Abstract We develop a theory of how an agent makes basic multiproduct consumption decisions in the presence of taste, consumption opportunity, and price shocks that are costly to attend to. We establish that the agent often simplifies her choices by restricting attention to a few important considerations, which depend on the decision at hand and affect her consumption patterns in specific ways. If the agent’s problem is to choose the consumption levels of many goods with different degrees of substitutability, then she may create mental budgets for more substitutable products (e.g., entertainment). In some situations, it is optimal to specify budgets in terms of consumption quantities, but when most products have an abundance of substitutes, specifying budgets in terms of nominal spending tends to be optimal. If the goods are complements, in contrast, then the agent may—consistent with naive diversification—choose a fixed, unconsidered mix of products. And if the agent’s problem is to choose one of multiple products to fulfill a given consumption need (e.g., for gasoline or a bed), then it is often optimal for her to allocate a fixed sum for the need.

Individuals must often choose among discrete actions with imperfect information about their payoffs. Before choosing, they have an opportunity to study the payoffs, but doing so is costly. This creates new choices such as the number of and types of questions to ask. We model these situations using the rational inattention approach to information frictions. We find that the decision maker's optimal strategy results in choosing probabilistically in line with a generalized multinomial logit model, which depends both on the actions' true payoffs as well as on prior beliefs. (JEL D11, D81, D83)

We review the recent literature on rational inattention, identify the main theoretical mechanisms, and explain how it helps us understand a variety of phenomena across fields of economics. The theory of rational inattention assumes that agents cannot process all available information, but they can choose which exact pieces of information to attend to. Several important results in economics have been built around imperfect information. Nowadays, many more forms of information than ever before are available due to new technologies, and yet we are able to digest little of it. Which form of imperfect information we possess and act upon is thus largely determined by which information we choose to pay attention to. These choices are driven by current economic conditions and imply behavior that features numerous empirically supported departures from standard models. Combining these insights about human limitations with the optimizing approach of neoclassical economics yields a new, generally applicable model. (JEL D83, D91, E71)

We solve a general class of dynamic rational-inattention problems in which an agent repeatedly acquires costly information about an evolving state and selects actions. The solution resembles the choice rule in a dynamic logit model, but it is biased towards an optimal default rule that does not depend on the realized state. We apply the general solution to the study of (i) the sunk-cost fallacy; (ii) inertia in actions leading to lagged adjustments to shocks; and (iii) the tradeoff between accuracy and delay in decision-making.

We recover valuations of time using detailed data from a large ride‐hail platform, where drivers bid on trips and consumers choose between a set of rides with different prices and wait times. Leveraging a consumer panel, we estimate demand as a function of both prices and wait times and use the resulting estimates to recover heterogeneity in the value of time across consumers. We study the welfare implications of personalized pricing and its effect on the platform, drivers, and consumers. Taking into account drivers' optimal reaction to the platform's pricing policy, personalized pricing lowers consumer surplus by 2.5% and increases overall surplus by 5.2%. Like the platform, drivers benefit from personalized pricing. By conditioning prices on drivers' wait times and not on consumers' data, the platform can capture a significant portion of the profits garnered from personalized pricing, and simultaneously benefit consumers.