Learning-driven prospect and household risk mitigation strategy under uncertain climate risks: a dynamic cumulative prospect theory approach

Dey, Jishu, Mishra, Tapas and Homroy, Swarnodeep (2027) Learning-driven prospect and household risk mitigation strategy under uncertain climate risks: a dynamic cumulative prospect theory approach. Risk Analysis. (Submitted)

Abstract

How do households perceive and value the growing risks of climate change (such as floods, fires, and windstorms) in their willingness to pay a certain housing insurance premium? Does (the speed of) learning about the (in)frequency of climate-related risks shape households’ decisions to endogenize it in their future premium decisions? In this paper, we introduce the role of learning in the characterisation of a prospect function of a household. Because households invariably make decisions under uncertainty, how certain risks (such as the probability of climate-related risks) develop over time, a class of households may see their urgencies differently from others. Through a learning-based approach, we formalize an agent-based decision, particularly a cumulative prospect in a dynamic setting. This setting allows households to categorise risks (as high, low, and medium) and accordingly, as risks evolve and move to a different epoch, households’ learning-driven memory shapes their cumulative prospects. We characterise households’ decision problems in a dynamic cumulative prospect theoretic framework and show how they can derive optimal insurance premiums under heterogeneous climate risks. Simulation results present various predictive scenarios of the evolving nature of climate risks and associated insurance objectives. Our results offer valuable insights into the role of learning in designing optimal insurance, considering the demand of consumers’ risk functions.

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Contributors

Author: Jishu Dey

Author: Tapas Mishra

Author: Swarnodeep Homroy

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