150 research outputs found
Almost Envy-Free Allocations of Indivisible Goods or Chores with Entitlements
We here address the problem of fairly allocating indivisible goods or chores
to agents with weights that define their entitlement to the set of
indivisible resources. Stemming from well-studied fairness concepts such as
envy-freeness up to one good (EF1) and envy-freeness up to any good (EFX) for
agents with equal entitlements, we present, in this study, the first set of
impossibility results alongside algorithmic guarantees for fairness among
agents with unequal entitlements.
Within this paper, we expand the concept of envy-freeness up to any good or
chore to the weighted context (WEFX and XWEF respectively), demonstrating that
these allocations are not guaranteed to exist for two or three agents. Despite
these negative results, we develop a WEFX procedure for two agents with integer
weights, and furthermore, we devise an approximate WEFX procedure for two
agents with normalized weights. We further present a polynomial-time algorithm
that guarantees a weighted envy-free allocation up to one chore (1WEF) for any
number of agents with additive cost functions. Our work underscores the
heightened complexity of the weighted fair division problem when compared to
its unweighted counterpart.Comment: Appears in the 38th AAAI Conference on Artificial Intelligence
(AAAI), 202
Fair Allocation of goods and chores -- Tutorial and Survey of Recent Results
Fair resource allocation is an important problem in many real-world
scenarios, where resources such as goods and chores must be allocated among
agents. In this survey, we delve into the intricacies of fair allocation,
focusing specifically on the challenges associated with indivisible resources.
We define fairness and efficiency within this context and thoroughly survey
existential results, algorithms, and approximations that satisfy various
fairness criteria, including envyfreeness, proportionality, MMS, and their
relaxations. Additionally, we discuss algorithms that achieve fairness and
efficiency, such as Pareto Optimality and Utilitarian Welfare. We also study
the computational complexity of these algorithms, the likelihood of finding
fair allocations, and the price of fairness for each fairness notion. We also
cover mixed instances of indivisible and divisible items and investigate
different valuation and allocation settings. By summarizing the
state-of-the-art research, this survey provides valuable insights into fair
resource allocation of indivisible goods and chores, highlighting computational
complexities, fairness guarantees, and trade-offs between fairness and
efficiency. It serves as a foundation for future advancements in this vital
field
Fair Division of a Graph
We consider fair allocation of indivisible items under an additional
constraint: there is an undirected graph describing the relationship between
the items, and each agent's share must form a connected subgraph of this graph.
This framework captures, e.g., fair allocation of land plots, where the graph
describes the accessibility relation among the plots. We focus on agents that
have additive utilities for the items, and consider several common fair
division solution concepts, such as proportionality, envy-freeness and maximin
share guarantee. While finding good allocations according to these solution
concepts is computationally hard in general, we design efficient algorithms for
special cases where the underlying graph has simple structure, and/or the
number of agents -or, less restrictively, the number of agent types- is small.
In particular, despite non-existence results in the general case, we prove that
for acyclic graphs a maximin share allocation always exists and can be found
efficiently.Comment: 9 pages, long version of accepted IJCAI-17 pape
Fair and Efficient Allocations under Subadditive Valuations
We study the problem of allocating a set of indivisible goods among agents
with subadditive valuations in a fair and efficient manner. Envy-Freeness up to
any good (EFX) is the most compelling notion of fairness in the context of
indivisible goods. Although the existence of EFX is not known beyond the simple
case of two agents with subadditive valuations, some good approximations of EFX
are known to exist, namely -EFX allocation and EFX allocations
with bounded charity.
Nash welfare (the geometric mean of agents' valuations) is one of the most
commonly used measures of efficiency. In case of additive valuations, an
allocation that maximizes Nash welfare also satisfies fairness properties like
Envy-Free up to one good (EF1). Although there is substantial work on
approximating Nash welfare when agents have additive valuations, very little is
known when agents have subadditive valuations. In this paper, we design a
polynomial-time algorithm that outputs an allocation that satisfies either of
the two approximations of EFX as well as achieves an
approximation to the Nash welfare. Our result also improves the current
best-known approximation of and to
Nash welfare when agents have submodular and subadditive valuations,
respectively.
Furthermore, our technique also gives an approximation to a
family of welfare measures, -mean of valuations for ,
thereby also matching asymptotically the current best known approximation ratio
for special cases like while also retaining the fairness
properties
Minimizing and balancing envy among agents using Ordered Weighted Average
International audienceIn the problem of fair resource allocation, envy freeness is one of the most interesting fairness criterion as it ensures that no agent prefers the bundle of another agent. However, when considering indivisible goods, an envy-free allocation may not exist. In this paper, we investigate a new relaxation of envy freeness consisting in minimizing the Ordered Weighted Average (OWA) of the envy vector. The idea is to choose the allocation that is fair in the sense of the distribution of the envy among agents. The OWA aggregator is a well-known tool to express fairness in multiagent optimization. In this paper, we focus on fair OWA operators where the weights of the OWA are decreasing. When an envy-free allocation exists, minimizing OWA will return this allocation. However, when no envy-free allocation exists, one may wonder how fair min OWA allocations are. After some definitions and description of the model, we show how to formulate the computation of such a min OWA allocation as a Mixed Integer Program. Then, we investigate the link between the min OWA allocation and other well-known fairness measures such as max min share and envy freeness up to one good or to any good
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