Maximin Shares in Hereditary Set Systems

We study the problem of fair allocation of M indivisible items among N agents using the popular notion of maximin share as our measure of fairness. The maximin share of an agent is the largest value she can guarantee herself if she is allowed to choose a partition of the items into N bundles (one for each agent), on the condition that she receives her least preferred bundle. A maximin share allocation provides each agent a bundle worth at least their maximin share. While it is known that such an allocation need not exist [Procaccia and Wang, 2014; Kurokawa et al., 2016], a series of work [Procaccia and Wang, 2014; David Kurokawa et al., 2018; Amanatidis et al., 2017; Barman and Krishna Murthy, 2017] provided 2/3 approximation algorithms in which each agent receives a bundle worth at least 2/3 times their maximin share. Recently, [Ghodsi et al., 2018] improved the approximation guarantee to 3/4. Prior works utilize intricate algorithms, with an exception of [Barman and Krishna Murthy, 2017] which is a simple greedy solution but relies on sophisticated analysis techniques. In this paper, we propose an alternative 2/3 maximin share approximation which offers both a simple algorithm and straightforward analysis. In contrast to other algorithms, our approach allows for a simple and intuitive understanding of why it works.

We study the problem of computing maximin share guarantees, a recently introduced fairness notion. Given a set of \(n\) agents and a set of goods, the maximin share of a single agent is the best that she can guarantee to herself, if she would be allowed to partition the goods in any way she prefers, into \(n\) bundles, and then receive her least desirable bundle. The objective then in our problem is to find a partition, so that each agent is guaranteed her maximin share. In settings with indivisible goods, such allocations are not guaranteed to exist, hence, we resort to approximation algorithms. Our main result is a \(2/3\)-approximation, that runs in polynomial time for any number of agents. This improves upon the algorithm of Procaccia and Wang [14], which also produces a \(2/3\)-approximation but runs in polynomial time only for a constant number of agents. We then investigate the intriguing case of \(3\) agents, for which it is already known that exact maximin share allocations do not always exist. We provide a \(6/7\)-approximation algorithm for this case, improving on the currently known ratio of \(3/4\). Finally, we undertake a probabilistic analysis. We prove that in randomly generated instances, with high probability there exists a maximin share allocation. This can be seen as a justification of the experimental evidence reported in [5, 14], that maximin share allocations exist almost always.

We study fair distribution of a collection of m indivisible goods among a group of n agents, using the widely recognized fairness principles of Maximin Share (MMS) and Any Price Share (APS). These principles have undergone thorough investigation within the context of additive valuations. We explore these notions for valuations that extend beyond additivity. First, we study approximate MMS under the separable (piecewise-linear) concave (SPLC) valuations, an important class generalizing additive, where the best known factor was 1/3-MMS. We show that 1/2-MMS allocation exists and can be computed in polynomial time, significantly improving the state-of-the-art. We note that SPLC valuations introduce an elevated level of intricacy in contrast to additive. For instance, the MMS value of an agent can be as high as her value for the entire set of items. We use a relax-and-round paradigm that goes through competitive equilibrium and LP relaxation. Our result extends to give (symmetric) 1/2-APS, a stronger guarantee than MMS. APS is a stronger notion that generalizes MMS by allowing agents with arbitrary entitlements. We study the approximation of APS under submodular valuation functions. We design and analyze a simple greedy algorithm using concave extensions of submodular functions. We prove that the algorithm gives a 1/3-APS allocation which matches the best-known factor. Concave extensions are hard to compute in polynomial time and are, therefore, generally not used in approximation algorithms. Our approach shows a way to utilize it within analysis (while bypassing its computation), and hence might be of independent interest.

We study the problem of fair allocation of m indivisible items among n agents with additive valuations using the popular notion of maximin share (MMS) as our measure of fairness. An MMS allocation provides each agent a bundle worth at least her maximin share. While it is known that such an allocation need not exist [5, 7], a series of remarkable work [1-3, 6, 7] provided 2/3 approximation algorithms in which each agent receives a bundle worth at least 2/3 times her maximin share. More recently, [4] showed the existence of 3/4 MMS allocations and a PTAS to find a 3/4 - e MMS allocation. Most of the previous works utilize intricate algorithms and require agents' approximate MMS values, which are computationally expensive to obtain.

We study the problem of computing fair divisions of a set of indivisible goods among agents with additive valuations. For the past many decades, the literature has explored various notions of fairness, that can be primarily seen as either having envy-based or share-based lens. For the discrete setting of resource-allocation problems, envy-free up to any good (EFX) and maximin share (MMS) are widely considered as the flag-bearers of fairness notions in the above two categories, thereby capturing different aspects of fairness herein. Due to lack of existence results of these notions and the fact that a good approximation of EFX or MMS does not imply particularly strong guarantees of the other, it becomes important to understand the compatibility of EFX and MMS allocations with one another. In this work, we identify a novel way to simultaneously achieve MMS guarantees with EFX/EF1 notions of fairness, while beating the best known approximation factors by Chaudhury et al. and Amanatidis et al. Our main contribution is to constructively prove the existence of (i) a partial allocation that is both 2/3-MMS and EFX, and (ii) a complete allocation that is both 2/3-MMS and EF1. Our algorithms run in pseudo-polynomial time if the approximation factor for MMS is relaxed to 2/3 - e for any constant e>0 and in polynomial time if, in addition, the EFX (or EF1) guarantee is relaxed to (1-d)-EFX (or (1-d)-EF1) for any constant d>0. In particular, we improve from the best approximation factor known prior to our work by Chaudhury et al., which computes partial allocations that are 1/2-MMS and EFX in pseudo-polynomial time.


 
 
 The problem of fair division of indivisible goods is a fundamental problem of resource allocation in multi-agent systems, also studied extensively in social choice. Recently, the problem was generalized to the case when goods form a graph and the goal is to allocate goods to agents so that each agent’s bundle forms a connected subgraph. For the maximin share fairness criterion, researchers proved that if goods form a tree, an allocation offering each agent a bundle of at least her maximin share value always exists. Moreover, it can be found in polynomial time. In this paper we consider the problem of maximin share allocations of goods on a cycle. Despite the simplicity of the graph, the problem turns out to be significantly harder than its tree version. We present cases when maximin share allocations of goods on cycles exist and provide in this case results on allocations guaranteeing each agent a certain fraction of her maximin share. We also study algorithms for computing maximin share allocations of goods on cycles.
 
 

We consider the fair division of indivisible items using the maximin shares measure. Recent work on the topic has focused on extending results beyond the class of additive valuation functions. In this spirit, we study the case where the items form an hereditary set system. We present a simple algorithm that allocates each agent a bundle of items whose value is at least 0.3667 times the maximin share of the agent. This improves upon the current best known guarantee of 0.2 due to Ghodsi et al. The analysis of the algorithm is almost tight; we present an instance where the algorithm provides a guarantee of at most 0.3738. We also show that the algorithm can be implemented in polynomial time given a valuation oracle for each agent.

We consider the fair division of indivisible items using the maximin shares measure. Recent work on the topic has focused on extending results beyond the class of additive valuation functions. In this spirit, we study the case where the items form a hereditary set system. We present a simple algorithm that allocates each agent a bundle of items whose value is at least 0.3666 times the maximin share of the agent. This improves upon the current best known guarantee of 0.2 due to Ghodsi et al. The analysis of the algorithm is almost tight; we present an instance where the algorithm provides a guarantee of at most 0.3738. We also show that the algorithm can be implemented in polynomial time given a valuation oracle for each agent.

