Search arXiv⌕ Search

arXiv · 2411.19881

EF1 Allocations for Identical Trilean and Separable Single-Peaked Valuations

Abstract

In the fair division of items among interested agents, envy-freeness is possibly the most favoured and widely studied formalisation of fairness. For indivisible items, envy-free allocations may not exist in trivial cases, and hence research and practice focus on relaxations, particularly envy-freeness up to one item (EF1). A significant reason for the popularity of EF1 allocations is its simple fact of existence. It is known that EF1 allocations exist for two agents with arbitrary valuations; agents with doubly-monotone valuations; agents with Boolean valuations; and identical agents with negative Boolean valuations. We consider two new but natural classes of valuations, and partly extend results on the existence of EF1 allocations to these valuations. Firstly, we consider trilean valuations - an extension of Boolean valuations - when the value of any subset is 0, $a$, or $b$ for any integers $a$ and $b$. Secondly, we define separable single-peaked valuations, when the set of items is partitioned into types. For each type, an agent's value is a single-peaked function of the number of items of the type. The value for a set of items is the sum of values for the different types. We prove EF1 existence for identical trilean valuations for any number of agents, and for separable single-peaked valuations for three agents. For both classes of valuations, we also show that EFX allocations do not exist.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Umang Bhaskar, Gunjan Kumar, Yeshwant Pandit, Rakshitha. 2024-11-29. EF1 Allocations for Identical Trilean and Separable Single-Peaked Valuations. https://arxiv.org/abs/2411.19881

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Money Burning Mechanism Design: From Welfare to Surplus

We settle the worst-case approximability of consumer-surplus maximization in general multidimensional mechanism-design environments. We do so through two black-box reductions from welfare maximization to the agents' total utility. Our first reduction turns exact welfare maximization into a prior-free, universally truthful and ex-post individually rational mechanism that preserves at least a $1/H_n$ fraction of optimal welfare as expected consumer surplus. The guarantee holds for $n$ agents with arbitrary nonnegative valuations over a finite outcome space, where $H_n$ is the $n$-th harmonic number. The factor $H_n$ is worst-case optimal, including its constant, even for a single-item auction with a known i.i.d. prior and Bayesian incentive compatibility. Our second reduction allows existing truthful welfare approximation mechanisms to be reused for surplus maximization. For valuation classes closed under scaling, it converts any ex-post individually rational, truthful $α$-approximation for welfare with nonnegative payments into an $O(α\log(n))$-approximation for surplus. Our sharp guarantee resolves the welfare-approximation aspect of the open question of Hartline and Roughgarden [2008] on the power of money burning beyond $k$-unit auctions, and the question of Ezra et al. [2025] concerning optimal surplus guarantees for broader valuation classes. It also replaces the outcome-dependent $O(\log|\mathcal{O}|)$ guarantee of Fotakis et al. [2015] with the tight agent-dependent factor $H_n$. These results yield polynomial-time mechanisms with the exact $H_n$ guarantee for gross-substitutes. They also give prior-free, universally truthful approximations of $O(H_n\log^2\log m)$ for XOS valuations and $O(H_n\log^3\log m)$ for subadditive valuations using demand and value queries, where $m$ is the number of items.

cs.GT↗

Self-Bounding Regret Matching+ in Potential Games and Product-Simplex Optimization

Regret matching+ (RM+) is parameter free, scale invariant, and central to large game solving, but its only general individual-regret guarantee grows as $\sqrt{T}$. A recent ICLR result used this envelope to prove that RM+ reaches an $ε$-stationary point of a smooth objective over a product of simplices in $O(ε^{-4})$ iterations, or $O(ε^{-8})$ from the standard zero initialization. We give an exact one-step conservation law for RM+. It states that forward utility gain pays for both squared state motion and growth of the regret-state norm. Norm growth is at most $\sqrt{m-1}$ times forward gain for $m$ actions, and the coefficient is sharp. This yields four results for unmodified RM+. Its regret on any utility path is controlled by centered temporal variation. Its regret is uniformly bounded under alternating play in every finite exact potential game, resolving an open question and making squared activation gaps summable. Both certified lazy and ordinary cyclic play attain an $ε^{-2}$ exponent. On any smooth, possibly nonconcave simplex objective, RM+ finds an $ε$-KKT point in $O(ε^{-2})$ iterations. Most broadly, for a smooth objective over an arbitrary product of simplices, cyclic block RM+ attains the same $O(ε^{-2})$ exponent from arbitrary initialization, with an explicit trajectory-dependent constant. The proof controls the finite objective loss caused by low-state blocks and then self-bounds every block state and the total squared path length. Complete proofs cover zero states, sharpness, common-profile stationarity, and robust gain dominance. Oracle-normalized diagnostics compare RM+ with predictive and smooth extra-gradient variants on graphical potential games and dense nonconvex objectives.

cs.GT↗

Randomized Online Fair Division: High-Probability and Expected Realized Fairness

We study randomized algorithms for the fully online allocation of indivisible goods among $n\ge2$ agents with nonnegative additive valuations. Goods arrive sequentially and must be allocated immediately and irrevocably, with only $n$ known in advance. Since exact ex-ante envy freeness and proportionality are readily achievable, while no positive ex-post approximation is possible for the fairness notions considered here, we study the intermediate notions of high-probability fairness and expected realized fairness. Against a non-adaptive adversary, we give a randomized algorithm for proportionality up to one good (PROP1) whose parameter depends only on $n$ and that preserves exact ex-ante envy-freeness and proportionality. At confidence $1-δ$, its PROP1 guarantee improves on independent uniform allocation (Rand) by a factor of $Ω(\log n)$, uniformly over $δ\in(0,1/2]$. As $n\to\infty$, its expected realized PROP1 factor is at least $\frac{3-\sqrt5}{2}-o(1)$. We also show that the expected realized PROP1 factor of Rand is $(1+o(1))/\log n$, yielding an improvement of at least $\bigl(\frac{3-\sqrt5}{2}-o(1)\bigr)\log n$ for our algorithm. For every randomized online algorithm and every positive approximation factor, the success probability can be made arbitrarily small for envy freeness up to any good (EFX) and at most $\frac{n+1}{2n}$ for envy freeness up to one good (EF1). Consequently, every randomized fully online algorithm has an expected realized EFX guarantee of zero and an expected realized EF1 guarantee of at most $\frac{n+1}{2n}$.

cs.GT↗