Balancing Bias in Two-sided Markets for Fair Stable Matchings
stable marriage; fairness
Yes
No ethics review needed.
- Is there a typo in line 304?
- The proposed method is novel and interesting. BSM is an interesting and well motivated problem in the stable matchings research community with several potential applications. The paper is overall well written but has some typos which makes it confusing to read.
- In terms of running time proposed exact algorithm outperforms the naive enumeration approach and compares favorably with approximation algorithms.
This paper develops an exact algorithm using carefully designed heuristics to solve the balanced stable marriage problem which is known to be NP-hard and demonstrates that the proposed algorithm is faster in practice than existing exact algorithms. In a stable marriage instance, there are two disjoint subsets or sides of agents, e.g., men and women, with each side having ranked preferences over agents on the other side. The goal is to find a matching \mu, a collection of disjoint pairs of men and women, that is stable, i.e., no pair of man and women prefer each other than their partner in \mu. Here, the rank that an agent i has for their partner \mu(i) can be thought of as a proxy for their dissatisfaction with the matching \mu. The total dissatisfaction of a side is the sum of dissatisfactions of agents on that side. 

In the balanced stable marriage problem (BSM), the goal is to find a stable marriage which minimizes the maximum among the dissatisfaction of the two sides, i.e., to balance the dissatisfaction of the two sides. This is motivated by a well known property of the set of all stable marriages that there is a man-optimal (which is also woman-pessimal) and a woman-optimal (which is also man-pessimal) stable marriage, and that the set of all stable marriages form a distributive lattice, and that it is possible to explore all stable marriages, from the man-optimal to woman-optimal stable matching through a series of operations called rotation elimination, each of which increases dissatisfaction of the men's side and decreases the dissatisfaction of the women's side. The set of rotations forms a poset and can be represented by a directed acyclic graph (DAG). 

The proposed algorithm performs an intelligent search of the space of all stable matchings using heuristics and pruning strategies obtained from a careful analysis of the structure of stable matchings and a classification of the set of rotations and "anti-chains", sets of rotations with no shared predecessors in the DAG or rotations. The paper identifies that it is sufficient to explore three sets of anti-chains (and induced stable matchings) in order to find a balanced stable matching from which they identify two sets of candidate rotations. This is the main technical contribution of the paper. The proposed algorithm takes as input the DAG of rotations, computable in polynomial time, and produces a balanced stable matching as output. The algorithm works by performing a local search by enumeration of stable matchings due to a restricted set of rotations. The local search is potentially exponential.

The section on experiments validates their approach with the proposed algorithm performing better than a naive approach in practice in terms of running time. A comparison with approximation algorithms is also included showing to what extend the minimax BSM objective is achieved by various algorithms in comparison with the proposed exact algorithm.
- I do not find a major weakness except the fit within ICLR. My lower score for the contribution is due to the review criteria which clearly states "Are the results valuable to share with the broader ICLR community". I am not confident that they are.
Yes
No ethics concerns
No ethics review needed.
See weakness section above.
Two-sided matching is a well-studied and well-motivated problem, from both a theoretical and practical perspective. The version of it that they study, called the Balanced Stable Marriage problem, aims to tackle a discrepancy that exists in the standard formulation of this two-sided matching which is optimal for individuals on one side of the market and pessimal for the other side. The efficiency bottleneck they aim to address in this problem is a practical barrier and the insights they present about the structure of these antichains may also prove useful for future work. The problem presentation in this work is well-motivated and the authors also do a reasonable job giving the key takeaways upfront. The experiments they present performed better than I would have expected and the authors present these results both on larger instances of synthetic data and real data from various domains.
This paper looks at the problem of stable matching in two-sided matching markets, which is a well-studied problem in the field of matching / mechanism design. In particular, the authors tackle the problem of finding a fair matching, as measured by comparing the two sides of the market, that is NP hard in its standard formulations: namely looking at the Balanced Stable Marriage problem. The balanced cost objective is non-convex and its minimization is NP hard. They are specifically motivated by the bottleneck created by the fact that the search space of all stable matching can grow exponentially in the size of the problem, and finding such a matching is infeasible in various real world applications. They authors present an algorithm, ISORROPIA, which uses a local search heuristic to reduce the search space and give an exact solution for instantiations that are reasonable in size. They do so by leveraging the structure of all feasible solutions to compactly represent them as a partially ordered sets. They present a theoretical analysis of this algorithm and experiments, using synthetic and real data, to show that ISORROPIA is more time and cost efficient than standard heuristics.
There are two key concerns with this work

(1) Specific to the results, while the first few sections of this paper were well-presented, the results are at times hard to decipher and follow along. It would be great if the authors revised the paper to provide intuition throughout the presentation of the algorithm. In particular, how are these anti-chains constructed and why does the pruning of the search space not seem to lead to significantly suboptimal solutions? 

(2) Since one of the most compelling things about this  ISORROPIA is that it may be of practical use, it would be great if the authors included several more synthetic experiments to show how the performance degrades in size and complexity of the problem. 

(3) Setting aside the above questions, it is not clear that ICLR is the right venue for this work as there doesn't seem to be any learning component.
Yes
No ethics review needed.
1. ISORROPIA narrows down the search space by focusing on promising antichains, yet the complexity of identifying these subsets is not thoroughly discussed. What is the theoretical bound on time complexity for extracting and evaluating these promising antichains, and under what conditions might this process become infeasible as the market size grows?

2.  Given that ISORROPIA minimizes the balance cost by searching over three specific sets of antichains, is there a mathematical guarantee that this focus always yields the exact minimum balance cost? If not, what bounds can be established for the deviation of ISORROPIA's solution from the true optimum?

