Economic Theory

With uncertainty about persistence, we show that forecasts necessarily become more persistent and over-react at long horizons. For these reasons, correctly specified and Bayesian forecasts may under-react at short horizons and over-react at long horizons. These results provide a unified explanation for several asset pricing and forecasting puzzles, including: (i) the excess responsiveness of long-horizon rates to short rates, (ii) the dominance of apparent term premia for long-term rates, (iii) the ex post predictability of bond yields, (iv) the excess volatility of long-horizon forward prices, (v) the excess persistence of long-horizon forecasts, and (vi) the over-reaction of long-horizon forecasts.

A monopolist offers personalized prices to consumers with unit demand. Consumers differ in their values, costs, and \emph{protected characteristics}---such as race or gender. The seller is subject to a non-discrimination constraint: consumers with the same cost, but different protected characteristics must face identical price distributions. Such regulations are present in markets like credit or insurance. We characterize the optimal pricing rule. Under this rule, surplus accrues to both protected groups, but only to those with intermediate values. Strengthening the constraint to cover transaction prices redistributes surplus, harming the low-value group and benefiting the high-value group. Meanwhile, prohibiting the use of protected characteristics as pricing inputs instead of regulating outputs harms the low-value group.

We build a general equilibrium model in which firms endogenously choose whether to target prices or quantities. We characterize how these choices of organizational targets depend on firms' uncertainty about microeconomic and macroeconomic factors. In equilibrium, the transmission of both nominal and real shocks hinges on firms' organizational targets. For example, under otherwise identical microfoundations, money is neutral under quantity targets and non-neutral under price targets. We further characterize how targets shape firms' strategic interactions and prove that the macroeconomic uncertainty that arises from each choice of targets reinforces incentives to choose that target. That is, choices of organizational targets are strategic complements. For this reason, monetary policy aimed at stabilization can backfire by inducing a regime shift that renders it ineffective. A simple quantification suggests that incentives over organizational targets can vary markedly at business-cycle frequencies and help explain the state-dependent pass-through of monetary shocks to prices and output.

We study optimization problems in which a linear functional is maximized over probability measures that are dominated by a given measure according to an integral stochastic order in an arbitrary dimension. We show that the following four properties are equivalent for any such order: (i) the test function cone is closed under pointwise minimum, (ii) the value function is affine, (iii) the solution correspondence has a convex graph with decomposable extreme points, and (iv) every ordered pair of measures admits an order-preserving coupling. As corollaries, we derive the extreme and exposed point properties involving integral stochastic orders such as multidimensional mean-preserving spreads and stochastic dominance. Applying these results, we generalize Blackwell's theorem by completely characterizing the comparisons of experiments that admit two equivalent descriptions—through instrumental values and through information technologies. We also show that these results immediately yield new insights into information design, mechanism design, and decision theory.

As AI systems enter institutional workflows, workers must decide whether to delegate task execution to AI and how much effort to invest in verifying AI outputs, while institutions evaluate workers using outcome-based standards that may misalign with workers’ private costs. We model delegation and verification as the solution to a rational worker’s optimization problem, and define worker quality by evaluating an institution-centered utility (distinct from the worker’s objective) at the resulting optimal action. We formally characterize optimal worker workflows and show that AI induces phase transitions, where arbitrarily small differences in verification ability lead to sharply different behaviors. As a result, AI can amplify workers with strong verification reliability while degrading institutional worker quality for others who rationally over-delegate and reduce oversight, even when baseline task success improves and no behavioral biases are present. These results identify a structural mechanism by which AI reshapes institutional worker quality and amplifies quality disparities between workers with different verification reliability.

As AI systems shift from tools to collaborators, a central question is how the skills of humans relying on them change over time. We study this question mathematically by modeling the joint evolution of human skill and AI delegation as a coupled dynamical system. In our model, delegation adapts to relative performance, while skill improves through use and decays under non-use; crucially, both updates arise from optimizing a single performance metric measuring expected task error. Despite this local alignment, adaptive AI use fundamentally alters the global stability structure of human skill acquisition. Beyond the high-skill equilibrium of human-only learning, the system admits a stable low-skill equilibrium corresponding to persistent reliance, separated by a sharp basin boundary that makes early decisions effectively irreversible under the induced dynamics. We further show that AI assistance can strictly improve short-run performance while inducing persistent long-run performance loss relative to the no-AI baseline, driven by a negative feedback between delegation and practice. We characterize how AI quality deforms the basin boundary and show that these effects are robust to noise and asymmetric trust updates. Our results identify stability, not incentives or misalignment, as the central mechanism by which AI assistance can undermine long-run human performance and skill.