Truthful and Fair Mechanisms for Matroid-Rank Valuations
Authors: Siddharth Barman, Paritosh Verma4801-4808
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | For establishing our results, we develop a characterization of truthful mechanisms for matroid-rank functions. This characterization in fact holds for a broader class of valuations (specifically, holds for binary XOS functions) and might be of independent interest. The paper includes theorems and proofs, such as 'Theorem 1', 'Theorem 2', 'Theorem 3', 'Lemma 1', 'Lemma 2', and corresponding proof sections. |
| Researcher Affiliation | Academia | Siddharth Barman,1 Paritosh Verma,2 1 Indian Institute of Science 2 Purdue University barman@iisc.ac.in, paritoshverma97@gmail.com |
| Pseudocode | Yes | Mechanism 1: Prioritized Egalitarian (PE) (Babaioff, Ezra, and Feige 2021) Input: Valuation profile (v1, v2, . . . , vn) consisting of the reported (matroid-rank) valuations of all the agents. Output: A non-wasteful Lorenz dominating allocation A = (A1, A2, . . . , An). 1: For the given profile (v1, v2, . . . , vn), compute a non-wasteful Lorenz dominating allocation A = (A1, A2, . . . , An), breaking ties in favor of agents with lower indices. Equivalently, among all (nonwasteful) Lorenz dominating allocations, select one, (A1, A2, . . . , An), that lexicographically maximizes the vector (v1(A1), v2(A2), . . . , vn(An)) (i.e., the Lorenz dominating allocation maximizes v1(A1), and then subject to that it maximizes v2(A2), and so on). 2: return A = (A1, A2, . . . , An) |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. It only references a previously published paper on arXiv. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments involving datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for training or validation. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |