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 [1].
On the Indecisiveness of Kelly-Strategyproof Social Choice Functions
Authors: Felix Brandt, Martin Bullinger, Patrick Lederer
JAIR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we show that, for weak preferences, only indecisive SCFs can satisfy strategyproofness. In particular, (i) every strategyproof rank-based SCF violates Pareto-optimality, (ii) every strategyproof support-based SCF (which generalize Fishburn s C2 SCFs) that satisfies Pareto-optimality returns at least one most preferred alternative of every voter, and (iii) every strategyproof non-imposing SCF returns the Condorcet loser in at least one profile. We also discuss the consequences of these results for randomized social choice. The paper contains numerous sections detailing theorems, lemmas, and their proofs. |
| Researcher Affiliation | Academia | Felix Brandt EMAIL Martin Bullinger EMAIL Patrick Lederer EMAIL Institut für Informatik Technische Universität München Boltzmannstr. 3, 85748 Garching, Germany |
| Pseudocode | No | The paper contains detailed mathematical proofs, definitions, and discussions of social choice theory concepts, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper focuses on theoretical results in social choice theory. It does not provide any statements about open-source code availability, links to code repositories, or code in supplementary materials for the methodology described. |
| Open Datasets | No | This paper is a theoretical work that develops mathematical results and proofs concerning social choice functions and preference aggregation. It does not involve or refer to any empirical datasets. |
| Dataset Splits | No | This paper is theoretical and does not involve experimental datasets. Therefore, it does not discuss or provide any dataset splits (e.g., training, validation, or test splits). |
| Hardware Specification | No | The paper presents theoretical proofs and mathematical results; it does not describe any experiments that would require specific hardware. Thus, no hardware specifications are mentioned. |
| Software Dependencies | No | As a theoretical paper focusing on mathematical proofs and definitions in social choice theory, it does not describe any software implementations or dependencies with specific version numbers. |
| Experiment Setup | No | The paper is entirely theoretical, presenting mathematical theorems and proofs. Consequently, there is no description of an experimental setup, hyperparameters, or training configurations. |