Changes in Risky Benefits and in Risky Costs: A Question of the Right Order

We organize and extend findings on the comparative static effects of risk changes on optimal behavior in a unifying expected utility model. We determine restrictions on preferences for clear-cut results. Risk increases of a benefit are compensated by lowering exposure to risk. For risk increases of a cost, the response depends on the order of the risk change. This discrepancy arises because even-order risk increases of a cost raise the riskiness of the payoff distribution, whereas odd-order risk increases of a cost reduce it. We identify the stochastic dominance orders to resolve this discrepancy and discuss specific decision problems as applications. This paper was accepted by Manel Baucells, behavioral economics and decision analysis.

OPTIMAL PATENT PROTECTION AND EXPECTED UTILITY MODEL: A TRANSITION ECONOMY EXAMPLE

This paper aims to critically appraise optimal patent protection using the expected utility model from the perspectives of governments to balance the motivation and social use of the intellectual property. In order to achieve this aim, the report has presented the working mechanism of governments towards patent protection; use of utility model by governments; past and present academic investigations on the topic; strategizing behavior of governments towards patents and as a brief example, a case of the transition economy. The expected utility model has provided an effective and efficient framework for the development of patent strategy by governments. The essay has noted that due to the differences between industries and their dynamics, it is expected that diverse patent regimes should be followed to balance the social utility and economic utility of the economic actor to engage in research and development.

Random ambiguity

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.

Discounted and Expected Utility from the Probability and Time Trade-Off Model

This paper shows the interaction between probabilistic and delayed rewards. In decision- making processes, the Expected Utility (EU) model has been employed to assess risky choices whereas the Discounted Utility (DU) model has been applied to intertemporal choices. Despite both models being different, they are based on the same theoretical principle: the rewards are assessed by taking into account the sum of their utilities and some similar anomalies have been revealed in both models. The aim of this paper is to characterize and consider particular cases of the Time Trade-Off (PPT) model and show that they correspond to the EU and DU models. Additionally, we will try to build a PTT model starting from a discounted and an expected utility model able to overcome the limitations pointed out by Baucells and Heukamp.

Expected Utility and Political Decision Making

Expected utility theory is widely used to formally model decisions in situations where outcomes are uncertain. As uncertainty is arguably commonplace in political decisions, being able to take that uncertainty into account is of great importance when building useful models and interpreting empirical results. Expected utility theory has provided possible explanations for a host of phenomena, from the failure of the median voter theorem to the making of vague campaign promises and the delegation of policymaking. A good expected utility model may provide alternative explanations for empirical phenomena and can structure reasoning about the effect of political actors’ goals, circumstances, and beliefs on their behavior. For example, expected utility theory shows that whether the median voter theorem can be expected to hold or not depends on candidates’ goals (office, policy, or vote seeking), and the nature of their uncertainty about voters. In this way expected utility theory can help empirical researchers derive hypotheses and guide them towards the data required to exclude alternative explanations. Expected utility has been especially successful in spatial voting models, but the range of topics to which it can be applied is far broader. Applications to pivotal voting or politicians’ redistribution decisions show this wider value. However, there is also a range of promising topics that have received ample attention from empirical researchers, but that have so far been largely ignored by theorists applying expected utility theory. Although expected utility theory has its limitations, more modern theories that build on the expected utility framework, such as prospect theory, can help overcome these limitations. Notably these extensions rely on the same modeling techniques as expected utility theory and can similarly elucidate the mechanisms that may explain empirical phenomena. This structured way of thinking about behavior under uncertainty is the main benefit provided by both expected utility theory and its extensions.

On the falsifiability and learnability of decision theories

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.

Weather Uncertainty and Efficacy of Fumigation in Tomato Production

This study quantifies the effect of weather factors on fumigation efficacy in terms of weed control, tomato yield, and the overall economic performance of fumigants. High soil temperature was found to reduce the efficacy of all fumigants against nutsedge, while rainfall only reduced the efficacy of a limited number of fumigants. The fumigants’ economic performance over a range of weather conditions was further simulated to identify the fumigant that is most effective under diverse weather conditions. The results show that although 1,3-D:Pic:Kpam outperforms methyl bromide over the experiment period, methyl bromide is still the best treatment when accounting for the impact of weather variability. The study illustrates the sensitivity of fumigant efficacy to weather conditions and the importance of achieving consistent and sustainable efficacy. The regression model and the expected utility model, along with the simulation techniques, form a useful tool that can be applied across regions or crops.

A Bayesian Method for Characterizing Population Heterogeneity

A stylized fact from laboratory experiments is that there is much heterogeneity in human behavior. We present and demonstrate a computationally practical non-parametric Bayesian method for characterizing this heterogeneity. In addition, we define the concept of behaviorally distinguishable parameter vectors, and use the Bayesian posterior to say what proportion of the population lies in meaningful regions. These methods are then demonstrated using laboratory data on lottery choices and the rank-dependent expected utility model. In contrast to other analyses, we find that 79% of the subject population is not behaviorally distinguishable from the ordinary expected utility model.

The Expected Utility of Insurance

This chapter offers a new version of popular “insurance” models of judicial independence, in which the competitiveness of the electoral arena induces leaders to prefer more independent courts, as a means of offering policy and personal security if they lose power. That is, paying the “premium” of increased constraints on behavior imposed by independent courts now for the insurance of protection in the future if out of office. The crux of the argument is that the risks associated with losing power in autocratic regimes are greater than in democracies, and therefore competition should be more salient in dictatorships than democracies. The stakes are higher because autocratic power means access to wealth and state resources in a way rarely equaled in democratic regimes, and more importantly the likelihood of being punished after leaving office is greater for former autocrats. Judiciaries exercising greater independence, however, can minimize the risks of being a former leader, and the chapter leverages this finding to develop an expected utility model, the empirical implication of which is higher salience of competition—when present—in autocracies. Unlike previous theories of how competition affects independence, this model integrates both the likelihood of losing office and the risks associated with such an outcome, and thus allows us to examine the phenomena across the democracy/dictatorship divide.