Probability weighting and the ‘level’ and ‘spacing’ of outcomes: An experimental study over losses

Etchart-Vincent, Nathalie

doi:10.1007/s11166-009-9066-0

Cited by 33 publications

(11 citation statements)

References 75 publications

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“…To represent heterogeneity, δ is drawn from a random distribution for each household. The parameter value 0.69, found by Tversky and Kahneman, is used as the mean, which is close to the values found by Etchart‐Vincent, and Abdellaoui, and the standard deviation is set at 0.025, which is consistent with estimates of Etchart‐Vincent and Abdellaoui

π_{i} = \frac{p_{i}^{δ}}{{false(p_{i}^{δ} + {(1 - p_{i})}^{δ} false)}^{1 / δ}}

…”

Section: Methodssupporting

confidence: 68%

Integrating Household Risk Mitigation Behavior in Flood Risk Analysis: An Agent‐Based Model Approach

et al. 2016

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Recent studies showed that climate change and socioeconomic trends are expected to increase flood risks in many regions. However, in these studies, human behavior is commonly assumed to be constant, which neglects interaction and feedback loops between human and environmental systems. This neglect of human adaptation leads to a misrepresentation of flood risk. This article presents an agent-based model that incorporates human decision making in flood risk analysis. In particular, household investments in loss-reducing measures are examined under three economic decision models: (1) expected utility theory, which is the traditional economic model of rational agents; (2) prospect theory, which takes account of bounded rationality; and (3) a prospect theory model, which accounts for changing risk perceptions and social interactions through a process of Bayesian updating. We show that neglecting human behavior in flood risk assessment studies can result in a considerable misestimation of future flood risk, which is in our case study an overestimation of a factor two. Furthermore, we show how behavior models can support flood risk analysis under different behavioral assumptions, illustrating the need to include the dynamic adaptive human behavior of, for instance, households, insurers, and governments. The method presented here provides a solid basis for exploring human behavior and the resulting flood risk with respect to low-probability/high-impact risks.

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π_{i} = \frac{p_{i}^{δ}}{{false(p_{i}^{δ} + {(1 - p_{i})}^{δ} false)}^{1 / δ}}

…”

Section: Methodssupporting

confidence: 68%

Integrating Household Risk Mitigation Behavior in Flood Risk Analysis: An Agent‐Based Model Approach

et al. 2016

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“…A general challenge for high loss experiments is finding a way to incentivize decisions, because individuals require significant endowments (Etchart‐Vincent, ). One method that mitigates this complication somewhat is the RPSM.…”

Section: Motivation and Hypothesesmentioning

confidence: 99%

Determinants of Probability Neglect and Risk Attitudes for Disaster Risk: An Online Experimental Study of Flood Insurance Demand among Homeowners

Robinson

Botzen

2019

Risk Analysis

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Little is known about why individuals place either a high or a very low value on mitigating risks of disaster‐type events, like floods. This study uses panel data methods to explore the psychological factors affecting probability neglect of flood risk relevant to the zero end‐point of the probability weighting function in Prospect Theory, and willingness‐to‐pay for flood insurance. In particular, we focus on explanatory variables of anticipatory and anticipated emotions, as well as the threshold of concern. Moreover, results obtained under real and hypothetical incentives are compared in an experiment with high experimental outcomes. Based on our findings, we suggest several policy recommendations to overcome individual decision processes, which may hinder flood protection efforts.

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“…Note that this kind of hypothetical choice has been widely used in experiments involving individual decision making under risk when losses are at stake(Abdellaoui 2000;Etchart-Vincent 2004, 2009Fennema and Van Assen 1999;Abdellaoui et al 2007;Abdellaoui et al 2008).4 Under both RDU and PT, risk attitude is meant to depend on two components, namely attitude toward consequences (encapsulated in utility) and attitude toward probabilities (captured through probability weighting).…”

mentioning

confidence: 99%

Monetary incentives in the loss domain and behavior toward risk: An experimental comparison of three reward schemes including real losses

2010

Self Cite

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International audienceIn the loss domain, both practical and ethical considerations rule out the systematic use of an incentive-compatible procedure involving real losses. The experimental study presented here aims at investigating whether some easier-to-implement procedure could be adequately used. For that purpose, the subjects' degree of risk aversion is compared across three payment conditions: a real-losses condition based on a random-lottery (incentive-compatible) system, which serves as a benchmark, and two challengers, namely a "losses-from-an-initial-endowment" procedure and a hypothetical-losses condition. As a by-product, our experimental design also allows us to investigate the impact of monetary incentives in the gain domain. The main result is twofold: no significant difference arises between the three payment conditions in the loss domain, while real and hypothetical choices significantly differ in the gain domain. Our results suggest that the use of monetary incentives may be more crucial in the gain domain than in the loss domain

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Probability weighting and the ‘level’ and ‘spacing’ of outcomes: An experimental study over losses

Abstract: Individual decision making under risk, Prospect theory, Losses, Probability weighting, C91, D81,

Cited by 33 publications

References 75 publications

Integrating Household Risk Mitigation Behavior in Flood Risk Analysis: An Agent‐Based Model Approach

Integrating Household Risk Mitigation Behavior in Flood Risk Analysis: An Agent‐Based Model Approach

Determinants of Probability Neglect and Risk Attitudes for Disaster Risk: An Online Experimental Study of Flood Insurance Demand among Homeowners

Monetary incentives in the loss domain and behavior toward risk: An experimental comparison of three reward schemes including real losses

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