A Unilateral Accident Model under Ambiguity
Standard accident models are based on the expected utility framework and represent agentsâ€™ beliefs about accident risk with a probability distribution. Consequently, they do not allow for Knightian uncertainty, or ambiguity, with respect to accident risk and cannot accommodate optimism (ambiguity loving) or pessimism (ambiguity aversion). This paper presents a unilateral accident model under ambiguity. To incorporate ambiguity, I adopt the Choquet expected utility framework and represent the injurerâ€™s beliefs with a neoadditive capacity. I show that neither strict liability nor negligence is generally efficient in the presence of ambiguity. In addition, I generally find that the injurerâ€™s level of care decreases (increases) with ambiguity if he is optimistic (pessimistic) and decreases (increases) with his degree of optimism (pessimism). The results suggest that negligence is more robust to ambiguity and, therefore, may be superior to strict liability in unilateral accident cases. Finally, I design an efficient ambiguity-adjusted liability rule.
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