SMU SOE Seminar Series (August 15, 2024): Associative Network in Decision Making

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ABSTRACT

We model associative networks that capture how a decision maker enlarges her consideration set through the mental association between alternatives, and demonstrate how this model serves as a toolbox to understand the impact of mental association on decision making. As a proof of concept, we characterize this model within a random attention framework and demonstrate that all the relevant parameters are uniquely identifiable. Notably, in a novel choice domain where not all observable alternatives are available, the presence of unavailable yet observable alternatives can influence the choice frequencies of alternatives through mental association.

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