发表机构
Economic Research Unit, ISI Kolkata; Department of Economics, Ashoka University（印度统计研究所经济研究单元; 阿肖卡大学经济学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出有限注意下的属性随机选择模型，刻画乘法与加法两种注意规则，分别解释选择延迟与吸引效应，并通过公理实现参数与偏好的唯一识别。
AI 中文摘要
我们研究了一个基于属性的随机选择模型，在该模型中，对每个属性的注意力是有限的，并在此框架内刻画了两种选择规则。乘法注意规则（MAR）仅对某个备选方案排名第一的属性给予奖励，并在各属性间以乘法方式组合这些注意力；它捕捉了因关注“过多”属性而产生的认知负荷所导致的选择延迟（弃权（不执行））。加法注意规则（AAR）则对跨属性的注意力进行平均，并根据备选方案在菜单中的完整序数排名给予其信用。这种聚合方式的差异正是两种规则在行为上区分开来的原因：AAR在跨菜单保持相关属性不变的情况下重现了吸引效应，而MAR只有在属性集本身发生变化时才能做到这一点，尽管AAR反过来无法容纳折中效应。两种规则都由可观察选择数据上的公理所刻画，并且在两种情况下，潜在的注意力参数和基于属性的偏好都能从该数据中被唯一识别。
英文摘要
We study an attribute-based model of stochastic choice in which attention to each attribute is limited, and characterize two choice rules within it. The Multiplicative Attention Rule (MAR) rewards an alternative only for the attributes on which it ranks first, combining attention to these multiplicatively across attributes; it captures choice deferral driven by the cognitive load of attending to `too many' attributes. The Additive Attention Rule (AAR) instead averages attention across attributes and credits an alternative by its full ordinal rank within the menu. This difference in aggregation is what separates the two rules behaviourally: AAR reproduces the Attraction Effect while holding the relevant attributes fixed across menus, something MAR can do only when the attribute set itself changes, though AAR, in turn, cannot accommodate the Compromise Effect. Both rules are characterized by axioms on observable choice data, and in both cases the underlying attention parameters and attribute-based preferences are uniquely identified from that data.
