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Although the challenge of enriching the psychology of decision makers in economic models has been at the frontier of theoretical research in the last decade, there has been no graduate-level, theory-oriented textbook to cover developments in the last 10-15 years. Thus, Bounded Rationality and Industrial Organization offers a welcome and crucial new understanding of market behavior-it challenges conventional wisdom in ways that are interesting and economically significant, and which in the end effect the well-being of all market participants.

I study a dynamic model of strategic reform decisions that potentially affect the stochastic evolution of a publicly observed economic variable. Policy makers maximize their evaluation by a boundedly rational public. Specifically, the public follows a rule that attributes recent changes to the most recent intervention. I analyze subgame perfect equilibrium in this model when the economic variable follows a linear growth trend with noise. Equilibrium is essentially unique and stationary, bearing a subtle formal relation to optimal search models. Policy makers tend to act during crises, display risk aversion conditional on acting, and prefer interventions that induce permanent noise. (JEL D78, D83)

We formalize the argument that political disagreements can be traced to a "clash of narratives." Drawing on the "Bayesian Networks" literature, we represent a narrative by a causal model that maps actions into consequences, weaving a selection of other random variables into the story. Narratives generate beliefs by interpreting long-run correlations between these variables. An equilibrium is defined as a probability distribution over narrative-policy pairs that maximize a representative agent's anticipatory utility, capturing the idea that people are drawn to hopeful narratives. Our equilibrium analysis sheds light on the structure of prevailing narratives, the variables they involve, the policies they sustain, and their contribution to political polarization. (JEL D72, D83, D85, F52)

AbstractNonretail platforms enable users to engage in noncommercial activities, while generating user information that helps ad targeting. We present a model in which the platform chooses a personalized ad‐display rule and an advertising fee (which depends on the targeted user group). The policy that maximizes the platform's advertising revenues creates an incentive for advertisers to strategize targeting. We provide a condition for incentive‐compatibility of the first‐best policy, and highlight the forces that make it harder to satisfy. We apply our result to examples of platforms. Our analysis of social networks turns out to be related to the "community‐detection" problem.

We study decentralized mechanisms for allocating firms into search pools. The pools are created in response to noisy preference signals provided by consumers, who then browse the pools via costly random sequential search. Surplus-maximizing search pools are implementable in symmetric Nash equilibrium. Full extraction of the maximal surplus is implementable if and only if the distribution of consumer types satisfies a set of simple inequalities, which involve the relative fractions of consumers who like different products and the Bhattacharyya coefficient of similarity between their conditional signal distributions. The optimal mechanism can be simulated by a keyword auction with broad matching. (JEL C78, D44, D82)