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

TL;DR

This paper presents a framework showing how the design of consumption data influences preference learning, highlighting implications for platform bias and regulation strategies.

Contribution

It introduces a data-design framework for preference discovery, emphasizing how exposure structure affects learning and bias in consumer preferences.

Findings

Bundling creates correlated exposure, propagating utility surprises.

Bias-targeted design can hinder learning and reinforce misperceptions.

Correlation-breaking bundles can accelerate preference discovery.

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.