pith. sign in

arxiv: 2503.20260 · v2 · submitted 2025-03-26 · 💻 cs.GT

Fair and efficient allocation of indivisible items under category constraints

Pith reviewed 2026-05-22 23:21 UTC · model grok-4.3

classification 💻 cs.GT
keywords fair divisionindivisible itemscategory constraintsenvy-freenessPareto optimalityKKM lemma
0
0 comments X

The pith

For any number of agents, there exists a Pareto-optimal allocation in which each can be made envy-free by reallocating at most min{k+1,n}(n-1) items.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper establishes existence results for fair allocations of indivisible items subject to category capacity constraints. It shows that a Pareto-optimal allocation can always be found such that envy-freeness is achievable by moving a bounded number of items, specifically at most min{k+1,n}(n-1) per agent. This generalizes a prior result for two agents to arbitrary n. When n is constant, the allocation can be computed efficiently. The proof introduces a novel use of the KKM lemma on weighted simplices.

Core claim

We prove that for any number n of agents, there always exists a Pareto-optimal allocation in which each agent can be made envy-free by reallocating at most min {k+1,n}(n-1) items. Moreover, when the number of agents is constant, we give a polynomial-time algorithm to compute such an allocation. Our results rely on a new application of the Knaster-Kuratowski-Mazurkiewicz (KKM) lemma to a simplex of agent weights.

What carries the argument

Application of the KKM lemma to a simplex of agent weights to guarantee existence of a Pareto-optimal allocation with bounded envy removal.

If this is right

  • Such allocations are computable in polynomial time for fixed n.
  • The envy removal bound scales with both the number of agents and categories.
  • The approach extends two-agent EF[1,1] guarantees to general n with bounded moves.
  • The KKM-based technique applies to other constrained indivisible allocation problems.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The bound implies that moves needed grow linearly with n but stay controlled relative to categories.
  • This could support practical computation of near-envy-free allocations under constraints without full search.
  • The method might tighten bounds or apply to chores mixed with goods in future extensions.

Load-bearing premise

The KKM lemma can be applied to a suitably defined simplex of agent weights to guarantee the existence of the desired allocation.

What would settle it

An instance with n agents and category capacities where every Pareto-optimal allocation requires reallocating more than min{k+1,n}(n-1) items to eliminate envy for some agent would disprove the claim.

read the original abstract

We study the problem of fairly allocating indivisible goods and chores under category constraints. Specifically, there are $n$ agents and $m$ indivisible items which are partitioned into categories with associated capacities. An allocation is considered feasible if each bundle satisfies the capacity constraints of its respective categories. For the case of two agents, Shoshan et al. (2023) recently developed a polynomial-time algorithm to find a Pareto-optimal allocation satisfying a relaxed version of envy-freeness, called EF$[1,1]$. Extending such guarantees beyond two agents has remained open. We make progress toward this question by proving that for any number $n$ of agents, there always exists a Pareto-optimal allocation in which each agent can be made envy-free by reallocating at most $\min \{k+1,n\}(n-1)$ items. Moreover, when the number of agents is constant, we give a polynomial-time algorithm to compute such an allocation. Our results rely on a new application of the Knaster-Kuratowski-Mazurkiewicz (KKM) lemma to a simplex of agent weights, which may be of independent interest for fair division problems involving indivisible items.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper proves that for any number n of agents and m indivisible items partitioned into categories with capacities, there exists a Pareto-optimal allocation such that each agent can be made envy-free by reallocating at most min{k+1,n}(n-1) items. It also gives a polynomial-time algorithm to compute such an allocation when n is constant. The existence proof relies on a new application of the KKM lemma to a simplex of agent weights.

Significance. If the central existence claim holds, the result meaningfully extends the two-agent EF[1,1] guarantee of Shoshan et al. (2023) to arbitrary n, with an explicit reallocation bound linear in n and depending on the number of categories k. The novel KKM application on a weight simplex is presented as potentially reusable for other indivisible fair-division settings. The fixed-n polynomial-time algorithm is a concrete algorithmic contribution.

major comments (1)
  1. [Proof of the main existence theorem (KKM application paragraph)] Proof of the main existence theorem (KKM application paragraph): the argument defines sets A_i on the simplex of agent weights and invokes KKM, but must explicitly verify that each A_i is closed and that the union covers every face while simultaneously enforcing category capacities and the min{k+1,n}(n-1) reallocation bound for EF. If a face weight vector cannot be assigned to any A_i without violating a capacity or the reallocation limit, the covering property fails and the claimed bound does not follow from KKM.
minor comments (2)
  1. [Abstract] Abstract: the parameter k is used in the bound without definition; it should be stated that k denotes the number of categories.
  2. [Proof section] The manuscript should include a short self-contained statement of the precise KKM lemma variant being applied (including the precise topological assumptions on the simplex and the sets).

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the careful reading of our manuscript and for highlighting the need for greater explicitness in the KKM application. We address the major comment below and will incorporate the requested clarifications in the revised version.

read point-by-point responses
  1. Referee: [Proof of the main existence theorem (KKM application paragraph)] Proof of the main existence theorem (KKM application paragraph): the argument defines sets A_i on the simplex of agent weights and invokes KKM, but must explicitly verify that each A_i is closed and that the union covers every face while simultaneously enforcing category capacities and the min{k+1,n}(n-1) reallocation bound for EF. If a face weight vector cannot be assigned to any A_i without violating a capacity or the reallocation limit, the covering property fails and the claimed bound does not follow from KKM.

