Anonymous and Copy-Robust Delegations for Liquid Democracy
Authors: Markus Utke, Ulrike Schmidt-Kraepelin
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our Contribution We show that the above impossibility is due to the restriction that delegation rules may not distribute the voting weight of a delegating voter to more than one casting voter. We generalize the definition of delegation rules to fractional delegation rules (Section 3) and provide generalizations of all three axioms above (Section 7). We introduce a natural variant of the BORDA BRANCHING rule [Brill et al., 2022], which we call MIXED BORDA BRANCHING. We show that this rule is equivalent to the RANDOM WALK RULE, a fractional delegation rule that has been suggested by Brill [2018]. In our main result, we build upon Fulkerson s algorithm [Fulkerson, 1974] and the Markov chain tree theorem [Leighton and Rivest, 1986] and show the existence of a polynomial-time algorithm for MIXED BORDA BRANCHING. |
| Researcher Affiliation | Academia | Markus Utke Department of Mathematics and Computer Science TU Eindhoven Eindhoven, The Netherlands m.utke@tue.nl Ulrike Schmidt-Kraepelin Simons Laufer Mathematical Sciences Institute (SLMath) Berkeley, CA, United States uschmidt@slmath.org Part of this research was carried out while both authors were affiliated with TU Berlin and Universidad de Chile, and Markus Utke was affiliated with University of Amsterdam. |
| Pseudocode | Yes | Algorithm 1 Fulkerson s Algorithm [Fulkerson, 1974, Kamiyama, 2014] Algorithm 2 Computation of MIXED BORDA BRANCHING |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| 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. The paper is theoretical in nature and does not involve empirical training on datasets. |
| 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. This is a theoretical paper and does not involve empirical validation on datasets. |
| 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. This is a theoretical paper. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers). It cites existing algorithms and theorems but does not list software dependencies for implementation. |
| Experiment Setup | No | The paper does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. This is a theoretical paper and does not involve empirical experiments requiring such details. |