Representative Proxy Voting
Authors: Elliot Anshelevich, Zack Fitzsimmons, Rohit Vaish, Lirong Xia5086-5093
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main contributions are as follows (see also Table 1): We introduce a new model of proxy voting and measure for the representation of voter preferences. We consider both the unrestricted case where the proxies can be placed anywhere, and the restricted case where the proxies can only be placed at the candidate locations.1 We first provide algorithms for computing an optimal θrepresentative proxy arrangement (i.e., one that uses a minimum number of proxies) in polynomial time (Theorems 1 and 3). However, these algorithms do not provide much insight into how many proxies are actually necessary in order to be θ-representative, for any possible set of candidates and voters. Because of this, we also prove upper and lower bounds on the number of proxies needed to have a θ-representative proxy arrangement (see Table 1). These bounds show that a relatively small number of proxies is enough to capture the preferences of any set of voters, even in the worst case. Our results also address the dual problem of minimizing the distance threshold θ for a given number of proxies. For example, we prove that for the unrestricted case, one can use a given budget of k proxies to compute a θrepresentative arrangement with θ 3 k (Corollary 1). We observe that the proxy arrangements determined by our algorithms are not only θ-representative with respect to the voters, but for elections using strict-Condorcet voting rules, the direct election outcome (i.e., without proxies) is within θ of the proxy-voting outcome (Proposition 3). |
| Researcher Affiliation | Academia | 1 Rensselaer Polytechnic Institute, Troy, New York, USA 12180 2 College of the Holy Cross, Worcester, Massachusetts, USA 01610 3 Tata Institute of Fundamental Research, Mumbai, India 400005 |
| Pseudocode | No | The paper describes algorithms (e.g., dynamic programming approach in proof sketch of Theorem 3) but does not present them in a formal pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any specific link or statement about open-sourcing the code for the described methodology. It refers to an arXiv preprint for a full version (Anshelevich et al. 2020), but this is not an explicit code release statement. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and proofs rather than empirical training on datasets. There is no mention of datasets used for training. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments with validation splits. No information on dataset splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithms and proofs, not on practical implementation details or experimental setups requiring specific software versions. No software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper is theoretical and describes algorithms and proofs, not empirical experiments with a specific setup or hyperparameters. No experimental setup details are provided. |