Group Decision Making via Probabilistic Belief Merging

Authors: Nico Potyka, Erman Acar, Matthias Thimm, Heiner Stuckenschmidt

IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We propose a probabilistic-logical framework for group decision-making. All proofs have been moved to an online appendix1 to meet space restrictions. Even though we could not find a proof so far, there is some empirical evidence that the following conjecture is true.
Researcher Affiliation Academia Nico Potyka Univ. of Osnabr uck, Erman Acar Univ. of Mannheim, Matthias Thimm Univ. of Koblenz, Heiner Stuckenschmidt Univ. of Mannheim
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 (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper.
Open Datasets No The paper does not provide concrete access information (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset. It uses an illustrative 'Example 1' with made-up data.
Dataset Splits No The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce data partitioning.
Hardware Specification No The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments.
Software Dependencies No The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment.
Experiment Setup Yes When applying p-norms other than the maximum norm, intuition suggests that the group beliefs will converge to the majority beliefs. If we employ the Manhattan norm, we have that Bi(C(a)) = [i 1, i 1] for i 2 N and that BG(C(a)) = [0, 1].