Explore Our Research

We study mechanism design for a sophisticated agent with non-expected utility (EU)
preferences. We show that the revelation principle holds if and only if all types are EU
maximizers: if at least one type is a non-EU maximizer, randomizing over dynamic
mechanisms generates a strictly larger set of implementable allocations than using static
mechanisms. Moreover, dynamic stochastic mechanisms can fully extract the private
information of any type who doesn’t have uniformly quasi-concave preferences without
providing that type any rent. Full-surplus extraction is possible in a broad variety of
non-EU environments, but impossible for types with concave preferences.

We study mechanism design in environments where agents have private preferences and private information about a common payoff-relevant state. In such settings with multi-dimensional types, standard mechanisms fail to implement efficient allocations. We address this limitation by proposing data-driven mechanisms that condition transfers on additional post-allocation information, modeled as an estimator of the payoff-relevant state. Our mechanisms extend the classic Vickrey–Clarke–Groves framework. We show they achieve exact implementation in posterior equilibrium when the state is fully revealed or utilities are affine in an unbiased estimator. With a consistent estimator, they achieve approximate implementation that converges to exact implementation as the estimator converges, and we provide bounds on the convergence rate. We demonstrate applications to digital advertising auctions and AI shopping assistants, where user engagement naturally reveals relevant information, and to procurement auctions with consumer spot markets, where additional information arises from a pricing game played by the same agents.

How should a buyer design procurement mechanisms when suppliers’ costs are unknown, and the buyer does not have a prior belief? We demonstrate that notably simple mechanisms—those that share a constant fraction of the buyer utility with the seller—allow the buyer to realize a guaranteed positive fraction of the efficient social surplus across all possible costs. Moreover, a judicious choice of the share based on the known demand maximizes the surplus ratio guarantee that can be attained across all possible (arbitrarily complex and nonlinear) mechanisms and cost functions. Results apply to related nonlinear pricing and optimal regulation problems.

We develop a methodology for modeling household income processes when subjective probabilistic assessments of future income are available. This allows us to flexibly estimate conditional cdf s directly using elicited individual subjective probabilities, and to obtain empirical measurements of subjective risk and subjective persistence. We then use two longitudinal surveys collected in rural India and rural Colombia to explore the nature of perceived income dynamics in those contexts. Our results suggest linear income processes are rejected in favor of more flexible versions in both cases; subjective income distributions feature heteroskedasticity, conditional skewness and nonlinear persistence.

We propose a new formulation of the maximum score estimator that uses compositions of rectified linear unit (ReLU) functions, instead of indicator functions as in Manski (1975, 1985), to encode the sign alignment restrictions. Since the ReLU function is Lipschitz, our new ReLU-based maximum score criterion function is substantially easier to optimize using standard gradient-based optimization pacakges. We also show that our ReLU-based maximum score (RMS) estimator can be generalized to an umbrella framework defined by multi-index single-crossing (MISC) conditions, while the original maximum score estimator cannot be applied. We establish the n −s/(2s+1) convergence rate and asymptotic normality for the RMS estimator under order-s Holder smoothness. In addition, we propose an alternative estimator using a further reformulation of RMS as a special layer in a deep neural network (DNN) architecture, which allows the estimation procedure to be implemented via state-of-the-art software and hardware for DNN.

This paper outlines an economic model that provides a framework for organising the growing literature on the performance of physicians and judges. The primary task of these professionals is to make decisions based on the information provided by their clients. The paper discusses professional decisions in terms of what Kahneman (2011) calls fast and slow decisions, known as System 1 and System 2 in cognitive science. Slow decisions correspond to the economist’s model of rational choice, while System 1 (fast) decisions are high‑speed, intuitive choices guided by training and human capital. This distinction is used to provide a model of decision‑making under uncertainty based on Bewley (2011)’s theory of Knightian uncertainty to show that human values are an essential input to optimal choice. This, in turn, provides conditions under which artificial intelligence (AI) tools can assist professional decision‑making, while pointing to cases where such tools need to explicitly incorporate human values in order to make better decisions.

The 1996 US welfare reform introduced time limits on welfare receipt. We use quasi-experimental evidence and a rich life-cycle model to understand the impact of time limits on different margins of behavior and well-being. We stress the impact of marital status and marital transitions on mitigating the cost and impact of time limits. Time limits cause women to defer claiming in anticipation of future needs and to work more, effects that depend on the probabilities of marriage and divorce. They also cause an increase in employment among single mothers and reduce divorce, but their introduction costs women 0.7% of lifetime consumption, gross of the redistribution of government savings.

We study how privacy regulation affects menu pricing by a monopolist platform that collects and monetizes personal data. Consumers differ in privacy valuation and sophistication: naive users ignore privacy losses, while sophisticated users internalize them. The platform designs prices and data collection options to screen users. Without regulation, privacy allocations are distorted and naive users are exploited. Regulation through privacy-protecting defaults can create a market for information by inducing payments for data; hard caps on data collection protect naive users but may restrict efficient data trade.