Two for One & One for All: Two-Sided Manipulation in Matching Markets

01/21/2022
by   Hadi Hosseini, et al.
0

Strategic behavior in two-sided matching markets has been traditionally studied in a "one-sided" manipulation setting where the agent who misreports is also the intended beneficiary. Our work investigates "two-sided" manipulation of the deferred acceptance algorithm where the misreporting agent and the manipulator (or beneficiary) are on different sides. Specifically, we generalize the recently proposed accomplice manipulation model (where a man misreports on behalf of a woman) along two complementary dimensions: (a) the two for one model, with a pair of misreporting agents (man and woman) and a single beneficiary (the misreporting woman), and (b) the one for all model, with one misreporting agent (man) and a coalition of beneficiaries (all women). Our main contribution is to develop polynomial-time algorithms for finding an optimal manipulation in both settings. We obtain these results despite the fact that an optimal one for all strategy fails to be inconspicuous, while it is unclear whether an optimal two for one strategy satisfies the inconspicuousness property. We also study the conditions under which stability of the resulting matching is preserved. Experimentally, we show that two-sided manipulations are more frequently available and offer better quality matches than their one-sided counterparts.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/08/2020

Accomplice Manipulation of the Deferred Acceptance Algorithm

The deferred acceptance algorithm is an elegant solution to the stable m...
research
12/10/2019

Approximate Strategy-Proofness in Large, Two-Sided Matching Markets

An approximation of strategy-proofness in large, two-sided matching mark...
research
06/08/2020

Provable Guarantees for General Two-sided Sequential Matching Markets

Two-sided markets have become increasingly more important during the las...
research
06/03/2021

Combinatorial Algorithms for Matching Markets via Nash Bargaining: One-Sided, Two-Sided and Non-Bipartite

This paper is an attempt to deal with the recent realization (Vazirani, ...
research
07/07/2021

Deep Learning for Two-Sided Matching

We initiate the use of a multi-layer neural network to model two-sided m...
research
07/14/2023

An IPW-based Unbiased Ranking Metric in Two-sided Markets

In modern recommendation systems, unbiased learning-to-rank (LTR) is cru...
research
09/20/2020

Almost Envy-free Repeated Matching in Two-sided Markets

A two-sided market consists of two sets of agents, each of whom have pre...

Please sign up or login with your details

Forgot password? Click here to reset