Maximizing Efficiency in Dynamic Matching Markets

03/04/2018
by   Itai Ashlagi, et al.
0

We study the problem of matching agents who arrive at a marketplace over time and leave after d time periods. Agents can only be matched while they are present in the marketplace. Each pair of agents can yield a different match value, and the planner's goal is to maximize the total value over a finite time horizon. We study matching algorithms that perform well over any sequence of arrivals when there is no a priori information about the match values or arrival times. Our main contribution is a 1/4-competitive algorithm. The algorithm randomly selects a subset of agents who will wait until right before their departure to get matched, and maintains a maximum-weight matching with respect to the other agents. The primal-dual analysis of the algorithm hinges on a careful comparison between the initial dual value associated with an agent when it first arrives, and the final value after d time steps. It is also shown that no algorithm is 1/2-competitive. We extend the model to the case in which departure times are drawn i.i.d from a distribution with non-decreasing hazard rate, and establish a 1/8-competitive algorithm in this setting. Finally we show on real-world data that a modified version of our algorithm performs well in practice.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/09/2018

Maximum Weight Online Matching with Deadlines

We study the problem of matching agents who arrive at a marketplace over...
research
12/01/2020

Dynamic Weighted Matching with Heterogeneous Arrival and Departure Rates

We study a dynamic non-bipartite matching problem. There is a fixed set ...
research
06/21/2022

High Satisfaction in Thin Dynamic Matching Markets

Dynamic matching markets are an ubiquitous object of study with applicat...
research
10/20/2021

Dynamic Bipartite Matching Market with Arrivals and Departures

In this paper, we study a matching market model on a bipartite network w...
research
02/20/2023

Continuous Time Analysis of Dynamic Matching in Heterogeneous Networks

This paper addresses the problem of dynamic matching in heterogeneous ne...
research
03/07/2022

Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets

We study a Markov matching market involving a planner and a set of strat...
research
11/30/2017

Two-sided Facility Location

Recent years have witnessed the rise of many successful e-commerce marke...

Please sign up or login with your details

Forgot password? Click here to reset