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Two-Sided Matching for mentor-mentee allocations-Algorithms and manipulation strategies
Christian Haas1, Margeret Hall1
1College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE, United States of America.
Plos One
|March 13, 2019
Summary
This study explores multi-objective algorithms and preference manipulation in Two-Sided Matching (TSM). Multi-objective heuristics improve solution quality across criteria, while preference manipulation impacts outcomes differently based on the algorithm used.
Area of Science:
- Computational Social Science
- Algorithmic Game Theory
- Operations Research
Background:
- Two-Sided Matching (TSM) is crucial for resource allocation based on participant preferences.
- Existing TSM algorithms often prioritize a single objective, neglecting others.
- The practical impact of preference manipulation in TSM remains underexplored.
Purpose of the Study:
- To evaluate TSM algorithms considering multiple objectives.
- To analyze the effects of preference manipulation strategies on TSM outcomes.
- To investigate the interplay between manipulation and algorithm choice using real-world data.
Main Methods:
- Comparative analysis of standard TSM algorithms and multi-objective heuristics.
- Evaluation of three distinct preference manipulation strategies.
- Empirical assessment using real datasets from a Mentor-Mentee program.
Main Results:
- Standard and multi-objective algorithms perform comparably on single criteria for tested problem sizes.
- Multi-objective heuristics yield superior solutions across multiple criteria.
- Preference manipulation strategies demonstrably affect participants and solution quality, with varying impacts across algorithms.
Conclusions:
- Multi-objective optimization enhances TSM solutions beyond single-criterion approaches.
- Understanding preference manipulation is vital for robust TSM system design.
- Real-world data confirms the theoretical implications of manipulation in TSM.
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