We offer a structured literature survey of experimental studies involving insurance demand choices and their experimental methodology. With this, we aim to fulfill two goals. First, we want to give an overview of the status of the literature as is. Second, the overview of the methodology provides researchers with an idea of how insurance demand experiments can be designed and what the advantages and disadvantages of the different design aspects are. We thus offer a resource for the design of future experiments.

Heuristic thinking can influence human behavior in decisions under risk and uncertainty. In an experimental setting, we study whether emotional activation primes individuals to use the representativeness heuristic and the affect heuristic. We observe the decision behavior of 272 subjects in a computerbased experiment that differentiates between incidental affect and integral affect. Positive incidental affect and integral affect increase the use of the representativeness heuristic, while negative incidental affect has no effect. Our findings have statistical and economic significance and carry implications for insurance companies and regulators.

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Prevention efforts, such as quitting smoking, flu vaccination, and exercising, are of crucial importance in health policy, but people tend to undertake too few of them. The main reason is that most prevention efforts only reduce but do not completely eliminate the risk of poor health. This makes it harder for people to assess the benefits of prevention, because they tend to misperceive and transform probabilities. In “When Risk Perception Gets in the Way: Probability Weighting and Underprevention,” Baillon et al. introduce psychological insights (probability weighting) in a model of optimal decision making and show that most people undertake too little prevention when the risk of poor health is between 10% and 80%. The paper discusses several policy measures to make people spend more on prevention.

Can measured risk attitudes and associated structural models predict insurance demand? In an experiment (n = 1,730), we elicit measures of utility curvature, probability weighting, loss aversion, and preference for certainty and use them to parameterize seventeen common structural models (e.g., expected utility, cumulative prospect theory). Subjects also make twelve insurance choices over different loss probabilities and prices. The insurance choices show coherence and some correlation with various risk-attitude measures. Yet all the structural models predict insurance poorly, often less accurately than random predictions. Simpler prediction heuristics show more promise for predicting insurance choices across different conditions.

Can measured risk attitudes and associated structural models predict insurance demand? In an experiment (n = 1,730), we elicit measures of utility curvature, probability weighting, loss aversion, and preference for certainty and use them to parameterize seventeen common structural models (e.g., expected utility, cumulative prospect theory). Subjects also make twelve insurance choices over different loss probabilities and prices. The insurance choices show coherence and some correlation with various risk-attitude measures. Yet all the structural models predict insurance poorly, often less accurately than random predictions. Simpler prediction heuristics show more promise for predicting insurance choices across different conditions.

Probability elicitation protocols are used to assess and incorporate subjective probabilities in risk and decision analysis. While most of these protocols use methods that have focused on the precision of the elicited probabilities, the speed of the elicitation process has often been neglected. However, speed is also important, particularly when experts need to examine a large number of events on a recurrent basis. Furthermore, most existing elicitation methods are numerical in nature, but there are various reasons why an expert would refuse to give such precise ratio-scale estimates, even if highly numerate. This may occur, for instance, when there is lack of sufficient hard evidence, when assessing very uncertain events (such as emergent threats), or when dealing with politicized topics (such as terrorism or disease outbreaks). In this article, we adopt an ordinal ranking approach from multicriteria decision analysis to provide a fast and nonnumerical probability elicitation process. Probabilities are subsequently approximated from the ranking by an algorithm based on the principle of maximum entropy, a rule compatible with the ordinal information provided by the expert. The method can elicit probabilities for a wide range of different event types, including new ways of eliciting probabilities for stochastically independent events and low-probability events. We use a Monte Carlo simulation to test the accuracy of the approximated probabilities and try the method in practice, applying it to a real-world risk analysis recently conducted for DEFRA (the U.K. Department for the Environment, Farming and Rural Affairs): the prioritization of animal health threats.

It is part of managerial wisdom that managers need to take risks in order to succeed. This is in stark contrast to the prominent agent-based theories of experiential learning and competitive selection that are known to be biased against risky alternatives in the long run. How can the positive managerial view on risk taking prevail given these results? Qualitative surveys of managers suggest risks to be acceptable if their outcome distribution meets certain criteria. We argue in this paper that these criteria are widely congruent with low downside risk. Using a novel simulation design, we analyze the effects of experiential learning and competitive selection on downside risk preferences in an ecology of agents. In the long run, experiential learning implies weak downside risk seeking, whereas competitive selection leads to strong downside risk aversion. Furthermore, competitive selection leads to the prevalence of outcome distributions with low downside risk in an ecology, even if they have a significantly higher level of total risk than comparable distributions with more downside risk. We draw implications for empirical studies on managerial behavior. The online appendix is available at https://doi.org/10.1287/orsc.2017.1149 .