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Keita Kuwahara

Publications and source records attributed to Keita Kuwahara.

5 recordsLinked to original sources
arXiv2026

The Capacity Cost of Informational Screening

An advisor knows who would benefit from using a resource but cannot assign its use. Individuals have private assessments and may ignore advice. We study how their choices can respect a fixed usage limit. In our model, whenever coordination is possible, eliciting private assessments makes some harmful use unavoidable. Policies that maximize welfare, or even minimize harm, also forgo beneficial uses that direct assignment would permit. In those environments, observing assessments instead allows advice to achieve as much beneficial use as direct assignment, without harmful use. The loss therefore reflects the incentive cost of eliciting private beliefs while leaving use voluntary.

econ.TH↗
arXiv2026

When Public Information Helps Consumers

Does more information benefit consumers when a monopolist can adjust its price? Although information helps consumers evaluate a product, Schlee (1996) showed that public disclosure can reduce consumer surplus. We characterize exactly when the opposite occurs. More informative disclosure increases consumer surplus if and only if a weighted combination of the slope and curvature of Myerson's virtual type is nonnegative. Producer surplus always increases, while a weaker condition characterizes when total surplus increases. In particular, information benefits consumers whenever the virtual type is convex. Even when convexity fails, the same conclusion holds if monopoly output is sufficiently small.

econ.TH↗
arXiv2026

A Note on 'The Limits of Price Discrimination' by Bergemann, Brooks, and Morris

This note revisits the analysis of third-degree price discrimination developed by Bergemann et al. (2015), which characterizes the set of consumer-producer surplus pairs that can be achieved through market segmentation. This was proved by means of market segmentation with random prices, but it was claimed that any segmentation with possibly random pricing has a corresponding direct segmentation, where a deterministic price is charged in each market segment. However, the latter claim is not correct under the definition of market segmentation given in the paper, and we provide counterexamples. We then propose an alternative definition to resolve this issue and examine the implications of the difference between the two definitions in terms of the main result of their paper.

econ.TH↗
arXiv2026

Selling Multiple Items to a Unit-Demand Buyer via Automated Mechanism Design

Finding the optimal (revenue-maximizing) mechanism to sell multiple items has been a prominent and notoriously difficult open problem. Existing work has mainly focused on deriving analytical results tailored to a particular class of problems (for example, Giannakopoulos, 2015; Yang, 2025). The present paper explores the possibility of a generally applicable methodology of the Automated Mechanism Design (AMD). We first employ the deep learning algorithm developed by Dütting et al. (2023) to numerically solve small-sized problems, and the results are then generalized by educated guesswork and finally rigorously verified through duality. By focusing on a single buyer who can consume one item, our approach leads to two key contributions: establishing a much simpler way to verify the optimality of a wide range of problems and discovering a completely new result about the optimality of uniform pricing. First, we show that selling each item at an identical price (or equivalently, selling the grand bundle of all items) is optimal for any number of items when the value distributions belong to a class that includes the uniform distribution as a special case. Different items are allowed to have different distributions. Second, for each number of items, we established necessary and sufficient conditions that $c$ must satisfy for uniform pricing to be optimal when the value distribution is uniform over an interval $[c, c+1]$. This latter model does not satisfy the previously known sufficient conditions for the optimality of grand bundling (Haghpanah & Hartline, 2021). Our results are in contrast to the only known results for $n$ items (for any $n$), Giannakopoulos (2015) and Daskalakis et al. (2017), which consider a single buyer with additive preferences, where the values of items are narrowly restricted to i.i.d. according to a uniform or exponential distribution.

econ.TH↗