On Fair and Efficient Allocations of Indivisible Goods

Authors: Aniket Murhekar, Jugal Garg5595-5602

AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We present a pseudo-polynomial time algorithm to compute an EF1+f PO allocation, thereby improving the earlier results. Our techniques also enable us to show that an EQ1+f PO allocation always exists when the values are positive, and that it can be computed in pseudo-polynomial time. We also consider the class of k-ary instances where k is a constant, i.e., each agent has at most k different values for the goods. We show that for such instances an EF1+f PO allocation can be computed in polynomial time. When all values are positive, we show that an EQ1+f PO allocation for such instances can be computed in polynomial time. Next, we consider instances where the number of agents is constant, and show that an EF1+PO (also EQ1+PO) allocation can be computed in polynomial time. These results significantly extend the polynomial-time computability beyond the known cases of binary or identical valuations. Further, we show that the problem of computing an EF1+PO allocation polynomial-time reduces to a problem in the complexity class PLS. We also design a polynomial-time algorithm that computes Nash welfare maximizing allocations when there are constantly many agents with constant many different values for the goods.
Researcher Affiliation Academia Aniket Murhekar, Jugal Garg University of Illinois, Urbana-Champaign {aniket2, jugal}@illinois.edu
Pseudocode Yes Algorithm 1 Computing an EF1+f PO allocation of goods; Algorithm 2 Computing an EQ1+f PO allocation of goods
Open Source Code No The paper does not provide any specific links or statements about releasing the source code.
Open Datasets No The paper is theoretical and focuses on algorithms and complexity analysis. It does not use datasets for training or evaluation.
Dataset Splits No The paper is theoretical and focuses on algorithms and complexity analysis. It does not use datasets or specify data splits for validation.
Hardware Specification No The paper is theoretical and does not describe running experiments; therefore, it does not specify any hardware used.
Software Dependencies No The paper is theoretical and does not describe running experiments; therefore, it does not list any software dependencies with specific version numbers.
Experiment Setup No The paper is theoretical and focuses on algorithm design and analysis. It does not describe any experimental setup details such as hyperparameters or training configurations.