3. What is the sensitivity of ISORROPIA’s performance to these initial stable marriages?

4. The rotation graph forms the basis for ISORROPIA’s local search, where each rotation represents a possible move in the solution space. How does the structure of the rotation graph (e.g., density of edges, distribution of antichains) affect the convergence and efficiency of ISORROPIA? Can we quantify the relationship between graph properties and the number of steps required to reach the optimal solution?

5. The paper states that the balance cost objective is non-convex, adding complexity to the optimization process. Can ISORROPIA guarantee convergence to a global minimum in all instances of the BSM problem, or are there instances where the algorithm might get stuck in local minima? If so, under what mathematical conditions does ISORROPIA ensure it escapes local minima?
The paper aims to achieve an exact solution to the Balanced Stable Marriage (BSM) problem, addressing fairness by minimizing dissatisfaction across both sides.  The introduced algorithm reduces computational overhead compared to baseline methods.
This paper addresses the Balanced Stable Marriage (BSM) problem, which aims to achieve fair, stable matchings in two-sided markets by balancing dissatisfaction across both sides. The authors introduce ISORROPIA, which finds exact, balanced stable marriages by focusing on promising subsets within the solution space. Through experimentation, ISORROPIA outperforms existing approaches, particularly in time efficiency, without sacrificing the fairness objective.
1. The paper lacks a comprehensive analysis of performance in diverse real-world applications.

2. The algorithm assumes static, pre-defined preference lists on both sides, which may not align with real-world two-sided markets where preferences are changing over time. 

3.  The algorithm focuses on a subset of promising antichains to reduce computational costs. However, this selection could inadvertently introduce bias by excluding other potentially fair matchings that fall outside the selected subset.

4. The paper compares ISORROPIA primarily with traditional baselines and established heuristics. Including comparisons with more recent advancements or adaptive heuristics in stable matching (such as learning-based stable matching) could be more better.
Yes
No ethics review needed.
1. In practical applications, what algorithm and performance measure are typically used for the stable marriage problem? Is the balance cost the dominant measure in practice? And does brute-force enumeration suffice in practice?
2. Have you considered how to construct instances where the proposed algorithm performs poorly? What are the limitations of the proposed algorithm?
3. Feel free to respond to the points raised in the "Weaknesses" section.
1. The paper is remarkably well-written and easy to follow, and the background on the problem is well-explained.
2. The main algorithm is rigorously stated and analyzed, with intuitive graphical illustrations to aid understanding.
3. The empirical evaluation against existing algorithms is solid and convincing, showing that the proposed algorithm is competitive in terms of running time and solution quality.
This paper considers the Balanced Stable Marriage (BSM) problem. In a two-sided market where each agent from one side has a preference list over agents from the other side, a stable matching is a matching in which no pair of agents prefers each other over their current partners. The seminal Gale-Shapley algorithm finds a stable matching in polynomial time. Subsequent work has focused on finding a stable matching that is optimal in some measure, such as regret cost, egalitarian cost, and sex-equality cost. The authors consider a specific measure of performance: the balance cost, which is the maximum of the total dissatisfaction of the two sides. The problem has been shown to be NP-hard, and the authors provide a heuristic algorithm that finds the optimal stable matching in balance cost. Although the algorithm is not guaranteed to run in polynomial time, the authors provide empirical evidence that it runs efficiently on practically relevant instances, outperforming existing algorithms in terms of running time or solution quality.
I have no major concerns about this paper and am happy to see it accepted. However, I have a few minor comments that the authors may consider addressing.

1. **Relevance.** This paper is of high quality, and I personally appreciate the results. However, it seems to me that the results are mainly of interest to the discrete algorithms community. It would be helpful if the authors could discuss the paper’s relevance to the broader AI community and why ICLR would be the right venue for this paper.
2. **Experiment design.** Some aspects of the experiments could be improved. For example, when reading Figure 4(d), I was initially surprised that `ENUM-` performs worse on smaller instances than on larger ones. Then I realized that this is because you limited the number of iterations for `ENUM-`, and for some reason, the smaller instances required more iterations for `ENUM-`. However, the running time of `ENUM-` on smaller instances is still much shorter than on larger instances. From a practical perspective, I think it would be more informative to limit the running time of the algorithms instead of the number of iterations.
We propose an efficient algorithm that returns the exact optimal solution to practical Balanced Stable Marriage problem instances.
@inproceedings{
anonymous2024balancing,
title={Balancing Bias in Two-sided Markets for Fair Stable Matchings},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=qykpnEWf2J},
note={under review}
}
The Balanced Stable Marriage (BSM) problem aims to find a stable matching in a two-sided market that minimizes the maximum dissatisfaction among two sides. The classical Deferred Acceptance algorithm merely produces an unfair stable marriage, providing optimal partners for one side while partially assigning pessimal partners to the other. Solving BSM is NP-hard, thwarting attempts to resolve the problem exactly. As the instance size increases in practice, recent studies have explored heuristics for finding a fair stable marriage but have not found an exact optimal solution for BSM efficiently. Nevertheless, in this paper we propose an efficient algorithm, Isorropia, that returns the exact optimal solution to practical BSM problem instances. Isorropia constructs two sets of candidate rotations from which it builds three sets of promising antichains, and performs local search on those three sets of promising antichains. Our extensive experimental study shows that Isorropia surpasses the time-efficiency of baselines that return the exact solution by up to three orders of magnitude.
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stable marriage; fairness
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alignment, fairness, safety, privacy, and societal considerations
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Balancing Bias in Two-sided Markets for Fair Stable Matchings
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