Strategy-proof exchange under trichotomous preferences

2021 ◽  
Vol 193 ◽  
pp. 105197
Author(s):  
Vikram Manjunath ◽  
Alexander Westkamp
Keyword(s):  
Strategy Proof

Strategy-proof mechanism for online resource allocation in cloud and edge collaboration

Computing ◽  
2021 ◽  
Author(s):  
Jixian Zhang ◽  
Laixin Chi ◽  
Ning Xie ◽  
Xutao Yang ◽  
Xuejie Zhang ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Online Resource ◽  
Strategy Proof

Adaptive strategy-proof double auction mechanism for heterogeneous spectrum allocation

2015 ◽  
Vol 9 (10) ◽  
pp. 1289-1297 ◽  
Author(s):  
Mojtaba Mazoochi ◽  
Mohammad Ali Pourmina ◽  
Hamidreza Bakhshi
Keyword(s):  
Adaptive Strategy ◽  
Spectrum Allocation ◽  
Double Auction ◽  
Auction Mechanism ◽  
Strategy Proof

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.

Sign in / Sign up

Export Citation Format

Share Document