Market Design: Understanding Markets Well Enough to Fix Them When They’re Broken

2010 ◽  
Author(s):  
Alvin E. Roth
Keyword(s):  
Market Design

Optimality Versus Practicality in Market Design: A Comparison of Two Double Auctions

2011 ◽  
Author(s):  
Mark A. Satterthwaite ◽  
Steven R. Williams ◽  
Konstantinos E. Zachariadis
Keyword(s):  
Market Design ◽  
Double Auctions

A Nice Electricity Market Design

2019 ◽  
Author(s):  
Chi-Keung Woo ◽  
Jay Zarnikau
Keyword(s):  
Electricity Market ◽  
Market Design

Autonomous Vehicle Market Design

2020 ◽  
Author(s):  
Zhen Lian ◽  
Garrett van Ryzin
Keyword(s):  
Market Design ◽  
Autonomous Vehicle ◽  
Vehicle Market

scholarly journals Combining Outcome-Based and Preference-Based Matching: A Constrained Priority Mechanism

2021 ◽  
pp. 1-24
Author(s):  
Avidit Acharya ◽  
Kirk Bansak ◽  
Jens Hainmueller
Keyword(s):  
Machine Learning ◽  
Host Country ◽  
Real World ◽  
Test Scores ◽  
Standardized Test ◽  
Market Design ◽  
Standardized Test Scores ◽  
Real World Data ◽  
Refugee Families ◽  
Strategy Proof

Abstract We introduce a constrained priority mechanism that combines outcome-based matching from machine learning with preference-based allocation schemes common in market design. Using real-world data, we illustrate how our mechanism could be applied to the assignment of refugee families to host country locations, and kindergarteners to schools. Our mechanism allows a planner to first specify a threshold $\bar g$ for the minimum acceptable average outcome score that should be achieved by the assignment. In the refugee matching context, this score corresponds to the probability of employment, whereas in the student assignment context, it corresponds to standardized test scores. The mechanism is a priority mechanism that considers both outcomes and preferences by assigning agents (refugee families and students) based on their preferences, but subject to meeting the planner’s specified threshold. The mechanism is both strategy-proof and constrained efficient in that it always generates a matching that is not Pareto dominated by any other matching that respects the planner’s threshold.

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