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..
Heuristic Voting as Ordinal Dominance Strategies
Authors: Omer Lev, Reshef Meir, Svetlana Obraztsova, Maria Polukarov2077-2084
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | To this end, we present a framework that allows for shades of gray of likelihood without probabilities. Specifically, we create a hierarchy of sets of world states based on a prospective poll, with inner sets contain more likely outcomes. This hierarchy of likelihoods allows us to define what we term ordinally-dominated strategies. We use this approach to justify various known voting heuristics as bounded-rational strategies. |
| Researcher Affiliation | Academia | Omer Lev Ben-Gurion University Beersheba, Israel EMAIL Reshef Meir Technion Haifa, Israel EMAIL Svetlana Obraztsova Nanyang Technological University Singapore EMAIL Maria Polukarov King s College London London, United Kingdom EMAIL |
| Pseudocode | Yes | Algorithm 1: OD(a i, ai A, i, Hi H ) |
| Open Source Code | No | The paper does not provide any link or explicit statement about the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper discusses theoretical examples (e.g., Example 1, Example 2, Example 5) but does not use any publicly available or open datasets for training or evaluation. |
| Dataset Splits | No | The paper does not use any datasets for validation, as it is a theoretical paper providing mathematical proofs and justifications. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to conduct research or experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not detail any experimental setup, hyperparameters, or training configurations. |