A Revealed Preference Test for Choquet and Max-Min Expected Utility with Ambiguity Aversion

The first paper of this dissertation in Chapter II, “Insurance for Technology Adoption: An Experimental Evaluation of Schemes and Subsidies with Maize Farmers in Mexico”, analyzes experimentally how bundling the purchase of a risky technology, namely a higher yielding maize seed variety, with different insurance schemes, affects the total take-up of that variety. In this regard, the paper looks at the effects of (1) partial insurance versus full insurance, (2) geographical versus local basis risk, and (3) fair versus below-fair premium. This is the first paper to evaluate insurance schemes with different levels of risk reduction, basis risk and premium subsidies regarding their effect on technology adoption. The results add to the debate on insurance serving as a potential tool for incentivizing agricultural producers to adopt more productive, but more risky technologies, and thereby enabling them to escape poverty (Carter et al. 2016; Fan et al. 2013; Lybbert and Carter 2014; Nicola 2015; World Bank 2013). The second paper in Chapter III, “The Relationship between Farmers’ Shock Experiences and their Uncertainty Preferences - Experimental Evidence from Mexico” addresses the relationship between farmers’ uncertainty preferences, sociodemographic characteristics and their experience of adverse harvest shocks. Uncertainty preferences refer to a range of preference parameters as derived from Cumulative Prospect Theory (Kahneman and Tversky 1979; Tversky and Kahneman 1992), namely risk aversion, loss aversion and probability weighting, as well as ambiguity aversion (Ellsberg 1961). A series of incentivized lottery games are used to estimate these parameters with the sample of Mexican maize farmers, controlling for (1) sociodemographic characteristics and (2) the severity of experienced maize harvest losses. While there are several field studies examining the effect of shocks on risk preferences with subjects from developing countries, only few look at preferences beyond Expected Utility Theory and take into account Cumulative Prospect Theory, and none has looked at ambiguity aversion. Therefore, this paper sheds light on the role that the experience of adverse random shocks, as well as a range of sociodemographic variables, have in explaining one’s uncertainty preferences. The third paper in Chapter IV, “Formal Insurance, Risk Sharing, and the Dynamics of Other-Regarding Preferences”, analyzes how selectively providing formal insurance to members of a risk sharing network affects informal transfers and, subsequently, the dynamics of other-regarding preferences within that network. Many poor households in developing countries are excluded from formal financial markets and therefore rely on the mutual exchange within informal risk sharing networks to protect themselves against adverse income shocks. Social interactions in the aftermath of such shocks have been found to strengthen the social ties among members of these networks, while formal insurance has been found to crowd-out these transfers. Similarly, this third paper finds that when some members of risk sharing networks become formally insured, it affects the informal exchange of transfers among members, as well as their other-regarding preferences. This is the first study to explore the effect of insurance on other-regarding preferences in that context. In order to do so, an incentivized, three-stage experimental design with a baseline and an ex-post measurement of altruism, trust and trustworthiness through dictator and trust games is implemented with random and anonymous groups of three. Between the baseline and the ex-post measurement, a solidarity game is played with the same anonymous groups as in the ex-post measurement of other-regarding preferences, during which the shock structure and the availability of formal insurance are varied exogenously. The findings suggest that the effect of insurance depends on (1) the covariance structure of shocks and (2) is different for the insured and non-insured members within a network. Insurance either decreases trust levels of the uninsured or increases trust levels of the insured subjects towards the other network members, depending on whether the shocks affects one or more than one network member at a time. Trustworthiness and altruism remain unaffected by insurance. Furthermore, the analysis indicates that the results are driven by a change in the dynamics of the transfer behavior within the network induced by formal insurance. Specifically, there is evidence that subjects increase trust levels towards their network members after receiving higher transfers relative to the maximum possible value from them, but not after receiving higher transfers in absolute terms.

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.

Expected utility theory, probably the most widely accepted normative theory for decision making under risk, has several required properties. Since different sets of axioms can be combined to result in the expected utility model, the term property can refer to either an axiom or a characteristic resulting from combinations of axioms. Since most properties are seen as appropriate components of a normative theory of choice, they could be referred to as principles or desiderata to emphasize their normative status (see Howard, 1992). But, not all properties hold consistently in choices made by experimental subjects. The resulting conflict between the normative appeal of expected utility theory and its shortcomings as a descriptive model of choice has been a motivating force in the development of generalized utility theories which relax the requirement that various properties hold.KeywordsUtility TheoryProspect TheoryUtility ModelIndifference CurveExpect Utility TheoryThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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.