We study the problem of computing maximin share allocations, a recently introduced fairness notion. Given a set of n agents and a set of goods, the maximin share of an agent is the best she can guarantee to herself, if she is allowed to partition the goods in any way she prefers, into n bundles, and then receive her least desirable bundle. The objective then is to find a partition, where each agent is guaranteed her maximin share. Such allocations do not always exist, hence we resort to approximation algorithms. Our main result is a 2/3-approximation that runs in polynomial time for any number of agents and goods. This improves upon the algorithm of Procaccia and Wang (2014), which is also a 2/3-approximation but runs in polynomial time only for a constant number of agents. To achieve this, we redesign certain parts of the algorithm in Procaccia and Wang (2014), exploiting the construction of carefully selected matchings in a bipartite graph representation of the problem. Furthermore, motivated by the apparent difficulty in establishing lower bounds, we undertake a probabilistic analysis. We prove that in randomly generated instances, maximin share allocations exist with high probability. This can be seen as a justification of previously reported experimental evidence. Finally, we provide further positive results for two special cases arising from previous works. The first is the intriguing case of three agents, where we provide an improved 7/8-approximation. The second case is when all item values belong to {0, 1, 2}, where we obtain an exact algorithm.

**Read paper on the following link:** https://ifaamas.org/Proceedings/aamas2022/pdfs/p597.pdf **Abstract:** We study the problem of fairly allocating a set of $m$ indivisible chores (i.e. items with non-positive value) to $n$ agents. We consider the desirable fairness notion of $1$-out-of-$d$ maximin share (MMS)---the minimum value that an agent can guarantee by partitioning items into $d$ bundles and selecting the least valued bundle---and focus on ordinal approximation of MMS that aims at finding the largest $d$ for which $1$-out-of-$d$ MMS exists. Our main theoretical contribution is showing the existence of $1$-out-of-$\lfloor\frac{3n}{4}\rfloor$ MMS allocations and proving that $1$-out-of-$\lfloor\frac{2n}{3}\rfloor$ MMS allocations of chores can be computed in polynomial time. Furthermore, we show that practical polynomial-time algorithms exist for approximating $1$-out-of-$\lfloor\frac{3n}{4}\rfloor$ MMS bound for chores. This is in contrast to computing allocations that guarantee a fraction of MMS to each agent, where only a polynomial-time approximation scheme (PTAS) with run-time exponential in the approximation accuracy $1/\epsilon$ is known.

**Read paper on the following link:** https://ifaamas.org/Proceedings/aamas2022/pdfs/p597.pdf **Abstract:** We study the problem of fairly allocating a set of $m$ indivisible chores (i.e. items with non-positive value) to $n$ agents. We consider the desirable fairness notion of $1$-out-of-$d$ maximin share (MMS)---the minimum value that an agent can guarantee by partitioning items into $d$ bundles and selecting the least valued bundle---and focus on ordinal approximation of MMS that aims at finding the largest $d$ for which $1$-out-of-$d$ MMS exists. Our main theoretical contribution is showing the existence of $1$-out-of-$\lfloor\frac{3n}{4}\rfloor$ MMS allocations and proving that $1$-out-of-$\lfloor\frac{2n}{3}\rfloor$ MMS allocations of chores can be computed in polynomial time. Furthermore, we show that practical polynomial-time algorithms exist for approximating $1$-out-of-$\lfloor\frac{3n}{4}\rfloor$ MMS bound for chores. This is in contrast to computing allocations that guarantee a fraction of MMS to each agent, where only a polynomial-time approximation scheme (PTAS) with run-time exponential in the approximation accuracy $1/\epsilon$ is known.

The problem of fair division of indivisible goods is a fundamental problem of social choice. Recently, the problem was extended to the setting when goods form a graph and the goal is to allocate goods to agents so that each agent's bundle forms a connected subgraph. Researchers proved that, unlike in the original problem (which corresponds to the case of the complete graph in the extended setting), in the case of the goods-graph being a tree, allocations offering each agent a bundle of or exceeding her maximin share value always exist. Moreover, they can be found in polynomial time. We consider here the problem of maximin share allocations of goods on a cycle. Despite the simplicity of the graph, the problem turns out be significantly harder than its tree version. We present cases when maximin share allocations of goods on cycles exist and provide results on allocations guaranteeing each agent a certain portion of her maximin share. We also study algorithms for computing maximin share allocations of goods on cycles.

