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..
Complexity of Manipulating Sequential Allocation
Authors: Haris Aziz, Sylvain Bouveret, Jยrยme Lang, Simon Mackenzie
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that the problem is NP-complete for one manipulating agent with additive utilities and several nonmanipulating agents. In doing so, we correct a wrong claim made in a previous paper. We then give two additional results. First, we present a polynomial-time algorithm for optimal manipulation when the manipulator has additive binary utilities. Second, we consider a stronger notion of manipulation whereby the untruthful outcome yields more utility than the truthful outcome for all utilities consistent with the ordinal preferences; for this notion, we show that a manipulation, if any, can be computed in polynomial time. |
| Researcher Affiliation | Academia | Haris Aziz Data61, CSIRO and UNSW Sydney, Australia EMAIL Sylvain Bouveret LIG Grenoble INP France EMAIL J erหome Lang Universit e Paris-Dauphine, PSL Research University CNRS, LAMSADE, Paris, France EMAIL Simon Mackenzie Carnegie Mellon University, Pittsburg, USA EMAIL |
| Pseudocode | Yes | Consider the following algorithm BR: Input: O+, ฯ, ( 2, . . . , n) Output: 1 k 1; n1 number of occurrences of 1 in ฯ; While k n1 and O+ = and ฯ = 11 . . . 1 O Allocate(head(ฯ), 2, . . . , n); remove O from O, O+, and ( 2, . . . , n); ฯ tail(ฯ); ak First(ฯ 1, 2, . . . , n, O+); remove ak from O, O+, and ( 2, . . . , n); k k + 1; End While 1 (a1, . . . , ak); complete 1 with the remaining items of O+ (if any) in an arbitrary way, and then by other items in an arbitrary way; Return 1 |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper focusing on complexity proofs and algorithms, and it does not involve the use of datasets for training or evaluation. Therefore, there is no information about dataset availability. |
| Dataset Splits | No | This is a theoretical paper and does not involve experimental validation with dataset splits. |
| Hardware Specification | No | This is a theoretical paper and does not describe empirical experiments that would require specific hardware specifications. Therefore, no hardware details are mentioned. |
| Software Dependencies | No | This is a theoretical paper and does not discuss software dependencies with version numbers for reproducibility of experiments, as it does not conduct empirical studies. |
| Experiment Setup | No | This is a theoretical paper and does not describe empirical experiments or their setup, such as hyperparameters or training configurations. |