The Dual Impact of Loss Aversion on Insurance Decision-Making: Mechanisms and Behavioral Interventions
Bibliographic record
Abstract
Loss aversion, a cornerstone concept in behavioral economics, profoundly influences insurance decision-making by amplifying individuals’ sensitivity to potential losses over equivalent gains. This study investigates the bidirectional effects of loss aversion across diverse risk scenarios—public health crises, natural disasters, and cybersecurity threats—and proposes behavioral interventions to reconcile its dual role as both a driver of risk mitigation and a source of cognitive bias. Through integrating prospect theory with empirical case analyses, this finding demonstrate that loss aversion leads to irrational insurance demand surges (e.g., a 58% increase in health insurance purchases during COVID-19 despite a 0.3% severe illness rate) while simultaneously causing underinsurance in imperceptible risks (e.g., only 17% of small businesses purchasing cyber insurance despite a 22% attack probability). Behavioral strategies such as default options, framing effects, and risk visualization tools are shown to optimize decision efficiency by recalibrating loss aversion’s psychological weight. This research contributes to both theoretical advancements in behavioral economics and practical applications for insurance product design and policy implementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".