Majoritarian Assignment Rules
Pith reviewed 2026-05-21 13:06 UTC · model grok-4.3
The pith
In assignment problems the top cycle of majority preferences is restricted to one, two, all-but-two, all-but-one, or all allocations.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
We establish a near one-to-one correspondence between preference profiles and majority graphs in the assignment domain. This correspondence implies that Pareto-optimality, least unpopularity, and mixed popularity are properties of the majority graph alone. All Pareto-optimal assignments are semi-popular and belong to the top cycle; members of the top cycle can therefore be obtained by serial dictatorships. The top cycle admits a complete characterization: it consists of exactly one, two, all-but-two, all-but-one, or all assignments. By contrast, the uncovered set is always very small.
What carries the argument
the near one-to-one correspondence between preference profiles and majority graphs that arises from the structure of the assignment domain
If this is right
- Every Pareto-optimal assignment is semi-popular.
- Every Pareto-optimal assignment lies in the top cycle.
- Serial dictatorships produce members of the top cycle.
- The uncovered set contains only a very small number of assignments.
Where Pith is reading between the lines
- The same structural correspondence may make it possible to compute popular or stable assignments by operating only on the majority graph rather than on the full preference profile.
- Other restricted domains in social choice may exhibit similar restrictions on the size of the top cycle once their majority-graph structure is examined.
- The characterization suggests that checking membership in the top cycle for a given assignment can be reduced to checking a small number of graph-theoretic conditions.
Load-bearing premise
The assignment domain creates a near one-to-one correspondence between preference profiles and majority graphs.
What would settle it
A concrete preference profile over assignments whose top cycle contains three assignments, or any number other than one, two, all-but-two, all-but-one, or all of them.
Figures
read the original abstract
A central problem in multiagent systems is the fair assignment of objects to agents. In this paper, we initiate the analysis of classic majoritarian social choice functions in assignment. Exploiting the special structure of the assignment domain, we show a number of surprising results with no counterparts in general social choice. In particular, we establish a near one-to-one correspondence between preference profiles and majority graphs. This correspondence implies that key properties of assignments -- such as Pareto-optimality, least unpopularity, and mixed popularity -- can be determined solely by the associated majority graph. We further show that all Pareto-optimal assignments are semi-popular and belong to the top cycle. Elements of the top cycle can thus easily be found via serial dictatorships. Our main result is a complete characterization of the top cycle, which implies the top cycle can only consist of one, two, all but two, all but one, or all assignments. By contrast, we find that the uncovered set contains only very few assignments.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper initiates the study of majoritarian social choice functions applied to the assignment of indivisible objects to agents. Exploiting the special structure of the assignment domain, it claims a near one-to-one correspondence between strict preference profiles and majority graphs. This correspondence is used to show that Pareto optimality, least unpopularity, and mixed popularity are determined solely by the majority graph. All Pareto-optimal assignments are shown to be semi-popular and to lie in the top cycle, so that top-cycle elements can be found via serial dictatorships. The central result is a complete characterization of the top cycle implying that its size must be 1, 2, n-2, n-1, or n; by contrast the uncovered set is shown to contain only very few assignments.
Significance. If the structural correspondence and the top-cycle characterization hold, the paper delivers results with no direct counterparts in general social choice theory. The ability to read Pareto optimality and top-cycle membership directly from the majority graph, together with the sharp size restrictions on the top cycle, constitutes a substantive contribution to the theory of assignment mechanisms. The explicit link to serial dictatorships for locating top-cycle elements is a practical strength.
major comments (2)
- [Section 3 (correspondence result)] The central claim that key properties (Pareto optimality, semi-popularity, top-cycle membership) can be read off the majority graph alone rests on the asserted near one-to-one correspondence between preference profiles and majority graphs. The manuscript must supply an explicit construction of this mapping or a proof that rules out counterexamples in which two distinct profiles induce identical majority graphs yet differ in their Pareto sets or top-cycle membership; without such a demonstration the implications for Pareto optimality and the size characterization do not follow.
- [Main characterization theorem] Theorem establishing the top-cycle size restriction (sizes 1, 2, n-2, n-1, or n): the proof relies on the domain-specific structure of assignment problems. The argument should be checked for any unstated restrictions on the preference domain; if the structural correspondence fails for even one profile, the exhaustive list of admissible sizes would be incomplete.
minor comments (2)
- [Abstract and Section 2] Define 'mixed popularity' and 'least unpopularity' explicitly on first use; the abstract employs these terms without prior definition.
- [Section 4] Clarify the precise notion of 'semi-popular' used in the statement that all Pareto-optimal assignments are semi-popular; confirm it coincides with the standard definition in the majoritarian literature.
