Bayesian Rational Search Engine User

Abstract

A user faces a list returned by a search system, ordered by a noisy proxy for relevance, and decides whether to pay a fixed cost to inspect another item or stop with the best she has uncovered. She does not enter the page knowing how good its items are, so each inspection both produces a candidate item and refines her belief about the page's underlying quality. We show that the optimal policy is a standout rule: the user stops once her best find is sufficiently good relative to what she expects from the page, with the required margin changing with depth. The resulting dynamics collapse to a one-dimensional Markov chain, which yields the full distribution of inspection depth through a closed-form recursion. The model uncovers three hidden mechanisms that explain why users stop: trust, commit, and cut-losses. It also yields a rich set of testable implications. The same Bayesian-rational view turns inspection depth into a learning-to-rank likelihood: an observed depth restricts the latent relevance path to a polyhedron of survival inequalities, while a conversion further identifies the terminal winner. Simulations show that the resulting estimator recovers relevance under correct specification and improves ranking when restricted to the features available to the incumbent ranker. Applied to public Baidu search logs, the inspection depth likelihood produces rankings that align more closely with expert relevance judgments than click-trained alternatives.