A Characterization of Voting Power for Discrete Weight Distributions
Authors: Yoram Bachrach, Yuval Filmus, Joel Oren, Yair Zick
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | A Characterization of Voting Power for Discrete Weight Distributions. We focus on a model where the agent weights originate from a stochastic process, resulting in weight uncertainty. We analyze the expected effect of the quota on voting power given the weight generating process. We examine two extreme cases of the balls and bins model: uniform and exponentially decaying probabilities. We show that the choice of a quota may have a large influence on the power disparity of the agents, even when the governing distribution is likely to result in highly similar weights for the agents. We characterize various interesting repetitive fluctuation patterns in agents power as a function of the quota. Our work represents a significant advance in our understanding of weighted voting games; via careful probabilistic analysis, we strongly generalize previous known results, and inform the design of both real and randomly generated voting systems. |
| Researcher Affiliation | Collaboration | Yoram Bachrach Microsoft Research, UK yobach@microsoft.com Yuval Filmus Technion, Israel yuvalfi@cs.technion.ac.il Joel Oren Univ. of Toronto, Canada oren@cs.toronto.edu Yair Zick Carnegie Mellon Univ., USA yairzick@cs.cmu.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the described methodology. No links or explicit statements about code release are found. |
| Open Datasets | No | The paper uses theoretical models and simulations (e.g., 'weights were drawn from a balls and bins distribution') rather than traditional public datasets for training. There is no mention of a public dataset or access information for one. |
| Dataset Splits | No | The paper does not describe dataset splits for training, validation, or testing, as it focuses on theoretical analysis and simulation rather than empirical evaluation on a traditional dataset. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running its analyses or simulations. |
| Software Dependencies | No | The paper does not provide any specific ancillary software details with version numbers (e.g., libraries, solvers, programming languages beyond general mentions). |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values or training configurations. While it mentions parameters for the Balls and Bins model (e.g., 'm = 10,000 balls'), these are part of the theoretical model illustration, not experimental training setup in the context of typical machine learning or empirical studies. |