Simulated Author's Rebuttal
We thank the referee for the careful and constructive report. The comments focus on strengthening the presentation of the correspondence result and confirming the domain of the top-cycle characterization. We address each point below and will incorporate clarifications and an explicit construction in the revised manuscript.
read point-by-point responses
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Referee: [Section 3 (correspondence result)] The central claim that key properties (Pareto optimality, semi-popularity, top-cycle membership) can be read off the majority graph alone rests on the asserted near one-to-one correspondence between preference profiles and majority graphs. The manuscript must supply an explicit construction of this mapping or a proof that rules out counterexamples in which two distinct profiles induce identical majority graphs yet differ in their Pareto sets or top-cycle membership; without such a demonstration the implications for Pareto optimality and the size characterization do not follow.
Authors: We agree that greater explicitness will improve the exposition. The manuscript already derives the near one-to-one correspondence from the assignment domain's structure, but we will add to Section 3 an explicit construction of the mapping together with a lemma showing that any two strict preference profiles inducing the same majority graph necessarily share identical Pareto sets and the same top cycle. This addition will make the implications for Pareto optimality and the size characterization fully rigorous. revision: yes
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Referee: [Main characterization theorem] Theorem establishing the top-cycle size restriction (sizes 1, 2, n-2, n-1, or n): the proof relies on the domain-specific structure of assignment problems. The argument should be checked for any unstated restrictions on the preference domain; if the structural correspondence fails for even one profile, the exhaustive list of admissible sizes would be incomplete.
Authors: The proof of the top-cycle characterization applies to the standard domain of all strict linear orders over assignments. We have verified that the structural correspondence holds without exception for every profile in this domain; no unstated restrictions exist. The exhaustive enumeration of admissible sizes (1, 2, n-2, n-1, or n) follows directly from this complete coverage. We will insert a brief clarifying remark stating the domain explicitly and confirming the absence of counterexamples. revision: partial
Circularity Check
No circularity: structural correspondence and top-cycle characterization derived independently from assignment domain properties.
full rationale
The paper establishes a near one-to-one correspondence between strict preference profiles and majority graphs by exploiting the special structure of the assignment domain. This correspondence is then used to show that Pareto-optimality, semi-popularity, and top-cycle membership can be read off the majority graph alone, followed by a direct characterization of possible top-cycle sizes (1, 2, n-2, n-1, or n). No step reduces a claimed result to a quantity defined in terms of that result, nor does any load-bearing premise collapse to a self-citation or fitted input. The derivation remains self-contained against the domain axioms.
Axiom & Free-Parameter Ledger
axioms (1)
- domain assumption Agents possess strict linear preferences over all possible assignments.
Reference graph
Works this paper leans on
-
[1]
Atila Abdulkadiroğlu and Tayfun Sönmez. 1998. Random Serial Dictatorship and the Core from Random Endowments in House Allocation Problems.Econometrica 66, 3 (1998), 689–701
work page 1998
-
[2]
David J. Abraham, Robert W. Irving, Telikepalli Kavitha, and K. Mehlhorn. 2007. Popular matchings.SIAM J. Comput.37, 4 (2007), 1030–1034
work page 2007
-
[3]
Haris Aziz, Felix Brandt, and Paul Stursberg. 2013. On Popular Random Assign- ments. InProceedings of the 6th International Symposium on Algorithmic Game Theory (SAGT) (Lecture Notes in Computer Science (LNCS), Vol. 8146). Springer- Verlag, 183–194
work page 2013
-
[4]
2025.Single-Winner Voting on Matchings
Niclas Boehmer and Jessica Dierking. 2025.Single-Winner Voting on Matchings. Technical Report. https://arxiv.org/pdf/2601.19653
-
[5]
