A Calculus for Computing Structured Justifications for Election Outcomes

Authors: Arthur Boixel, Ulle Endriss, Ronald de Haan4859-4866

AAAI 2022 | Conference PDF | Archive PDF | Plain Text | 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 {a.boixel, u.endriss, r.dehaan}@uva.nl
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.