Goal-Based Collective Decisions: Axiomatics and Computational Complexity

Authors: Arianna Novaro, Umberto Grandi, Dominique Longin, Emiliano Lorini

IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We study agents expressing propositional goals over a set of binary issues to reach a collective decision. We adapt properties and rules from the literature on Social Choice Theory to our setting, providing an axiomatic characterisation of a majority rule for goal-based voting. We study the computational complexity of finding the outcome of our rules (i.e., winner determination), showing that it ranges from Nondeterministic Polynomial Time (NP) to Probabilistic Polynomial Time (PP).
Researcher Affiliation Academia Arianna Novaro1, Umberto Grandi1, Dominique Longin2, Emiliano Lorini2 1 IRIT, University of Toulouse 2 IRIT, CNRS, Toulouse
Pseudocode No The paper does not contain structured pseudocode or algorithm blocks.
Open Source Code No The paper does not provide concrete access to source code for the methodology described, nor does it explicitly state that code is being released.
Open Datasets No This paper is theoretical and does not involve empirical experiments with datasets, therefore no training dataset information is provided.
Dataset Splits No This paper is theoretical and does not involve empirical experiments with datasets, therefore no validation dataset information is provided.
Hardware Specification No This paper is theoretical and does not involve empirical experiments, therefore no specific hardware details are provided.
Software Dependencies No This paper is theoretical and does not involve empirical experiments, therefore no specific ancillary software details with version numbers are provided.
Experiment Setup No This paper is theoretical and does not involve empirical experiments, therefore no specific experimental setup details are provided.