Subset Selection via Implicit Utilitarian Voting
Authors: Ioannis Caragiannis, Swaprava Nath, Ariel D. Procaccia, Nisarg Shah
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results show that regret-based rules are more compelling than distortion-based rules, leading us to focus on developing a scalable implementation for the optimal (deterministic) regret-based rule. Our methods underlie the design and implementation of an upcoming social choice website. In this section, we evaluate their average-case performance on simulated as well as real data, and compare them against nine well-known voting rules: |
| Researcher Affiliation | Academia | Ioannis Caragiannis University of Patras, Greece caragian@ceid.upatras.gr Swaprava Nath Carnegie Mellon University, USA swapravn@cs.cmu.edu Ariel D. Procaccia Carnegie Mellon University, USA arielpro@cs.cmu.edu Nisarg Shah Carnegie Mellon University, USA nkshah@cs.cmu.edu |
| Pseudocode | No | The paper describes computational approaches and equations but does not present any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions that they 'have implemented the deterministic regret minimization rule on Robo Vote' and that 'Robo Vote (www.robovote.org), which is scheduled to launch in May 2016'. However, it does not provide a direct link to the source code for the methodology described in the paper. |
| Open Datasets | Yes | We perform three experiments: (i) choosing a utility profile uniformly at random from the simplex of all utility profiles, (ii) drawing a real-world utility profile from the Jester datasets [Goldberg et al., 2001], and (iii) drawing a realworld preference profile from the Pref Lib datasets [Mattei and Walsh, 2013], and choosing a consistent utility profile uniformly at random. |
| Dataset Splits | No | The paper mentions drawing data from datasets and performing simulations ('10 000 random simulations'), but it does not specify any train/validation/test splits of the data for reproducibility purposes. |
| Hardware Specification | Yes | The experiments were performed on a single machine with quad-core 2.9 GHz CPU and 32 GB RAM. |
| Software Dependencies | No | The paper mentions using 'the SFO toolbox for Matlab [Krause, 2010]' and solving Integer Linear Programs (ILP) but does not provide specific version numbers for Matlab, the SFO toolbox, or any ILP solver used, which are necessary for reproducible software dependencies. |
| Experiment Setup | Yes | For each experiment, we have 8 voters and 10 alternatives, and test for k 2 [4]. For each setting, we perform 10 000 random simulations, and measure both distortion and regret for the actual utility profile, as opposed to the worst-case utility profile. A time limit of 2 minutes was set because a running time greater than this would not be helpful for our website, where the results need to be delivered quickly to the users. |