Abstract
In this paper we analyze two recent axiomatic
approaches proposed by Dubois et al., and by Giang and Shenoy, respectively, for qualitative decision making where uncertainty is described by possibility theory. Both axiomtizations are inspired by von Neumann and Morgenstern's system of axioms for the case of probability theory. We show that our approach naturally unifies two axiomatic systems that correspond, respectively, to pessimistic and optimistic decision criteria proposed by Dubois et al. The simplifying unification is achieved by (i) replacing axioms
that are supposed to reflect two informational
attitudes (uncertainty aversion and uncertainty
attraction) by an axiom that imposes order on set of standard lotteries, and (ii) using a binary utility scale in which each utility level is represented by a pair of numbers.
Description
A longer version of this paper is available from KU Scholarworks as Giang, P. H. and P. P. Shenoy, "Two Axiomatic Approaches to Decision Making Using Possibility Theory," European Journal of Operational Research, Vol. 162, No. 2, 2005, pp. 450--467.
Citation
Giang, P. H. and P. P. Shenoy (2001), "A Comparison of Axiomatic Approaches to Qualitative Decision Making Using Possibility Theory," in J. Breese and D. Koller (eds.), Uncertainty in Artificial Intelligence, pp. 162--170, Morgan Kaufmann, San Francisco, CA.