Econ Papers Daily / 期刊论文
少即是多：日益复杂的模糊性下的决策
Less is more: Decision-making under increasingly complex ambiguity
摘要
This paper examines whether increasingly complex decisions under ambiguity are better described by more complex behavioural models. Using an allocation experiment, we vary structural complexity through the number of possible outcomes and decision stages and compare Expected Utility (EU), αMaxMin (AMM), and Choquet Expected Utility (CEU). EU is the most frequently selected model according to AICc in all three ambiguous-lottery parts, although the strength of its relative advantage varies across tasks, with CEU remaining competitive in the most complex part. Estimated risk-attitude parameters exhibit a relatively stable central pattern across tasks, whereas ambiguity-attitude parameters vary more noticeably across task structures. Overall, the results show that explicitly incorporating ambiguity attitudes does not uniformly improve penalized model fit as structural complexity increases. Rather, the empirical value of more flexible ambiguity models depends on the structure of the choice environment. The findings highlight the importance of evaluating model complexity empirically rather than assuming that structurally more complex decisions necessarily require more flexible behavioural models.
中文摘要
本文考察在模糊性下日益复杂的决策是否更适合用更复杂的行为模型来描述。通过一项分配实验，我们借助可能结果的数量和决策阶段来改变结构复杂性，并比较期望效用（EU）、αMaxMin（AMM）和Choquet期望效用（CEU）。根据AICc，在全部三个模糊彩票部分中，EU都是最常被选择的模型，尽管其相对优势的强度因任务而异，CEU在最复杂的部分仍具有竞争力。估计的风险态度参数在各任务间呈现相对稳定的中心模式，而模糊态度参数则随任务结构变化更为明显。总体而言，结果表明，随着结构复杂性增加，明确纳入模糊态度并不能一致地改善惩罚化模型拟合。相反，更灵活的模糊性模型的实证价值取决于选择环境的结构。这些发现凸显了从实证上评估模型复杂性的重要性，而不是假定结构上更复杂的决策必然需要更灵活的行为模型。