Alexander K. Zentefis Publications

We consider the problem of explaining a data generating process (DGP) to a decision maker (DM) who cannot understand it. Explanations are information, not approximations: they are useful because they rule out possible DGPs. If the DM maximizes her average payoff, explanations using OLS are robustly useful, and augmenting them with summary statistics makes them more so. Sampling error can destroy these guarantees, but theoretical assumptions linking that error to the DGP can restore them. If the DM is sufficiently ambiguity averse, explanations that are affine in outcomes are not robustly useful, but certificates reporting lower bounds on outcomes are.

A monotone function interval is the set of monotone functions that lie pointwise between two fixed monotone functions. We characterize the set of extreme points of monotone function intervals and apply this to a number of economic settings. First, we leverage the main result to characterize the set of distributions of posterior quantiles that can be induced by a signal, with applications to political economy, Bayesian persuasion, and the psychology of judgment. Second, we combine our characterization with properties of convex optimization problems to unify and generalize seminal results in the literature on security design under adverse selection and moral hazard.