When Risk Perception Gets in the Way: Probability Weighting and Underprevention

Baillon, Aurélien; Bleichrodt, Han; Emirmahmutoglu, Aysil; Jaspersen, Johannes G.; Peter, Richard

doi:10.1287/opre.2019.1910

Cited by 21 publications

(15 citation statements)

References 88 publications

(101 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Masuda and Lee ( 2019 ) also find that their subjects exert too little preventive effort regardless of the timing of the loss. They argue that their results cannot be explained by expected utility and suggest probability weighting as an alternative explanation, in line with the analysis of Baillon et al ( 2020 ).…”

Section: Introductionsupporting

confidence: 77%

“…The above analyses are based on expected utility. Baillon et al ( 2020 ) consider rank-dependent utility (prospect theory for losses) and derive the implications of probability weighting on prevention, which, as we noted above, is equivalent to full insurance with nonperformance risk. They show that for intermediate probabilities, inverse S-shaped probability weighting, the most commonly observed case, will lead to underinsurance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Insurance decisions under nonperformance risk and ambiguity

2021

Self Cite

View full text Add to dashboard Cite

An important societal problem is that people underinsure against risks that are unlikely or occur in the far future, such as natural disasters and long-term care needs. One explanation is that uncertainty about the risk of non-reimbursement induces ambiguity averse and risk prudent decision makers to take out less insurance. We set up an insurance experiment to test this explanation. Consistent with the theoretical predictions, we find that the demand for insurance is lower when the nonperformance risk is ambiguous than when it is known and when decision makers are risk prudent. We cannot attribute the lower take-up of insurance to our measure of ambiguity aversion, probably because ambiguity attitudes are richer than aversion alone.

show abstract

Section: Introductionsupporting

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Insurance decisions under nonperformance risk and ambiguity

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Any inverse S-shaped probability weighting function with fixed point p * and inflection point p has two points p ′ and p ′′ , one smaller and one larger than p * and p , which define the likelihood insensitivity region. For p ∈ (p � , p �� ) the probability weighting function is flatter than the identity, w � (p) < 1 , while it is steeper than the identity on (0, p � ) ∪ (p �� , 1) , see Lemma 1 in Baillon et al (2020). Likewise, any S-shaped probability weighting function has two points p ′ and p ′′ such that w � (p) > 1 for p ∈ (p � , p �� ) and w � (p) < 1 for p ∈ (0, p � ) ∪ (p �� , 1) .…”

Section: Decreasing Relative Overweightingmentioning

confidence: 99%

Probability weighting and insurance demand in a unified framework

Jaspersen

Peter

Ragin

2022

Geneva Risk Insur Rev

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…However, this alternative treatment would not be as risky and as effective as the original treatment option. 11 Explicitly,…”

Section: Decision Thresholds Under Therapeutic Ambiguitymentioning

confidence: 99%

Decision Thresholds for Medical Tests Under Ambiguity Aversion

Sevim

Felder

2022

Front.Health Serv.

View full text Add to dashboard Cite

We consider medical decision-making under diagnostic and therapeutic uncertainty and analyze how ambiguity aversion affects the decisions to test and treat, thereby contributing to the understanding of the observed heterogeneity of such decisions. We show that under diagnostic ambiguity (i.e., the probability of disease is ambiguous), prior testing becomes more attractive if the default option is no treatment and less so if the default option is treatment. Conversely, with therapeutic ambiguity (i.e., the probability of a successful treatment is ambiguous), ambiguity aversion reduces the tolerance toward treatment failure so that the test option is chosen at a lower probability of failure. We differentiate between conditional and unconditional ambiguity aversion and show that this differentiation has implications for the propensity to test. We conclude by discussing the normative scope of ambiguity aversion for the recommendations and decisions of regulatory bodies.

show abstract

When Risk Perception Gets in the Way: Probability Weighting and Underprevention

Cited by 21 publications

References 88 publications

Insurance decisions under nonperformance risk and ambiguity

Insurance decisions under nonperformance risk and ambiguity

Probability weighting and insurance demand in a unified framework

Decision Thresholds for Medical Tests Under Ambiguity Aversion

Contact Info

Product

Resources

About