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Fair Division and Collective Welfare

Hervé Moulin
TLDR
This book discusses three cardinal interpretations of collective welfare: Bentham's "utilitarian" proposal to maximize the sum of individual utilities, the Nash product, and the egalitarian leximin ordering.
Abstract
The concept of fair division is as old as civil society itself Aristotle's "equal treatment of equals" was the first step toward a formal definition of distributive fairness The concept of collective welfare, more than two centuries old, is a pillar of modern economic analysis Reflecting fifty years of research, this book examines the contribution of modern microeconomic thinking to distributive justice Taking the modern axiomatic approach, it compares normative arguments of distributive justice and their relation to efficiency and collective welfare The book begins with the epistemological status of the axiomatic approach and the four classic principles of distributive justice: compensation, reward, exogenous rights, and fitness It then presents the simple ideas of equal gains, equal losses, and proportional gains and losses The book discusses three cardinal interpretations of collective welfare: Bentham's "utilitarian" proposal to maximize the sum of individual utilities, the Nash product, and the egalitarian leximin ordering It also discusses the two main ordinal definitions of collective welfare: the majority relation and the Borda scoring method The Shapley value is the single most important contribution of game theory to distributive justice A formula to divide jointly produced costs or benefits fairly, it is especially useful when the pattern of externalities renders useless the simple ideas of equality and proportionality The book ends with two versatile methods for dividing commodities efficiently and fairly when only ordinal preferences matter: competitive equilibrium with equal incomes and egalitarian equivalence The book contains a wealth of empirical examples and exercises

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Citations
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Mesos: a platform for fine-grained resource sharing in the data center

TL;DR: The results show that Mesos can achieve near-optimal data locality when sharing the cluster among diverse frameworks, can scale to 50,000 (emulated) nodes, and is resilient to failures.
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Dominant resource fairness: fair allocation of multiple resource types

TL;DR: Dominant Resource Fairness (DRF), a generalization of max-min fairness to multiple resource types, is proposed, and it is shown that it leads to better throughput and fairness than the slot-based fair sharing schemes in current cluster schedulers.
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Issues in multiagent resource allocation

TL;DR: A survey of some of the most salient issues in Multiagent Resource Allocation, including various languages to represent the pref-erences of agents over alternative allocations of resources as well as different measures of social welfare to assess the overall quality of an allocation.
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The Unreasonable Fairness of Maximum Nash Welfare

TL;DR: It is proved that the maximum Nash welfare solution selects allocations that are envy free up to one good --- a compelling notion that is quite elusive when coupled with economic efficiency.
Proceedings ArticleDOI

The combinatorial assignment problem: approximate competitive equilibrium from equal incomes

TL;DR: A solution to the combinatorial assignment problem by proposing two new criteria of outcome fairness, the maximin share guarantee and envy bounded by a single good, which weaken well-known criteria to accommodate indivisibilities and formalize why dictatorships are unfair.
References
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Journal ArticleDOI

Procedurally fair and stable matching

TL;DR: An adjustment of therandom order mechanism is considered, the equitable random order mechanism, that combines aspects of procedural and “endstate” fairness.
Journal ArticleDOI

The Consensus Value: A New Solution Concept for Cooperative Games

TL;DR: In this paper, the authors developed a new solution concept for cooperative games: the consensus value, which is the unique function that satisfies efficiency, symmetry, the quasi dummy property and additivity.
Posted Content

Even allocations for generalised rationing problems

TL;DR: In this article, an extension of the standard rationing problem, consisting of the allocation of utility losses, is studied, where neither linearly transferable utilities nor risk averse agents are assumed.