Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Strategyproof Mechanisms for Friends and Enemies Games
Authors: Michele Flammini, Bojana Kodric, Giovanna Varricchio1950-1957
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We investigate strategyproof mechanisms for Friends and Enemies Games... We provide strategyproof mechanisms for both settings. More precisely, for FA we first present a deterministic napproximation mechanism, and then show that a much better result can be accomplished by resorting to randomization. Namely, we provide a randomized mechanism whose expected approximation ratio is 4, and arbitrarily close to 4 with high probability. For EA, we give a simple (1 + 2)napproximation mechanism, and show that its performance is asymptotically tight by proving that it is NP-hard to approximate the optimal solution within O(n1 ε) for any fixed ε > 0. |
| Researcher Affiliation | Academia | Michele Flammini, Bojana Kodric, Giovanna Varricchio Gran Sasso Science Institute, L Aquila, Italy EMAIL |
| Pseudocode | Yes | Mechanism M4. Given an EA preference profile d, M4 1. enumerates the agents in N from 1 up to n; 2. sets C = ; 3. for i = 1 up to n if there exists j > i in the neighborhood of i in G d not matched yet, then C = C {i, j}, otherwise, C = C {i}; 4. returns C. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the methodology is openly available. |
| Open Datasets | No | The paper focuses on theoretical mechanisms and their approximation ratios, and does not involve experimental training on datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper describes theoretical mechanisms and their analysis, and does not involve experimental procedures that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies or versions. |
| Experiment Setup | No | The paper is theoretical and does not include details on experimental setup or parameters. |