AI 中文总结
本文分析半参数离散选择模型中选择概率显示确定性效用指数排序的条件，拓展二元选择的分位数约束至多项选择，明确了多项选择下排序恢复的局限性。
AI 中文摘要
本文分析半参数离散选择模型中，选择概率何时能显示确定性效用指数的排序。研究从二元选择入手，指出分位数阈值可保证排序恢复，这类阈值既可能源于可交换不可观测变量下偏离效用最大化的行为（如有限注意力），也可能源于标准效用最大化下的不可交换不可观测变量。随后将这些行为与分布路径拓展至多项选择：在有限注意力下，注意力概率的平衡约束会产生全局线性排序划分，该划分对不可观测变量的分布具有鲁棒性，且在效用指数差异存在足够丰富的联合变异时也是必要的；若缺乏所需的注意力约束，相反排序会产生重叠的概率像。在不可交换不可观测变量下，通常不存在可比的分布一致划分，但固定分布时，无论是行为拓展还是分布拓展，都可通过从标准化效用差异到选择概率的单射非线性映射实现排序恢复。综上，研究结果区分了分布鲁棒的全局排序划分与固定不可观测变量分布下的排序恢复，明确了将二元分位数约束拓展至多项选择的局限性。
英文摘要
This paper analyzes when choice probabilities reveal rankings of deterministic utility indices in semiparametric discrete choice models. It begins with binary choice, where quantile thresholds guarantee ranking recovery, and shows that such thresholds can arise either from behavioral departures from utility maximization (e.g., limited attention) under exchangeable unobservables, or from non-exchangeable unobservables under standard utility maximization. These behavioral and distributional routes are then extended to multinomial choice. Under limited attention, balance restrictions on attention probabilities yield global linear ranking partitions which are robust to the distribution of unobservables and, given sufficiently rich joint variation in the differences of utility indices, are also necessary. Absent the required attention restrictions, opposite rankings can produce overlapping probability images. Under non-exchangeable unobservables, a comparable distribution-uniform partition generally need not exist. Holding the distribution fixed, however, ranking recovery remains possible via an injective nonlinear map from normalized utility differences to choice probabilities under both behavioral and distributional extensions. Together, the results distinguish distribution-robust global ranking partitions from ranking recovery with a fixed distribution of unobservables and clarify the limits of extending binary quantile restrictions to multinomial choice.
