Structured, uncertainty-driven exploration in real-world consumer choice

Abstract

We study how people make choices among a large number of options when they have limited experience. In a large dataset of online food delivery purchases, we find evidence for sophisticated exploration strategies predicted by contemporary theories. People actively seek to reduce their uncertainty about restaurants and use similarity-based generalization to guide their selections. Our findings suggest that theories of exploratory choice have real-world validity.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jun 24, 2019
Source ID
10.1073/pnas.1821028116

Entities

People

  • Bastien Brier
  • Bradley C. Love
  • Eric Schulz
  • Michael T. Todd
  • Rahul Bhui
  • Samuel J Gershman

Organizations

  • Alan Turing Institute
  • Deliveroo
  • Harvard University
  • Office of Naval Research
  • University College London

Tags

Readers

  • Industrial Economics
  • Neural Network Machine Learning.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.