Fair and Efficient Resource Allocation with Partial Information

Halpern, Daniel; Shah, Nisarg

doi:10.24963/ijcai.2021/32

Cited by 14 publications

(9 citation statements)

References 1 publication

(9 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is easy to see that α-balancedness implies α-proportionality. 2 Balancedness and similar cardinality constraints have been investigated previously in various fair division contexts (Leroux and Leroux 2004;Biswas and Barman 2018;Bei et al 2021b;Halpern and Shah 2021); in our case, note that 1-balancedness is equivalent to envy-freeness.…”

Section: Introductionmentioning

confidence: 53%

A Little Charity Guarantees Fair Connected Graph Partitioning

Caragiannis

Micha

Shah

2022

AAAI

Self Cite

View full text Add to dashboard Cite

Motivated by fair division applications, we study a fair connected graph partitioning problem, in which an undirected graph with m nodes must be divided between n agents such that each agent receives a connected subgraph and the partition is fair. We study approximate versions of two fairness criteria: \alpha-proportionality requires that each agent receive a subgraph with at least (1/\alpha)*m/n nodes, and \alpha-balancedness requires that the ratio between the sizes of the largest and smallest subgraphs be at most \alpha. Unfortunately, there exist simple examples in which no partition is reasonably proportional or balanced. To circumvent this, we introduce the idea of charity. We show that by "donating" just n-1 nodes, we can guarantee the existence of 2-proportional and almost 2-balanced partitions (and find them in polynomial time), and that this result is almost tight. More generally, we chart the tradeoff between the size of charity and the approximation of proportionality or balancedness we can guarantee.

show abstract

Section: Introductionmentioning

confidence: 53%

A Little Charity Guarantees Fair Connected Graph Partitioning

Caragiannis

Micha

Shah

2022

AAAI

Self Cite

View full text Add to dashboard Cite

show abstract

“…managed to close a gap on the price of EF1 that was le open in the work of Bei et al [2021], and also showed tight bounds for other fairness notions, in particular, 1/2-MMS and Prop1. Halpern and Shah [2021] showed tight bounds on the Price of EF1 and of approximate MMS under the constraint of having only ordinal information about the agent values, a typical assumption made in the context of distortion in social choice [Anshelevich et al, 2021].…”

Section: Fairness and Efficiencymentioning

confidence: 99%

Fair Division of Indivisible Goods: A Survey

Amanatidis¹,

Birmpas²,

Filos-Ratsikas³

et al. 2022

Preprint

View full text Add to dashboard Cite

Allocating resources to individuals in a fair manner has been a topic of interest since the ancient times, with most of the early rigorous mathematical work on the problem focusing on infinitely divisible resources. Recently, there has been a surge of papers studying computational questions regarding various different notions of fairness for the indivisible case, like maximin share fairness (MMS) and envy-freeness up to any good (EFX). We survey the most important results in the discrete fair division literature, focusing on the case of additive valuation functions and paying particular a ention to the progress made in the last 10 years. * is work is partially supported by the ERC Advanced Grant 788893 AMDROMA "Algorithmic and Mechanism Design Research in Online Markets", the MIUR PRIN project ALGADIMAR "Algorithms, Games, and Digital Markets", and the NWO Veni project No. VI.Veni.192.153.

show abstract

“…Furthermore, showed tight bounds for other fairness notions, in particular, for 1/2-MMS and Prop1. Halpern and Shah [2021] showed tight bounds on the price of EF1 and of approximate MMS under the constraint of having only ordinal information about the agent values, a typical assumption made in the context of distortion in social choice [Anshelevich et al, 2021].…”

Section: Opt(i) Sw(a)mentioning

confidence: 99%

“…Let H n = Θ(ln n) be the n-th harmonic number. Halpern and Shah [2021] showed that with only ordinal information, it is impossible to achieve be er than 1/H n -MMS, while in previous work, Amanatidis et al [2016] showed that 1/2H n -MMS allocations can be computed. Hosseini et al [2021] showed the existence of Pareto optimal and MMS or EFX allocations when agents have lexicographic preferences.…”

Section: Limited Informationmentioning

confidence: 99%

Fair Division of Indivisible Goods: A Survey

Amanatidis¹,

Birmpas²,

Filos-Ratsikas³

et al. 2022

Preprint

View full text Add to dashboard Cite

Allocating resources to individuals in a fair manner has been a topic of interest since ancient times, with most of the early mathematical work on the problem focusing on resources that are infinitely divisible. Over the last decade, there has been a surge of papers studying computational questions regarding the indivisible case, for which exact fairness notions such as envy-freeness and proportionality are hard to satisfy. One main theme in the recent research agenda is to investigate the extent to which their relaxations, like maximin share fairness (MMS) and envy-freeness up to any good (EFX), can be achieved. In this survey, we present a comprehensive review of the progress made in the related literature by highlighting different ways to relax fairness notions, common algorithm design techniques, and the most interesting questions for future research.

show abstract

Fair and Efficient Resource Allocation with Partial Information

Cited by 14 publications

References 1 publication

A Little Charity Guarantees Fair Connected Graph Partitioning

A Little Charity Guarantees Fair Connected Graph Partitioning

Fair Division of Indivisible Goods: A Survey

Fair Division of Indivisible Goods: A Survey

Contact Info

Product

Resources

About