Optimal insurance deductibles under limited information
研究了投保人在对未来损失和自身效用函数信息有限的情况下，如何确定最优保险免赔额，并提出了易于实施的近似决策规则。
When determining the optimal deductible level for an insurance policy, a policyholder faces two limitations. First, uncertainty arises from the randomness of future losses. Second, the opacity of the functional forms of the policyholder's loss distribution and utility function contributes to additional limitations. While the academic literature focuses on the former, we additionally include limited information on these functional forms in our model setting to reflect real-world decision-making. That is, we draw on an expected utility framework and analyze the relationship between optimal deductible levels under limited and full information. We also derive several decision rules under limited information in order to approximate the optimal deductible level under full information. To support real-world decision-making, these rules could be easily implemented in an online decision aid offered by an insurance broker, a comparison portal for insurance contracts or a consumer protection agency.