In this paper we initiate the study of finding fair and efficient allocations of an indivisible mixed manna: Divide m indivisible items among n agents under the fairness notion of maximin share (MMS) and the efficiency notion of Pareto optimality (PO). A mixed manna allows an item to be a good for some agents and a chore for others. The problem of finding $\alpha$-MMS allocation for the (near) best $\alpha\in(0,1]$ for which it exists, remains unresolved even for a goods manna with constantly many agents, while the problem of finding $\alpha$-MMS+PO allocation is unexplored for any $\alpha\in(0,1]$. We make significant progress on the above questions for a mixed manna. First, we show that for any $\alpha>0$, an $\alpha$-MMS allocation may not always exist, thus ruling out solving the problem for a fixed $\alpha$. Second, towards computing $\alpha$-MMS+PO allocation for the best possible $\alpha$, we obtain a dichotomous result: We derive two conditions and show that the problem is tractable under these two conditions, while dropping either renders the problem intractable. The two conditions are: (i) number of agents is a constant, and (ii) for every agent, her absolute value for all the items is at least a constant factor of her total (absolute) value for all the goods or all the chores. In particular, first, for instances satisfying (i) and (ii) we design a PTAS - an efficient algorithm to find an $(\alpha-\epsilon)$-MMS and $\gamma$-PO allocation when given $\epsilon,\gamma>0$, for the highest possible $\alpha\in(0,1]$. Second, we show that if either condition is not satisfied then finding an $\alpha$-MMS allocation for any $\alpha\in(0,1]$ is NP-hard, even when a solution exists for $\alpha=1$. To the best of our knowledge, ours is the first algorithm that ensures both approximate MMS and PO guarantees.

This paper is concerned with various types of allocation problems in fair division of indivisible goods, aiming at maximin share, proportional share, and minimax share allocations. However, such allocations do not always exist, not even in very simple settings with two or three agents. A natural question is to ask, given a problem instance, what is the largest value c for which there is an allocation such that every agent has utility of at least c times her fair share. We first prove that the decision problem of checking if there exists a minimax share allocation for a given problem instance is $$\mathrm {NP}$$ -hard when the agents’ utility functions are additive. We then show that, for each of the three fairness notions, one can approximate c by a polynomial-time approximation scheme, assuming that the number of agents is fixed. Next, we investigate the restricted cases when utility functions have values in $$\{0,1\}$$ only or are defined based on scoring vectors (Borda and lexicographic vectors), and we obtain several tractability results for these cases. Interestingly, we show that maximin share allocations can always be found efficiently with Borda utilities, which cannot be guaranteed for general additive utilities. In the nonadditive setting, we show that there exists a problem instance for which there is no c-maximin share allocation, for any constant c. We explore a class of symmetric submodular utilities for which there exists a tight $$\frac{1}{2}$$ -maximin share allocation, and show how it can be approximated to within a factor of $$\nicefrac {1}{4}$$ .

The maximin share (MMS) guarantee is a desirable fairness notion for allocating indivisible goods. While MMS allocations do not always exist, several approximation techniques have been developed to ensure that all agents receive a fraction of their maximin share. We focus on an alternative approximation notion, based on the population of agents, that seeks to guarantee MMS for a fraction of agents. We show that no optimal approximation algorithm can satisfy more than a constant number of agents, and discuss the existence and computation of MMS for all but one agent and its relation to approximate MMS guarantees. We then prove the existence of allocations that guarantee MMS for 2/3 of agents, and devise a polynomial time algorithm that achieves this bound for up to nine agents. A key implication of our result is the existence of allocations that guarantee the value that an agent receives by partitioning the goods into 3n/2 bundles, improving the best known guarantee when goods are partitioned into 2n-2 bundles. Finally, we provide empirical experiments using synthetic data.