Maxmin expected utility in Savage's framework

We introduce a model of random ambiguity aversion. Choice is stochastic due to unobserved shocks to both information and ambiguity aversion. This is modeled as a random set of beliefs in the maxmin expected utility model of Gilboa and Schmeidler (1989). We characterize the model and show that the distribution of ambiguity aversion can be uniquely identified from binary choices. A novel stochastic order on random sets is introduced that characterizes greater uncertainty aversion under stochastic choice. If the set of priors is the Aumann expectation of the random set, then choices satisfy dynamic consistency. This corresponds to an agent who knows the distribution of signals but is uncertain about how to interpret signal realizations. More broadly, the analysis of stochastic properties of random ambiguity attitudes provides a theoretical foundation for the study of other random nonlinear utility models.

We develop a revealed preference test for the Choquet expected utility model with ambiguity aversion, which does not rely on specific functional form assumptions on the utility index. It is computationally efficient if the number of states is not too large, even for a large number of observations. This is a nice feature compared to other existing revealed preference tests for decision models with ambiguity. We illustrate the usefulness of our results by implementing our test on two experimental datasets from the literature, and we compare the empirical fit of this model to the subjective expected utility model. (JEL C91, D81, D91, G41)

Muth’s Rational Expectations Hypothesis, That “…The Subjective Probability Distributions Are Distributed Around an Objective Probability Distribution, for a given Information Set…” is an Oxymoron. It Can Be Corrected by Amending It to Read “…The Subjective Probability Distributions Are Distributed Around a Subjective Consensus Probability Distribution, for a given Information Set…”

Subjective uncertainty is characterized by ambiguity if the decision maker has an imprecise knowledge of the probabilities of payoff relevant events. In such an instance, the decision maker's beliefs are better represented by a set of probability functions than by a unique probability function. An ambiguity averse decision maker adjusts his choice on the side of caution in response to his imprecise knowledge of the odds. The non-additive expected utility model allows a formal characterization of such behavior. Using this model, this paper shows that ambiguity aversion can explain the existence of incomplete contracts. The setting for the demonstration is the investment hold-up model which has been the focus of much of the recent research on the implications of incomplete contracts.

Allais, Ellsberg, and Preferences for Hedging

Two of the most well-known regularities of preferences under risk and uncertainty are ambiguity aversion and the Allais paradox. We study the behavior of an agent who can display both tendencies at the same time. We introduce a novel notion of preference for hedging that applies to both objective lotteries and uncertain acts, and captures both aversion to ambiguity and attraction towards certainty in objective lotteries. We show that this axiom, together with other standard axioms, is equivalent to two representations that generalize the MaxMin Expected Utility model of Gilboa and Schmeidler (1989). In both representations the agent evaluates ambiguity using multiple priors, but does not use Expected Utility to evaluate objective lotteries. In the rst representation, lotteries are evaluated by distorting probabilities as in the Rank Dependent Utility model, but using the worst from a set of such distortions. In the second, equivalent, representation the agent treats objective lotteries as ‘ambiguous objects,’ and uses a set of priors to evaluate them. We show that a preference for hedging is not sucient to guarantee an Ellsberg-like behavior if the agent violates Expected

The Distribution of Ambiguity Attitudes

We propose and characterize a model of preferences over acts such that the decision maker prefers act f to act g if and only if E μ φ( E π u○f) ⩾ E μ φ( E π u○g), where E is the expectation operator, u is a von Neumann-Morgenstern utility function, φis an increasing transformation, and μis a subjective probability over the set Πof probability measures πthat the decision maker thinks are relevant given his subjective information. A key feature of our model is that it achieves a separation between ambiguity, identified as a characteristic of the decision maker's subjective beliefs, and ambiguity attitude, a characteristic of the decision maker's tastes. We show that attitudes toward pure risk are characterized by the shape of u, as usual, while attitudes toward ambiguity are characterized by the shape of φ. Ambiguity itself is defined behaviorally and is shown to be characterized by properties of the subjective set of measures Π. One advantage of this model is that the well-developed machinery for dealing with risk attitudes can be applied as well to ambiguity attitudes. The model is also distinct from many in the literature on ambiguity in that it allows smooth, rather than kinked, indifference curves. This leads to different behavior and improved tractability, while still sharing the main features (e.g., Ellsberg's paradox). The maxmin expected utility model (e.g., Gilboa and Schmeidler (1989)) with a given set of measures may be seen as a limiting case of our model with infinite ambiguity aversion. Two illustrative portfolio choice examples are offered. Copyright The Econometric Society 2005.