Expected Utility and Sequential Elimination Models of Career Decision Making

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.

Rank-Dependent Pessimism and its divergence from Maxmin Expected Utility

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

<p>Forecasts have the potential to improve decision-making but have not been widely evaluated because current forecast value methods have critical limitations. The ubiquitous Relative Economic Value (REV) is limited to binary decisions, cost-loss economic model, and risk neutral decision-makers. Expected Utility Theory can flexibly model more real-world decisions, but its application in forecasting has been limited and the findings are difficult to compare. To enable a systematic comparison of these methods a new metric, Relative Utility Value (RUV), is developed based on Expected Utility Theory. It has the same interpretation as REV but is more flexible and able to handle a wider range of real-world decisions because all aspects of the decision-context are user-defined. Also, when specific assumptions are imposed it is shown that REV and RUV are equivalent. We demonstrate the key differences and similarities between the methods with a case study using probabilistic subseasonal streamflow forecasts in a catchment in the Southern Murray-Darling Basin of Australia. This showed that for most decision-makers the ensemble forecasts were more valuable than a reference climatology for all lead-times (max 30 days), decision types (binary, multi-categorical, and continuous-flow), and levels of risk aversion. Risk aversion had a mixed impact across the different decision-types and the key driver was found to be the specific decision thresholds relative to the damage function. The generality of RUV makes it applicable to any domain where forecast information is used for making decisions, and the flexibility enables forecast assessment tailored to specific decisions and decision-makers. It complements forecast verification and enables assessment of forecast systems through the lens of customer impact.</p>

We proffer a method to assess the adequacy of expected utility theory (EUT) in empirical studies involving discrete and continuous choices. The method calibrates a utility function to revealed choices and rejects EUT for absurd degrees of implied concavity over the wealth at risk. We find EUT rejections in cases where risk‐averse choices are made between gambles with similar stakes, regardless of their absolute sizes. EUT is shown to be applicable only when expected payoffs of gambles are similar, or when more than half of wealth is at risk. We illustrate our method with an application to agricultural production.

Maxmin expected utility in Savage's framework

One important aspect of risk analysis is the individual and societal comparison of probability distributions over a set of undesirable outcomes. To compare all possible distributions it is mandatory to assign one single value (called an index) to each distribution. Typical indices are the expected value and the expected utility model; the paper notes some theoretical and practical limitations of these indices. The paper briefly describes the author’s linearized moments model (LMM) that gives consideration to individual risk perceptions and that takes into account the shape of the probability distribution including the low-probability high-consequence tail. The LMM solves the main theoretical and practical inconsistencies of the expected utility model. It is noted that current indices like the expected value, the expected utility model and the prospect theory are special cases of the LMM.

This paper surveys nearly two hundred scholarly works that use mathematical methods, which include stochastic models, difference and differential equation models, expected utility models, and various types of game theoretic models, to study domestic political conflict (DPC), which includes terrorism, guerrilla wars and insurrections. A citation count reveals that the DPC articles surveyed here cite less than three quarters of an article from within their own DPC modelling tradition and cite less than two articles from any DPC modelling tradition. The only exceptions to the rule that “nobody cites nobody else” are the stochastic and expected utility modelers. I conclude that the “field” of formal models of DPC hardly exists: few authors read other authors, few articles cite other articles, few models build on other models. Several suggestions aimed at promoting greater accumulation in formal models of DPC are offered.

We analyze three explanations of candidate choice in presidential nomination campaigns: (1) a preference model, which contends that nomination choice is based on the ideological, issue, or candidate preferences of the voter; (2) a candidate-chances explanation that argues choice is motivated by the chances candidates have of winning the nomination or the general election; and (3) an expected utility model that includes both preferences and candidate chances by discounting ideological, issue, and candidate-trait preferences by the perceived chances each candidate has of winning in November. We test these explanations on samples of Democratic caucus attenders and state convention delegates from Iowa in 1984. The expected utility model most successfully predicts candidate choice in both samples. Even with appropriate controls for candidate affect, both preference and candidate chances measures have significant independent effects on candidate choice. Our analysis suggests that the interests of individual nomination participants and party organizations may not be as disparate as many critics of the contemporary process believe.