Finding Personalized Good-Enough Solutions to Unsatisfiable Stable Roommates Problems
This work addresses the challenge of matching agents in roommate scenarios where traditional stable solutions are unavailable, offering a personalized approach that could benefit real-world applications like housing or team formation.
The paper tackles the problem of finding personalized 'good-enough' matchings for Stable Roommates problems when no stable solution exists, by incorporating agents' habits, preferences, and networks of preferred friends, and demonstrates its usefulness through examples and empirical evaluations.
The Stable Roommates problems are characterized by the preferences of agents over other agents as roommates. A solution is a partition of the agents into pairs that are acceptable to each other (i.e., they are in the preference lists of each other), and the matching is stable (i.e., there do not exist any two agents who prefer each other to their roommates, and thus block the matching). Motivated by real-world applications, and considering that stable roommates problems do not always have solutions, we continue our studies to compute "good-enough" matchings. In addition to the agents' habits and habitual preferences, we consider their networks of preferred friends, and introduce a method to generate personalized solutions to stable roommates problems. We illustrate the usefulness of our method with examples and empirical evaluations.