    Authors: We agree that the current paragraph would benefit from a more explicit, step-by-step verification. In the revision we will add a dedicated subsection that (i) proves each A_i is closed by showing it is the inverse image of a closed set under a continuous map derived from the envy and capacity functions; (ii) establishes the covering property on every face of the weight simplex by constructing, for any weight vector on a face, a feasible allocation that respects category capacities and satisfies the reallocation bound of at most min{k+1,n}(n-1) items per agent, thereby placing the vector in at least one A_i; and (iii) confirms that the bound is preserved uniformly because the construction never requires more than that many swaps to eliminate envy while maintaining Pareto optimality. This will directly address the concern that a face vector might fall outside all A_i. revision: yes

Circularity Check

0 steps flagged

No circularity; existence via external KKM lemma on constructed simplex

full rationale

The paper derives the claimed existence result by defining a simplex over agent weights and invoking the standard KKM lemma to produce a weight vector inducing a PO allocation satisfying the reallocation bound. KKM is an independent topological theorem, not defined in terms of the target bound or fitted to any data. No self-citations are load-bearing for the central claim, no parameters are fitted then renamed as predictions, and the construction does not reduce the bound to an input by definition. The derivation is self-contained against external mathematical benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central existence claim rests on the standard KKM lemma applied to a constructed simplex; no free parameters or new entities are introduced.

axioms (1)
  • standard math Knaster-Kuratowski-Mazurkiewicz (KKM) lemma
    Invoked to prove existence via a simplex of agent weights (abstract).

pith-pipeline@v0.9.0 · 5740 in / 1150 out tokens · 91852 ms · 2026-05-22T23:21:02.517264+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

15 extracted references · 15 canonical work pages

  1. [1]

    Fair allocation of indivisible goods and chores

    Haris Aziz, Ioannis Caragiannis, Ayumi Igarashi, and To by W alsh. Fair allocation of indivisible goods and chores. Autonomous Agents and Multi-Agent Systems , 36(1):3:1–3:21, 2022

  2. [2]

    Finding fair and efficient allocations

    Siddharth Barman, Sanath Kumar Krishnamurthy, and Rohi t Vaish. Finding fair and efficient allocations. In Proceedings of the 19th ACM Conference on Economics and Comp utation (EC) , pages 557–574, 2018

  3. [3]

    Brams and Alan D

    Steven J. Brams and Alan D. Taylor. Fair Division: From Cake-Cutting to Dispute Resolution . Cambridge Uni- versity Press, 1996

  4. [4]

    The combinatorial assignment problem: App roximate competitive equilibrium from equal incomes

    Eric Budish. The combinatorial assignment problem: App roximate competitive equilibrium from equal incomes. Journal of Political Economy , 119(6):1061–1103, 2011

  5. [5]

    Procaccia, Nisarg Shah, and Junxing W ang

    Ioannis Caragiannis, David Kurokawa, Herv´ e Moulin, Ar iel D. Procaccia, Nisarg Shah, and Junxing W ang. The unreasonable fairness of maximum Nash welfare. ACM Transactions on Economics and Computation , 7(3):12:1– 12:32, 2019. 10 AYUMI IGARASHI AND FR ´ED ´ERIC MEUNIER

  6. [6]

    Con strained fair and efficient allocations

    Benjamin Cookson, Soroush Ebadian, and Nisarg Shah. Con strained fair and efficient allocations. In Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI ), volume 39, 2025. Extended version available at https://arxiv.org/abs/2411.00133

  7. [7]

    On f air division under heterogeneous matroid constraints

    Amitay Dror, Michal Feldman, and Erel Segal-Halevi. On f air division under heterogeneous matroid constraints. Journal of Artificial Intelligence Research , 76:567–611, 2023

  8. [8]

    Consensus of subjectiv e probabilities: The pari-mutuel method

    Edmund Eisenberg and David Gale. Consensus of subjectiv e probabilities: The pari-mutuel method. The Annals of Mathematical Statistics , 30(1):165–168, 1959

  9. [9]

    Ein Beweis des Fixpunktsatzes f¨ ur n- dimensionale Simplexe

    Bronis/suppress law Knaster, Kazimierz Kuratowski, and Stefan Mazurkiewicz. Ein Beweis des Fixpunktsatzes f¨ ur n- dimensionale Simplexe. Fundamenta Mathematicae, 14(1):132–137, 1929

  10. [10]

    Springer, 2007

    Jiˇ r ´ ı Matouˇ sek and Bernd G¨ artner.Understanding and using linear programming , volume 1. Springer, 2007

  11. [11]

    W elfare economics and existence of an equilibrium for a competitive economy

    Takashi Negishi. W elfare economics and existence of an equilibrium for a competitive economy. Metroeconomica, 12(2-3):92–97, 1960

  12. [12]

    Combinatorial optimization: polyhedra and efficiency , volume A

    Alexander Schrijver. Combinatorial optimization: polyhedra and efficiency , volume A. Springer

  13. [13]

    Efficie nt nearly-fair division with capacity constraints

    Hila Shoshan, Noam Hazon, and Erel Segal-Halevi. Efficie nt nearly-fair division with capacity constraints. In Proceedings of the 22nd International Conference on Autono mous Agents and Multiagent Systems (AAMAS) , pages 206–214, 2023

  14. [14]

    Constraints in fair division

    W arut Suksompong. Constraints in fair division. ACM SIGecom Exchanges , 19(2):46–61, 2021

  15. [15]

    Hal R. Varian. Two problems in the theory of fairness. Journal of Public Economics , 5(3):249–260, 1976. University of Tokyo, Tokyo, Japan Email address : igarashi@mist.i.u-tokyo.ac.jp ´Ecole Nationale des Ponts et Chauss ´ees, France Email address : frederic.meunier@enpc.fr