We study the degree of falsifiability of theories of choice. A theory is easy to falsify if relatively small data sets are enough to guarantee that the theory can be falsified: the Vapnik–Chervonenkis (VC) dimension of a theory is the largest sample size for which the theory is “never falsifiable.” VC dimension is motivated strategically. We consider a model with a strategic proponent of a theory and a skeptical consumer, or user, of theories. The former presents experimental evidence in favor of the theory; the latter may doubt whether the experiment could ever have falsified the theory.We focus on decision‐making under uncertainty, considering the central models of expected utility, Choquet expected utility, and max–min expected utility models. We show that expected utility has VC dimension that grows linearly with the number of states, while that of Choquet expected utility grows exponentially. The max–min expected utility model has infinite VC dimension when there are at least three states of the world. In consequence, expected utility is easily falsified, while the more flexible Choquet and max–min expected utility are hard to falsify. Finally, as VC dimension and statistical estimation are related, we study the implications of our results for machine learning approaches to preference recovery.

This paper utilizes panel data from rural India on a daily rated labour market to examine how workers deal with the uncertainty which arises from the existence of involuntary unemployment. In particular, it measures the relative explanatory power of the three models which the literature suggests should better fit the conditions prevailing in this market: the Expected Utility Model (EUM) with linear objective probabilities, the EUM with linear subjective probabilities and the EUM with non-linear subjective probabilities. The econometric analysis indicates that the EUM with linear has to be preferred to that with non-linear probabilities. Moreover, it supports the hypothesis that the decision making model is under uncertainty and that people work out their subjective probabilities through their past experiences. Finally, agents in the sample turn out to be risk-averse and not to have a positive reservation wage.

A number of classical as well as quite new utility representations for gains are explored with the aim of understanding the behavioral conditions that are necessary and sufficient for various subfamilies of successively stronger representations to hold. Among the utility representations are: ranked additive, weighted, rank-dependent (which includes cumulative prospect theory as a special case), gains decomposition, subjective expected, and independent increments*, where * denotes something new in this article. Among the key behavioral conditions are: idempotence, general event commutativity*, coalescing, gains decomposition, and component summing*. The structure of relations is sufficiently simple that certain key experiments are able to exclude entire classes of representations. For example, the class of rank-dependent utility models is very likely excluded because of empirical results about the failure of coalescing. Figures 1–3 summarize some of the primary results.

Expected Utility and Sequential Elimination Models of Career Decision Making

This paper proposes a decomposition of the cost of risk (as measured by a risk premium) across intervals/quantiles of the payoff distribution. The analysis is based on general smooth risk preferences. While this includes the expected utility model as a special case, the investigation is done under a broad class of non-expected utility models. We decompose the risk premium into additive components across quantiles. Defining downside risk as the risk associated with a lower quantile, this provides a basis to evaluate the cost of exposure to downside risk. We derive a local measure of the cost of risk associated with each quantile. It establishes linkages between the cost of risk, risk preferences and the distribution of risky prospects across quantiles (as measured by quantile variance and skewness). The analysis gives new and useful information on how risk aversion, exposure to downside risk and departures from the expected utility model interact as they affect the risk premium.

One area of research concerning the demand for insurance focuses on determining the optimal form for the indemnification function. This literature shows that from the viewpoint of the insured the deductible form for the indemnification function is optimal under quite general circumstances both in the expected utility model and in nonexpected utility models that preserve second degree stochastic dominance.1 This result depends on the way in which deductible insurance is priced. How other forms of insurance are priced obviously matters if the deductible form is to be optimal. It is also the case that the pricing of deductible policies relative to one another plays a role in demonstrating the optimality of the deductible form.

In this chapter a new model of choice under risk within the framework of RDU theory is offered. The suggested model contains the expected utility model as a special case. Compared to anticipated utility theory it is more general in one respect and more restrictive in another. It is more general since it allows the probability distortion to depend on the prizes available. But it restricts on the other hand these distortions to be homogeneous in the probabilities.