Efficient Algorithms for Electing Successive Committees
Authors: Pallavi Jain, Andrzej Kaczmarczyk
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Motivated by a desire to unlock the full potential of the described temporal model of committee elections, we devise (parameterized) algorithms that effectively solve the mentioned hard cases in realistic scenarios of a moderate number of candidates or of a limited time horizon. We analyze the same set of rules they did and contribute several positive algorithmic results, as shown in Table 1. |
| Researcher Affiliation | Academia | 1 Indian Institute of Technology, Jodhpur, India 2 Department of Computer Science, The University of Chicago, Chicago, United States EMAIL, EMAIL |
| Pseudocode | Yes | Proof. Let us define a Boolean function F(A, j, s, G), which returns true if among candidates A, there is a series achieving quality at least s and consisting of j committees, each of size 2 such that in the j-th committee candidates from G A appear for the first time in the series; F returns false otherwise. Our algorithm computes the values of F applying the DP approach to the following recursive formula. Denoting by W(A, G, k) a collection of all size-k sets W such that G W A we get: f(A,j, s, G) = _ W W(A,G,k) f(A \ G, j 1, s sc(W), W \ G). |
| Open Source Code | No | The paper does not contain any explicit statements about releasing code, nor does it provide a link to a code repository. It mentions a full version of the paper will be published, but not related code. |
| Open Datasets | No | The paper focuses on theoretical algorithms for committee elections and does not describe or use any specific publicly available datasets for experimental evaluation. It defines an election E = (C, V) with candidates and voters, but this is a theoretical construct, not a specific dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments using datasets, therefore, there is no mention of dataset splits for training, testing, or validation. |
| Hardware Specification | No | The paper is purely theoretical, focusing on algorithmic complexity and proofs. It does not describe any experimental setup or mention specific hardware used for computations. |
| Software Dependencies | No | The paper presents theoretical algorithms and complexity analysis. It does not mention any specific software components or their version numbers that would be required to reproduce experimental results. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and complexity analysis. It does not describe any experiments, and therefore, no experimental setup details, hyperparameters, or training configurations are provided. |