Some remarks on the modeling of discrete matching markets

2021 ◽  
Author(s):  
Marilda Sotomayor
Keyword(s):  
Matching Markets

Matching Markets via Descending Price

2019 ◽  
Author(s):  
Bo Waggoner ◽  
Eric Glen Weyl
Keyword(s):  
Matching Markets

Almost mutually best in matching markets: rank gaps and size of the core

2021 ◽  
Author(s):  
Flip Klijn ◽  
Markus Walzl ◽  
Christopher Kah
Keyword(s):  
Matching Markets ◽  
The Core

Application fee manipulations in matching markets

2013 ◽  
Vol 49 (6) ◽  
pp. 446-453 ◽  
Author(s):  
Mustafa Oǧuz Afacan
Keyword(s):  
Matching Markets

scholarly journals Matching markets under (in)complete information

2015 ◽  
Vol 157 ◽  
pp. 295-314 ◽  
Author(s):  
Lars Ehlers ◽  
Jordi Massó
Keyword(s):  
Complete Information ◽  
Matching Markets

scholarly journals Stability and Nash Implementation in Matching Markets with Couples

2008 ◽  
Author(s):  
Claus Jochen Haake ◽  
Bettina-Elisabeth Klaus
Keyword(s):  
Matching Markets ◽  
Nash Implementation

Designing Market Thickness and Optimal Frequency of Multi-Period Stable Matching in Cloud-Based Design and Manufacturing

2018 ◽  
Author(s):  
J. D. Thekinen ◽  
Yupeng Han ◽  
Jitesh H. Panchal
Keyword(s):  
Service Providers ◽  
Arrival Rate ◽  
Fixed Number ◽  
Simulation Studies ◽  
Matching Markets ◽  
Optimal Frequency ◽  
Optimal Matching ◽  
Design And Manufacturing ◽  
Sensitivity Studies ◽  
Utility Distribution

A central issue in two-sided matching markets such as Cloud-Based Design and Manufacturing (CBDM) where agents interact over a long period of time is the design of optimal matching period during recursive implementation. Existing literature provides mechanisms that satisfy useful properties such as stability in a single matching cycle, but they lack studies on the effect of the period of matching cycle on the optimality. To address this gap, we perform simulation studies on a synthetic CBDM scenario where service seekers arrive as a Poisson process with a fixed number of service providers offering resources. We identify the optimal matching period and assess its robustness using sensitivity studies. Optimality is measured in terms of utility obtained by the agents, the number of matches and fairness of the utility distribution. We show that a matching period equal to the ratio of the number of service providers to the arrival rate of service seekers is optimal.

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