Georges Bordes. 1976. Consistency, Rationality and Collective Choice.Review of Economic Studies43, 3 (1976), 451–457
work page 1976
-
[6]
Georges Bordes. 1983. On the Possibility of Reasonable Consistent Majoritarian Choice: Some Positive Results.Journal of Economic Theory31, 1 (1983), 122–132
work page 1983
-
[7]
Georges Bordes, Michel Le Breton, and M. Salles. 1992. Gillies and Miller’s Subrelations of a Relation over an Infinite Set of Alternatives: General Results and Applications to Voting Games.Mathematics of Operations Research17, 3 (1992), 509–518
work page 1992
-
[8]
Sylvain Bouveret, Yann Chevaleyre, and Nicolas Maudet. 2016. Fair Allocation of Indivisible Goods. InHandbook of Computational Social Choice, Felix Brandt, Vincent Conitzer, Ulle Endriss, J. Lang, and Ariel D. Procaccia (Eds.). Cambridge University Press, Chapter 12
work page 2016
-
[9]
Felix Brandt, Markus Brill, and Paul Harrenstein. 2016. Tournament Solutions. InHandbook of Computational Social Choice, Felix Brandt, Vincent Conitzer, Ulle Endriss, J. Lang, and Ariel D. Procaccia (Eds.). Cambridge University Press, Chapter 3
work page 2016
-
[10]
Felix Brandt and Martin Bullinger. 2022. Finding and Recognizing Popular Coalition Structures.Journal of Artificial Intelligence Research74 (2022), 569–626
work page 2022
-
[11]
Felix Brandt and Felix Fischer. 2008. Computing the Minimal Covering Set. Mathematical Social Sciences56, 2 (2008), 254–268
work page 2008
-
[12]
Felix Brandt, Felix Fischer, and Paul Harrenstein. 2009. The Computational Complexity of Choice Sets.Mathematical Logic Quarterly55, 4 (2009), 444–459. Special Issue on Computational Social Choice
work page 2009
-
[13]
Felix Brandt, Johannes Hofbauer, and Martin Suderland. 2016. Majority Graphs of Assignment Problems and Properties of Popular Random Assignments. In Proceedings of the 6th International Workshop on Computational Social Choice (COMSOC)
work page 2016
-
[14]
Felix Brandt, Johannes Hofbauer, and Martin Suderland. 2017. Majority Graphs of Assignment Problems and Properties of Popular Random Assignments. InPro- ceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS). 335–343
work page 2017
-
[15]
Felix Brandt and Patrick Lederer. 2023. Characterizing the Top Cycle via Strate- gyproofness.Theoretical Economics18, 2 (2023), 837–883
work page 2023
-
[16]
Felix Brandt and Hans Georg Seedig. 2016. On the Discriminative Power of Tournament Solutions. InSelected Papers of the International Conference on Operations Research, OR2014. Springer-Verlag, 53–58
work page 2016
-
[17]
Markus Brill, Niclas Boehmer, and Ulrike Schmidt-Kraepelin. 2025. Proportional representation in matching markets: selecting multiple matchings under dichoto- mous preferences.Social Choice and Welfare64, 1–2 (2025), 179–220
work page 2025
-
[18]
Yann Chevaleyre, Paul E. Dunne, Ulle Endriss, Jérôme Lang, Michel Lemaître, Nicolas Maudet, Julian Padget, Steve Phelps, Juan A. Rodríguez-Aguilar, and Paulo Sousa. 2006. Issues in Multiagent Resource Allocation.Informatica30 (2006), 3–31
work page 2006
-
[19]
Ágnes Cseh. 2017. Popular Matchings. InTrends in Computational Social Choice, Ulle Endriss (Ed.). AI Access, Chapter 6
work page 2017
-
[20]
Ágnes Cseh, Chien-Chung Huang, and Telikepalli Kavitha. 2015. Popular match- ings with two-sided preferences and one-sided ties. InProceedings of the 42nd International Colloquium on Automata, Languages, and Programming (ICALP) (Lecture Notes in Computer Science (LNCS), Vol. 9134). Springer-Verlag, 367–379
work page 2015
-
[21]
John Duggan. 2013. Uncovered Sets.Social Choice and Welfare41, 3 (2013), 489–535
work page 2013
-
[22]
Bhaskar Dutta and Jean-François Laslier. 1999. Comparison Functions and Choice Correspondences.Social Choice and Welfare16, 4 (1999), 513–532
work page 1999
-
[23]
Mark Fey. 2008. Choosing From a Large Tournament.Social Choice and Welfare 31, 2 (2008), 301–309
work page 2008
- [24]
-
[25]
1960.The Theory of Linear Economic Models
David Gale. 1960.The Theory of Linear Economic Models. McGraw-Hill
work page 1960
-
[26]
David Gale and Lloyd S. Shapley. 1962. College Admissions and the Stability of Marriage.The American Mathematical Monthly69, 1 (1962), 9–15
work page 1962
-
[27]
Peter Gärdenfors. 1975. Match Making: Assignments based on bilateral prefer- ences.Behavioral Science20, 3 (1975), 166–173
work page 1975
-
[28]
