Title: A List-Based Random Attention Model
Speaker: Yujian Chen
Time: Jan 17, 2025 (Fri) 12:00 - 1:30 pm
Hybrid:
Hong Yuan Building 311
Zoom Meeting: https://us02web.zoom.us/j/8089091002?pwd=cHN4SzE4a3FLWnZCQnBZczczdVlUdz09&omn=89636233155
Abstract:
I propose a list-based random attention model to study how stochastic limited attention affects decision-making when an agent selects one item from an ordered list of alternatives. The model attributes stochastic choice to variations in the number of items considered by the agent, which may differ across lists. I show that a finite dataset of lists’ choice distributions can be rationalized by the model if and only if these distributions satisfy two testable conditions. When the choice distributions are rationalizable, both conditions convey information on the agent’s unobserved attention behavior and preference. As an application, I explore how a list designer can construct a list of alternatives that maximizes worst-case profit, given partial information (extracted from the data set) on the agent’s attention behavior and preference.