Applying the solutions defined in the axiomatic bargaining theory to actual bargaining problems is a challenge when the problem is not described by its Utility Possibility Set (UPS) but as a social choice environment specifying the set of alternatives and utility profile underlying the UPS. It requires computing the UPS, which is an operational challenge, and then identifying at least one alternative that actually achieves the bargained solution’s outcome. We introduce the axioms of Independence of Non-Strongly-Efficient Alternatives (resp. Weakly) and (...) Independence of Redundant Alternatives. A solution satisfying these axioms can be applied to a simplified problem based on any reduced set of alternatives generating the strong (resp. weak) Pareto frontier of the initial problem, without changing the outcome, making the application of bargaining solutions to actual problems easier. We compare our axioms to usual independence axioms, and discuss their consistency with usual bargaining solutions. Then, we introduce monotonicity conditions corresponding to the existence of an interest group, i.e., agents ranking the alternatives in the same order. For such monotonic social choice environments, we provide a parameterized family of alternatives that generates the Pareto frontier of the bargaining problem, in line with our previous results. Our analysis illustrates that an axiomatic approach can be useful to foster the application of bargaining solutions, in complement to usual computational methods. (shrink)

We consider a situation where an individual is facing an uncertain situation, but may costly alter his knowledge of the uncertainties. We study in this context how risk aversion may modify the individual search behavior. We consider a one-armed bandit problem (where one arm is safe and the other is risky) and study how the agent risk aversion can change the sequence of arms selected. The main result is that when the utility function is more concave, the agent has more (...) chances to select the safe arm. We also discuss how search is affected by risk aversion. (shrink)

We provide an economic interpretation of the practice consisting in incorporating risk measures as constraints in an expected prospect maximization problem. For what we call the infimum of expectations class of risk measures, we show that if the decision maker (DM) maximizes the expectation of a random prospect under constraint that the risk measure is bounded above, he then behaves as a “generalized expected utility maximizer” in the following sense. The DM exhibits ambiguity with respect to a family of utility (...) functions defined on a larger set of decisions than the original one; he adopts pessimism and performs first a minimization of expected utility over this family, then performs a maximization over a new decisions set. This economic behaviour is called “maxmin under risk” and studied by Maccheroni (Econ Theory 19:823–831, 2002). As an application, we make the link between an expected prospect maximization problem, subject to conditional value-at-risk being less than a threshold value, and a non-expected utility economic formulation involving “loss aversion”-type utility functions. (shrink)