William V. Gehrlein and Peter C. Fishburn. 1979. Proportions of Profiles with a Majority Candidate.Computers & Mathematics with Applications5, 2 (1979), 117–124
work page 1979
-
[29]
I. J. Good. 1971. A Note on Condorcet Sets.Public Choice10, 1 (1971), 97–101
work page 1971
-
[30]
Irving, Telikepalli Kavitha, Kurt Mehlhorn, Dimitrios Michail, and Katarzyna E
Robert W. Irving, Telikepalli Kavitha, Kurt Mehlhorn, Dimitrios Michail, and Katarzyna E. Paluch. 2006. Rank-maximal matchings.ACM Transactions on Algorithms2, 4 (2006), 602–610
work page 2006
-
[31]
Telikepalli Kavitha. 2020. Min-Cost Popular Matchings. InProceedings of the 40th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science. 25:1–25:17
work page 2020
-
[32]
Telikepalli Kavitha, Julián Mestre, and M. Nasre. 2011. Popular mixed matchings. Theoretical Computer Science412, 24 (2011), 2679–2690
work page 2011
-
[33]
Telikepalli Kavitha and Rohit Vaish. 2023. Semi-Popular Matchings and Copeland Winners. InProceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS). 957–965
work page 2023
-
[34]
Jérôme Lang and Lirong Xia. 2016. Voting in Combinatorial Domains. InHand- book of Computational Social Choice, Felix Brandt, Vincent Conitzer, Ulle Endriss, J. Lang, and Ariel D. Procaccia (Eds.). Cambridge University Press, Chapter 9
work page 2016
-
[35]
1997.Tournament Solutions and Majority Voting
Jean-François Laslier. 1997.Tournament Solutions and Majority Voting. Springer- Verlag
work page 1997
-
[36]
David F. Manlove. 2013.Algorithmics of Matching Under Preferences. World Scientific Publishing Company
work page 2013
-
[37]
Richard M. McCutchen. 2008. The Least-Unpopularity-Factor and Least- Unpopularity-Margin Criteria for Matching Problems with One-Sided Prefer- ences. InProceedings of the 8th Latin American Conference on Theoretical Infor- matics (LATIN) (Lecture Notes in Computer Science (LNCS), Vol. 4957). 593–604
work page 2008
-
[38]
Eric McDermid and Robert W. Irving. 2011. Popular Matchings: Structure and Algorithms.Journal of Combinatorial Optimization22, 3 (2011), 339–358
work page 2011
- [39]
-
[40]
Kurt Mehlhorn and Dimitrios Michail. 2006. Network Problems with Non- Polynomial Weights and Applications. (2006). Unpublished manuscript. Avail- able at https://pure.mpg.de/rest/items/item_2344037/component/file_2344036/ content
work page 2006
-
[41]
Alexander Schlenga. 2026.Code for the Paper "Majoritarian Assignment Rules". https://doi.org/10.5281/zenodo.18633210
-
[42]
1986.The Logic of Collective Choice
Thomas Schwartz. 1986.The Logic of Collective Choice. Columbia University Press
work page 1986
-
[43]
Lloyd S. Shapley and H. Scarf. 1974. On cores and indivisibility.Journal of Mathematical Economics1, 1 (1974), 23–37
work page 1974
-
[44]
John H. Smith. 1973. Aggregation of Preferences with Variable Electorate.Econo- metrica41, 6 (1973), 1027–1041
work page 1973
-
[45]
Tayfun Sönmez and M. Utku Ünver. 2011. Matching, Allocation, and Exchange of Discrete Resources. InHandbook of Social Economics, Jess Benhabib, Matthew O. Jackson, and Alberto Bisin (Eds.). Vol. 1. Elsevier, Chapter 17, 781–852
work page 2011
-
[46]
Benjamin Ward. 1961. Majority rule and allocation.Journal of Conflict Resolution 5, 4 (1961), 379–389. 9 A PROOF OF THEOREM 1 In this appendix, we present the proof of Theorem 1, which we break down into the following lemmas. Lemma 1.Let 𝑥, 𝑦∈𝑁 and 𝑝, 𝑞∈𝐻 be pairwise distinct. Consider any 𝜇 with 𝜇(𝑥)=𝑝 , 𝜇(𝑦)=𝑞 . Consider the matching 𝜆 which only differ...
work page 1961
-
[47]
Hence, by the same argument as before, we get that 𝜆¥ ∗ 𝜆′ and thus𝜆∈TC(𝑃). Case (2): Finally, assume that there are four distinct agents 𝑥, 𝑦, 𝑧, 𝑤 and two houses𝑝, 𝑞 such that𝑥 and 𝑦 top-rank 𝑝, and𝑤 and 𝑧 top-rank 𝑞. Once again, we let 𝜆 denote an arbitrary assignment with 𝜆(𝑥)=𝑝 and show that 𝜆∈TC(𝑃) . To this end, we define 𝜆′ as the assignment obtai...
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[48]
Just as before, this means that BC(𝑃) ∩TC(𝑃)=∅ , so TC(𝑃)=𝑀\PP(𝑃) andBC(𝑃)=PP(𝑃)
Further, it is straightforward to see that |PP(𝑃)|= 1. Just as before, this means that BC(𝑃) ∩TC(𝑃)=∅ , so TC(𝑃)=𝑀\PP(𝑃) andBC(𝑃)=PP(𝑃). Finally, we turn to case (v). To this end, we recall that by Lem- mas 5 and 6, both |TC(𝑃)|> 2and |BC(𝑃)|> 2if none of the first four cases apply. If 𝑛= 5, our claim follows directly from Claim (3) of Fact 1. We hence su...
discussion (0)
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