Rational Inattention and Sequential Information Sampling

We propose a new principle for measuring the cost of information structures in rational inattention problems, based on the cost of generating the information used to make a decision through a dynamic evidence accumulation process. We introduce a continuous-time model of sequential information sampling, and show that, in a broad class of cases, the choice frequencies resulting from optimal information accumulation are the same as those implied by a static rational inattention problem with a particular static information-cost function. Among the static cost functions that can be justified in this way is the mutual information cost function proposed by Sims (2010), but we show that other cost functions can be micro-founded in this way as well. In particular, we introduce a class of “neighborhood-based” cost functions, which make it more costly to undertake experiments that can produce different results in similar states, and show that the predictions of this alternative rational inattention theory better conform with evidence from perceptual discrimination experiments.