An agency relationship under general conditions of uncertainty: A game theory application to the doctor-patient interaction

The supply of information, particularly of bad news, in an agency relationship is a sensitive issue. We employ a game theory approach to investigate conflicts in the particular case of the doctor–patient relationship when information affects the emotions of patients. The doctor does not know the type of agent and the patient does not know how much information he is given. Hence, the paper obtains results when there is conflict, rather than common interest in the objectives of the two parties. The perfect Bayesian equilibrium describes beliefs and strategies which guarantee adherence to the doctor’s recommendation. We show also that the patient may non-adhere to the recommendation not only when the doctor fails to identify the patient’s needs but also if he falsely believes that the doctor has not done so

Measuring loss aversion under ambiguity: a method to make prospect theory completely observable

We propose a simple, parameter-free method that, for the first time, makes it possible to completely observe Tversky and Kahneman’s (1992) prospect theory. While methods exist to measure event weighting and the utility for gains and losses separately, there was no method to measure loss aversion under ambiguity. Our method allows this and thereby it can measure prospect theory’s entire utility function. Consequently, we can properly identify properties of utility and perform new tests of prospect theory. We implemented our method in an experiment and obtained support for prospect theory. Utility was concave for gains and convex for losses and there was substantial loss aversion. Both utility and loss aversion were the same for risk and ambiguity, as assumed by prospect theory, and sign-comonotonic trade-off consistency, the central condition of prospect theory, held

State dependent choice

We propose a theory of choices that are influenced by the psychological state of the agent. The central hypothesis is that the psychological state controls the urgency of the attributes sought by the decision maker in the available alternatives. While state dependent choice is less restricted than rational choice, our model does have empirical content, expressed by simple "revealed preference" type of constraints on observable choice data. We demonstrate the applicability of simple versions of the framework to economic contexts. We show in particular that it can explain widely researched anomalies in the labour supply of taxi drivers

Making the Anscombe-Aumann approach to ambiguity suitable for descriptive applications

The Anscombe-Aumann (AA) model, originally introduced to give a normative basis to expected utility, is nowadays mostly used for another purpose: to analyze deviations from expected utility due to ambiguity (unknown probabilities). The AA model makes two ancillary assumptions that do not refer to ambiguity: expected utility for risk and backward induction. These assumptions, even if normatively appropriate, fail descriptively. This paper relaxes these ancillary assumptions to avoid the descriptive violations, while maintaining AA\xe2\x80\x99s convenient mixture operation. Thus, it becomes possible to test and apply all AA-based ambiguity theories descriptively while avoiding confounds due to violated ancillary assumptions. The resulting tests use only simple stimuli, avoiding noise due to complexity. We demonstrate the latter in a simple experiment where we find that three assumptions about ambiguity, commonly made in AA theories, are violated: reference independence, universal ambiguity aversion, and weak certainty independence. The second, theoretical, part of the paper accommodates the violations found for the first ambiguity theory in the AA model\xe2\x80\x94Schmeidler\xe2\x80\x99s CEU theory\xe2\x80\x94by introducing and axiomatizing a reference dependent generalization. That is, we extend the AA ambiguity model to prospect theory