Non-Excludable Dynamic Mechanism Design

28 Pages Posted:

See all articles by Santiago Balseiro

Santiago Balseiro

Columbia Business School - Decision Risk and Operations

Vahab Mirrokni

Google Inc.

Renato Paes Leme

Google Inc.

Song Zuo

Google Inc.

Date Written: February 12, 2020

Abstract

Dynamic mechanism design expands the scope of allocations that can be implemented and the performance that can be attained compared to static mechanisms. Even under stringent participation constraints and restrictions on transfers, recent work demonstrated that it is possible for a designer to extract the surplus of all players as revenue when players have quasilinear utilities and the number of interactions is large. Much of the analysis has focused on excludable environments (i.e., any player can be excluded from trade without affecting the utilities of others). The mechanisms presented in the literature, however, do not extend to non-excludable environments. Two prototypical examples of such environments are: (i) public projects, where all players must have the same allocation; and (ii) non-disposable goods, where each item must be allocated to some player. We show a general mechanism that can asymptotically extract full surplus as revenue in such environments. Moreover, we provide a tight characterization for general environments, and identify necessary and sufficient conditions on the possibility of asymptotic full surplus extraction. Our characterization is based on the geometry of achievable utility sets -- convex sets that delineate the expected utilities that can be implemented by static mechanisms. Our results provide a reduction from dynamic to static mechanism design: the geometry of the achievable utility set of static mechanisms completely determines whether it is possible to fully extract surplus in the limit.

Keywords: Dynamic mechanism design, non-excludability, geometric method

Suggested Citation

Balseiro, Santiago and Mirrokni, Vahab and Paes Leme, Renato and Zuo, Song, Non-Excludable Dynamic Mechanism Design (February 12, 2020). Available at SSRN: https://ssrn.com/abstract=

Santiago Balseiro

Columbia Business School - Decision Risk and Operations ( email )

3022 Broadway
New York, NY 10027
United States

Vahab Mirrokni

Google Inc. ( email )

1600 Amphitheatre Parkway
Second Floor
Mountain View, CA 94043
United States

Renato Paes Leme

Google Inc. ( email )

1600 Amphitheatre Parkway
Second Floor
Mountain View, CA 94043
United States

Song Zuo (Contact Author)

Google Inc. ( email )

Beijing
China

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
5
Abstract Views
30
PlumX Metrics