How do you know you won't like it if you've (never) tried it? Preference discovery and data design

2026-04-15

AI Summary

This study addresses how consumer preferences, though formed through experience, can be distorted by the sequence and composition of experiences deliberately designed by digital platforms. The authors propose a “data design” framework that treats the structure of consumption data as an active design variable. Integrating co-consumption network modeling with robust exposure strategies, the paper examines how bundled recommendations influence preference learning through the propagation of perceived utility. The analysis reveals that popularity-driven bundling delays accurate preference discovery, whereas exposure designs that disrupt item correlations substantially enhance learning efficiency. This framework elucidates how platforms can sustain biased demand through strategic data structuring and offers regulators a novel intervention pathway grounded in breaking spurious item correlations.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Knowledge Representation and Reasoning: Preferences
• Humans and AI: Learning Human Values and Preferences

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Consumers discover their preferences through experience, yet the sequence and composition of those experiences are often designed by firms, digital platforms, or policymakers. We introduce a ``data-design'' framework for preference discovery, in which the structure of consumption data shapes learning. Bundling generates correlated exposure across goods, so utility surprises propagate through the co-consumption network. When estimation errors are known, bias-targeted design can shut down learning and amplify misperceptions. Conversely, robust design uses only the geometry of past co-consumption: popularity-biased bundles slow learning, while correlation-breaking bundles accelerate preference discovery. The framework thus explains how dominant platforms can sustain biased demand through exposure design, and why effective regulation may need to intervene on the structure of exposure itself rather than only on prices or market shares.

Problem

• preference discovery
• data design
• exposure design
• consumption experience
• learning bias

Innovation

• data design
• preference discovery
• bundling
• co-consumption network
• robust design