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..
A Calculus for Computing Structured Justifications for Election Outcomes
Authors: Arthur Boixel, Ulle Endriss, Ronald de Haan4859-4866
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In the context of social choice theory, we develop a tableaubased calculus for reasoning about voting rules. This calculus can be used to obtain structured explanations for why a given set of axioms justifies a given election outcome for a given profile of voter preferences. We then show how to operationalise this calculus, using a combination of SAT solving and Answer Set Programming, to arrive at a flexible framework for presenting human-readable justifications to users. |
| Researcher Affiliation | Academia | Institute for Logic, Language and Computation (ILLC), University of Amsterdam EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code. The code used to operationalise the calculus is available online (Boixel, Endriss, and De Haan 2021). |
| Open Datasets | No | The paper discusses a theoretical calculus and uses a small, illustrative example (Example 1) rather than a publicly available dataset for training or evaluation. No information regarding dataset access is provided. |
| Dataset Splits | No | The paper does not involve empirical experiments with datasets that would require training, validation, or test splits. The work is theoretical in nature. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running its experiments or operationalizing the calculus. |
| Software Dependencies | No | For our implementation (Boixel, Endriss, and De Haan 2021) we used clingo as ASP grounder/solver (Gebser et al. 2008). While a software name is mentioned, a specific version number for clingo or any other software dependency is not provided. |
| Experiment Setup | No | The paper focuses on a theoretical calculus and its operationalization; it does not describe experimental setups with hyperparameters, training configurations, or system-level settings typically found in empirical studies. |