Associative Networks in Decision Making

2026-08-10

AI Summary

This study investigates how decision makers incorporate unavailable alternatives into their consideration sets through psychological associations and how these alternatives influence choices. To this end, the authors introduce a novel framework that embeds an associative network into a random attention model, formally capturing the mental linkage between available and merely observable options. This approach provides a unified explanation for both classical menu effects and their “phantom” variants, enables unique identification of model parameters, and successfully accounts for a range of choice anomalies. The findings highlight the pivotal role of associative links in brand strategy, product imitation, and platform design, offering fresh theoretical insights and practical implications for behavioral decision research.

Abstract

We present a model of associative networks that captures how decision makers expand their consideration set through mental associations between alternatives. Our model provides a tractable approach to study how associations shape choice when some alternatives are available and others are merely observable but unavailable. We characterize the model within a random attention framework and demonstrate unique identification of all parameters. This framework delivers a unified account of several prominent choice anomalies, including classic menu effects and their ``phantom'' counterparts. We illustrate how associative links serve as a strategic variable in applications such as branding, imitation, and platform design.

Problem

• associative networks
• decision making
• consideration set
• choice anomalies
• mental associations

Innovation

• associative networks
• consideration set
• random attention
• choice anomalies
• phantom alternatives