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Preference Aggregation With Incomplete Information

Econometrica 2014 82(2), 589-599
We show in an environment of incomplete information that monotonicity and the Pareto property applied only when there is common knowledge of Pareto dominance imply (i) there must exist a common prior over the smallest common knowledge event, and (ii) aggregation must be ex ante and ex post utilitarian with respect to that common prior and individual von Neumann–Morgenstern utility indices.

Weighted Linear Discrete Choice

American Economic Review 2025 115(4), 1226-1257
We introduce a new model of stochastic choice that assigns each choice option a utility, along with a salience parameter reflecting economic frictions. We characterize our model behaviorally and investigate its comparative statics properties. We show that the model generates intuitive closed-form solutions in equilibrium settings where firms can choose price, quality, and advertising. In addition, we show that the model allows for flexible substitution patterns and changes in market shares across choice sets. We demonstrate that our model can be easily identified and can outperform alternatives in demand prediction. (JEL D11, D21, D43, M37)

Incentives in Experiments: A Theoretical Analysis

Journal of Political Economy 2018 126(4), 1472-1503
Experimental economists currently lack a convention for how to pay subjects in experiments with multiple tasks. We provide a theoretical framework for analyzing this question. Assuming statewise monotonicity and nothing else, we prove that paying for one randomly chosen problem—the random problem selection mechanism—is essentially the only incentive compatible mechanism. Paying for every period is similarly justified when we assume only a “no complementarities at the top” condition. To help experimenters decide which is appropriate for their particular experiment, we discuss empirical tests of these two assumptions.