of decision under risk , 1979 .

[34]  G. Klir,et al.  Fuzzy Measure Theory , 1993 .

[35]  Jean-Luc Marichal,et al.  An axiomatic approach of the discrete Choquet integral as a tool to aggregate interacting criteria , 2000, IEEE Trans. Fuzzy Syst..

[36]  Peter P. Wakker Subjective Expected Utility with Nonadditive Probabilities , 1989 .

[37]  Ehud Lehrer,et al.  Extension Rules or What Would the Sage Do , 2014 .

[38]  Radko Mesiar,et al.  A Universal Integral as Common Frame for Choquet and Sugeno Integral , 2010, IEEE Transactions on Fuzzy Systems.

[39]  Yutaka Nakamura Subjective expected utility with non-additive probabilities on finite state spaces , 1990 .

[40]  Josef Hadar,et al.  Rules for Ordering Uncertain Prospects , 1969 .

[41]  Michel Grabisch,et al.  A decade of application of the Choquet and Sugeno integrals in multi-criteria decision aid , 2010, Ann. Oper. Res..

[42]  M. Sugeno,et al.  Some quantities represented by the Choquet integral , 1993 .

[43]  Michel Grabisch,et al.  A discrete Choquet integral for ordered systems , 2011, Fuzzy Sets Syst..

[44]  J. Schreiber Foundations Of Statistics , 2016 .

[45]  V. Bawa OPTIMAL, RULES FOR ORDERING UNCERTAIN PROSPECTS+ , 1975 .

[46]  Philippe Artzner,et al.  Coherent Measures of Risk , 1999 .