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..
Approximate Inference of Outcomes in Probabilistic Elections
Authors: Batya Kenig, Benny Kimelfeld2061-2068
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the complexity of estimating the probability of an outcome in an election over probabilistic votes... The main contribution of this paper is a multiplicative FPRAS for the probability of losing. We do so for a large class of positional scoring rules... To this end, we adapt the Karp-Luby-Madras approximation algorithm (Karp, Luby, and Madras 1989), and the main theoretical challenges we face is in establishing (provable approximations of) the requirements that this algorithm makes on a specific use case. To the best of our knowledge, this paper presents the first multiplicative approximation algorithm in the context of elections over probabilistic voters. |
| Researcher Affiliation | Academia | Batya Kenig Paul G. Allen School of Computer Science and Engineering University of Washington EMAIL, Benny Kimelfeld Technion Israel Institute of Technology Haifa 3200003, Israel EMAIL |
| Pseudocode | Yes | Figure 1: Sampling conditioned on L(x, c) Algorithm sample(P = { 1, . . . , n},L(x, c)) |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing code for the work described, nor does it provide a direct link to a source-code repository. |
| Open Datasets | No | This paper is theoretical and does not conduct experiments with empirical datasets, therefore, it does not provide concrete access information for a publicly available or open dataset used for training. |
| Dataset Splits | No | This paper is theoretical and does not conduct experiments with dataset splits, therefore, it does not provide specific dataset split information. |
| Hardware Specification | No | This paper is theoretical and does not describe the hardware used for experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers, as it is a theoretical paper and does not discuss specific implementation environments. |
| Experiment Setup | No | This paper is theoretical and does not describe experimental setup details such as hyperparameters or training configurations. |