Fair Public Decision Making

By Vincent Conitzer, Rupert Freeman, Nisarg Shah. 2016

200 citations recorded by the source corpus.

We generalize the classic problem of fairly allocating indivisible goods to the problem of fair public decision making, in which a decision must be made on several social issues simultaneously, and, unlike the classic setting, a decision can provide positive utility to multiple players. We extend the popular fairness notion of proportionality (which is not guaranteeable) to our more general setting, and introduce three novel relaxations --- proportionality up to one issue, round robin share, and pessimistic proportional share --- that are also interesting in the classic goods allocation setting. We show that the Maximum Nash Welfare solution, which is known to satisfy appealing fairness properties in the classic setting, satisfies or approximates all three relaxations in our framework. We also provide polynomial time algorithms and hardness results for finding allocations satisfying these axioms, with or without insisting on Pareto optimality.

Subfields: Computational Theory and Mathematics, Economics and Econometrics, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Charles Lyu
2. Kamesh Munagala
3. Xingyu Chen
4. Brandon Fain

In Economics and Econometrics

1. Nisarg Shah
2. Kamesh Munagala
3. Brandon Fain

In Management Science and Operations Research

1. Nisarg Shah
2. Kamesh Munagala
3. Brandon Fain

Citations in the corpus

• Proportionally Fair Clustering (2019, background): “, 2016; 2018), similar to other group fairness concepts for public goods that explicitly consider shared resources (Conitzer et al., 2017; Aziz et al., 2017).”
• Fair Allocation of Indivisible Public Goods (2018, background): “[9] provide to individual agents! In Section 4, we consider matching constraints.”