Probability weighting and insurance demand in a unified framework

We provide a comprehensive analysis of the impact of probability weighting on optimal insurance demand in a unified framework. We identify decreasing relative overweighting as a new local condition on the probability weighting function that is useful for comparative static analysis. We discuss the effects of probability weighting on coinsurance, deductible choice, insurance demand for low-probability, high-impact risks versus high-probability, low-impact risks, and insurance demand in the presence of nonperformance risk. Probability weighting can make better or worse predictions than expected utility depending on the insurance demand problem at hand

Insurance demand experiments: Comparing crowdworking to the lab

We analyze an insurance demand experiment conducted in two different settings: in-person at a university laboratory and online using a crowdworking platform. Subject demographics differ across the samples, but average insurance demand is similar. However, choice patterns suggest online subjects are less cognitively engaged—they have more variation in their demand and react less to changes in exogenous factors of the insurance situation. Applying data quality filters does not lead to more comparable demand patterns between the samples. Additionally, while online subjects pass comprehension questions at the same rate as in-person subjects, they show more random behavior in other questions. We find that online subjects are more likely to engage in “coarse thinking,” choosing from a reduced set of options. Our results justify caution in using crowdsourced subjects for insurance demand experiments. We outline some best practices which may help improve data quality from experiments conducted via crowdworking platforms

On the change of risk aversion in wealth: a field experiment in a closed economic system

How does risk aversion change in wealth? To answer this question, we implemented a field experiment in the form of a free-to-play mobile game. Players made lottery choices at various points in the game and at different levels of in-game wealth. Since the game was designed as a closed economic system, that is, wealth could not be transferred into or out of the game, only in-game wealth was relevant for players’ choices. Analyzing the choices of over 2000 players, we find evidence for decreasing absolute risk aversion and decreasing relative risk aversion. We also find evidence of an “always safe” heuristic in a subgroup of decisions and observe a tendency of players to act according to the “hot hand fallacy”. Our research design allows us to exclude inertia and lets us analyze lottery stakes of significant size relative to in-game wealth. Our results render implications for theoretical research, empirical studies, and for the optimal design of financial products

Estimating extreme cancellation rates in life insurance

This paper assesses the risk of a mass lapse event in life insurance. The rarity of the event and the complexity of policyholder behavior make the risk assessment of such a scenario difficult. Using a simulation study, we evaluate how different estimation methods can assess the risk of this scenario, using panel data at the company level. We then use the best-performing method to estimate the probability distribution function of a mass cancellation event in the United States and Germany. We identify dependencies of the event on company and country characteristics, which have not been taken into account by regulating agencies. We also find that the current mass lapse scenario in Solvency II has no empirical foundation for the German market. We show that an empirically valid scenario leads to a significantly lower solvency capital requirement for the average German life insurer. © 2021 The Authors. Journal of Risk and Insurance published by Wiley Periodicals LLC on behalf of American Risk and Insurance Association

Probability elicitation under severe time pressure: a rank-based method

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