
Group Fairness for Knapsack Problems
We study the knapsack problem with group fairness constraints. The input...
read it

Generic Preferences over Subsets of Structured Objects
Various tasks in decision making and decision support systems require se...
read it

Participatory Budgeting with Cumulative Votes
In participatory budgeting we are given a set of projects—each with a co...
read it

Obtaining a Proportional Allocation by Deleting Items
We consider the following control problem on fair allocation of indivisi...
read it

Markov Determinantal Point Processes
A determinantal point process (DPP) is a random process useful for model...
read it

A CondorcetConsistent Participatory Budgeting Algorithm
The budget is the key means for effecting policy in democracies, yet its...
read it

Predicting Human Card Selection in Magic: The Gathering with Contextual Preference Ranking
Drafting, i.e., the selection of a subset of items from a larger candida...
read it
Fair Knapsack
We study the following multiagent variant of the knapsack problem. We are given a set of items, a set of voters, and a value of the budget; each item is endowed with a cost and each voter assigns to each item a certain value. The goal is to select a subset of items with the total cost not exceeding the budget, in a way that is consistent with the voters' preferences. Since the preferences of the voters over the items can vary significantly, we need a way of aggregating these preferences, in order to select the socially most preferred valid knapsack. We study three approaches to aggregating voters preferences, which are motivated by the literature on multiwinner elections and fair allocation. This way we introduce the concepts of individually best, diverse, and fair knapsack. We study computational complexity (including parameterized complexity, and complexity under restricted domains) of computing the aforementioned concepts of multiagent knapsacks.
READ FULL TEXT
Comments
There are no comments yet.