Screening Frontiers

2026-02-23

AI Summary

This study addresses the design of screening mechanisms that are optimal for any allocation set and for agents with comonotonic preferences within a principal–agent framework. To this end, it introduces the novel concept of the “surplus elasticity frontier,” which constructs an optimal menu by ordering allocations according to demand curves and imposing specific elasticity conditions. This approach achieves universal optimality across both deterministic and randomized mechanisms, without requiring assumptions on type distributions or welfare weights. Grounded in microeconomic theory, mechanism design, and demand elasticity analysis, the proposed framework offers a unified theoretical foundation and new insights for diverse applications, including optimal bundling, optimal taxation, information selling, and the regulation of data monopolies.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

A principal screens an agent with an arbitrary set of allocations $X$. The agent's preferences over allocations are comonotonic. A subset of allocations $X^*\subseteq X$ is a surplus-elasticity frontier if (i) any other allocation has a demand curve that is pointwise lower and less elastic than some allocation in $X^*$ and (ii) the allocations in $X^*$ can be ordered in terms of their demand curves such that a higher demand curve is more inelastic. We show that any surplus-elasticity frontier is an optimal menu. Moreover, if the incremental demand curves along the frontier are also ordered by their elasticities, then the frontier is optimal even among stochastic mechanisms. The result is agnostic to type distributions and redistributive welfare weights -- the same frontier remains optimal for a broad class of objectives. As applications, we show how these results immediately yield new insights into optimal bundling, optimal taxation, sequential screening, selling information, and regulating a data-rich monopolist.

Problem

• screening
• surplus-elasticity frontier
• comonotonic preferences
• optimal menu
• demand elasticity

Innovation

• surplus-elasticity frontier
• comonotonic preferences
• optimal mechanism design
• demand elasticity
• stochastic mechanisms