We develop and estimate a model of consumer search with spatial learning. Consumers make inferences from previously searched objects to unsearched objects that are nearby in attribute space, generating path dependence in search sequences. The estimated model rationalizes patterns in data on online consumer search paths: search tends to converge to the chosen product in attribute space, and consumers take larger steps away from rarely purchased products. Eliminating spatial learning reduces consumer welfare by 12%: cross‐product inferences allow consumers to locate better products in a shorter time. Spatial learning has important implications for product recommendations on retail platforms. We show that consumer welfare can be reduced by unrepresentative product recommendations and that consumer‐optimal product recommendations depend on both consumer learning and competition between platforms.

We document the evolution of the new market for frequency response within the UK electricity system over a six-year period. Firms competed in price while facing considerable initial uncertainty about demand and rival behavior. We show that prices stabilized over time, converging to a rest point that is consistent with equilibrium play. We draw on models of fictitious play and adaptive learning to analyze how this convergence occurs and show that these models predict behavior better than an equilibrium model prior to convergence.