The past few decades have ushered in an experimental revolution in economics whereby scholars are now much more likely to generate their own data. While there are virtues associated with this movement, there are concomitant difficulties. Several scientific disciplines, including economics, have launched research registries in an effort to attenuate key inferential issues. This study assesses registries both empirically and theoretically, with a special focus on the AEA registry. We find that over 90% of randomized control trials (RCTs) in economics do not register, only 50% of the RCTs that register do so before the intervention begins, and the majority of these preregistrations are not detailed enough to significantly aid inference. Our empirical analysis further shows that using other scientific registries as aspirational examples is misguided, as their perceived success in tackling the main issues is largely a myth. In light of these facts, we advance a simple economic model to explore potential improvements. A key insight from the model is that removal of the (current) option to register completed RCTs could increase the fraction of trials that register. We also argue that linking IRB applications to registrations could further increase registry effectiveness.

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We introduce a robust approach to study dynamic monopoly pricing of a durable good in the face of buyer learning. A buyer receives information about her willingness-to-pay for the seller’s product over time, and decides when to make a one-time purchase. The seller does not know how the buyer learns, but commits to a pricing strategy to maximize profits against the worst-case information arrival process. We show that a constant price path delivers the robustly optimal profit, with profit and price both lower than under known values. Thus, under the robust objective, intertemporal incentives do not arise at the optimum, despite the possibility for information arrival to influence the timing of purchases. We delineate whether constant prices remain optimal (or not) when the seller seeks robustness against a subset of information arrival processes. As part of the analysis, we develop new techniques to study dynamic Bayesian persuasion.

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We introduce an evolutionary framework to evaluate competing (mis)specifications in strategic situations, focusing on which misspecifications can persist over a correct specification. Agents with heterogeneous specifications coexist in a society and repeatedly match against random opponents to play a stage game. They draw Bayesian inferences about the environment based on personal experience, so their learning depends on the distribution of specifications and matching assortativity in the society. One specification is evolutionarily stable against another if, whenever sufficiently prevalent, its adherents obtain higher expected objective payoffs than their counterparts. The learning channel leads to novel stability phenomena compared to frameworks where the heritable unit of cultural transmission is a single belief instead of a specification (i.e., set of feasible beliefs). We apply the framework to linear-quadratic-normal games where players receive correlated signals but possibly misperceive the information structure. The correct specification is not evolutionarily stable against a correlational error, whose direction depends on matching assortativity. As another application, the framework also endogenizes coarse analogy classes in centipede games.

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I develop a theoretical model of costly information acquisition in order to evaluate transparency requirements in empirical research. A sender chooses an experiment characterized by multiple dimensions, while a receiver observes the experiment’s outcome (though not necessarily all dimensions). I show that the receiver may prefer to keep dimensions hidden, even those contributing to bias, despite preferring more informative experiments. This can occur if the perception of bias is lessened when the sender compensates along a dimension that is observed. I elucidate how complementarity between dimensions underlies this result. (JEL D82, D83)

After observing the outcome of a Blackwell experiment, a Bayesian decisionmaker can form (a) posterior beliefs over the state, as well as (b) posterior beliefs she would observe any given signal (assuming an independent draw from the same experiment). I call the latter her contingent hypothetical beliefs. I show geometrically how contingent hypothetical beliefs relate to information structures. Specifically, the information structure can (generically) be derived by regressing contingent hypothetical beliefs on posterior beliefs over the state. Her prior is the unit eigenvector of a matrix determined from her posterior beliefs over the state and her contingent hypothetical beliefs. Thus, all aspects of a decisionmaker's information acquisition problem can be determined using ex-post data (i.e., beliefs after having received signals). I compare my results to similar ones obtained in cases where information is modeled deterministically; the focus on single-agent stochastic information distinguishes my work.