This website uses cookies.

By clicking the "Accept" button or continuing to browse our site, you agree to first-party and session-only cookies being stored on your device to enhance site navigation and analyze site performance and traffic. For more information on our use of cookies, please see our Privacy Policy.

  1. Home
  2. ASSA Annual Meeting
  3. 2025
  4. Program
  5. Advances in Mechanism and Information Design
« Back to Results

Advances in Mechanism and Information Design

Paper Session

Friday, Jan. 3, 2025 2:30 PM - 4:30 PM (PST)

San Francisco Marriott Marquis, Willow
Hosted By: Chinese Economic Association in North America
  • Chair: Kai Hao Yang, Yale University

Selling Diversity

Vasiliki Skreta
,
University of Texas-Austin, University College London and Center for Economic and Policy Research
Suraj Malladi
,
Northwestern University

Abstract

We provide a general yet tractable model to derive revenue-optimal mechanisms in settings with allocation-dependent externalities. Such externalities are present in set- tings ranging from concerts and parties to fashion items and software to college and workplace experiences. Our framework allows for transfers and for screening through ordeals like waiting in line, joining a fan club, or completing lengthy applications. We elucidate when and how such seemingly wasteful actions enhance price discrimination in the presence of externalities. Ordeals effectively filter out undesirable customers or members in some settings. In other settings, ordeals can be leveraged to allocate to desirable consumers who may have low purchasing power. We find conditions under which optimal mechanisms create two classes of customers: those who pay with money and those who pay with effort (effort discrimination). We find separate conditions under which those who pay also engage in the ordeal (certification).

Nested Bundling

Frank Yang
,
Stanford University

Abstract

A nested bundling strategy creates menus in which more expensive bundles include
all the goods of the less expensive ones. We study when nested bundling is optimal
and determine which nested menu is optimal, when consumers differ in one dimension. We introduce a partial order on the set of bundles, defined by (i) set inclusion
and (ii) sales quantity when sold alone. We show that, under quasiconcavity assumptions, if the undominated bundles with respect to this partial order are nested, then
nested bundling is optimal. We provide an iterative procedure to determine the minimal optimal menu that consists of a subset of the undominated bundles. The proof
technique involves a new constructive monotone comparative statics theorem. We
present partial converses. Additionally, we provide distributionally robust characterizations of nested bundling. We also show that under suitable conditions it is possible
to extend our analysis to allow multidimensional heterogeneity.

Improvable Equilibria

Fedor Sandomirskiy
,
Princeton University
Kirill Rudov
,
University of California-Berkeley
Leeat Yariv
,
Princeton University

Abstract

In many settings, agents can communicate---either directly or through intermediaries---before they engage in strategic interactions. We explore when such communication can be beneficial in general strategic contexts. We show that this question reduces, for any non-degenerate objective, to determining when Nash equilibria are extreme points within the set of correlated equilibria. Our results demonstrate that any sufficiently random mixed Nash equilibrium, involving at least three agents randomizing, can always be improved by either correlating agents' actions or switching to a less random equilibrium, regardless of the underlying objective. As a result, symmetric equilibria in a variety of symmetric environments---such as auctions, voting, and matching---are inherently suboptimal, no matter the goal.

Non-Discriminatory Personalized Pricing

Kai Hao Yang
,
Yale University
Philipp Strack
,
Yale University

Abstract

A unit mass of consumers with unit demands purchase a product from a monopolist. Consumers have a binary protected characteristic, which is associated with value distributions ranked in the likelihood ratio order. We characterize the revenue-maximizing market segmentation and pricing strategy subject to a non-discriminatory constraint, where consumers with different protected characteristics must face the same prices. This problem is equivalent to an optimal transport with a non-supermodular objective function. Outcomes induced by revenue-maximizing segmentations only differ in the surplus of consumers with the characteristic associated with low values, with the lowest surplus level being zero. In the meantime, consumers with the characteristic associated with high values always retain positive surplus, but suffer from deadweight losses.
JEL Classifications
  • D8 - Information, Knowledge, and Uncertainty
  • D4 - Market Structure, Pricing, and Design