Flexible Representative Democracy: An Introduction with Binary Issues
Authors: Ben Abramowitz, Nicholas Mattei
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We find through theoretical and empirical analysis that FRD can yield significant improvements over RD for emulating DD with full participation. |
| Researcher Affiliation | Academia | 1Rensselaer Polytechnic Institute, Troy, NY, USA 2Tulane University, New Orleans, LA, USA |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper generates data for its simulations rather than using a pre-existing publicly available dataset with concrete access information. For instance, it states: 'In all our simulations, for all issues si we let vi j = 1 and ci l = 1 with probability 1 2 for all voters and candidates. This means that all candidates and voters come from the same populations, i.e., that they are both drawn from the same distribution'. |
| Dataset Splits | No | The paper does not specify exact training, validation, and test splits; it describes the process of generating synthetic data for simulations and running iterations. |
| Hardware Specification | Yes | We implemented these rules in Gurobi 8.1 and used a server with 16 cores and 32GB of memory |
| Software Dependencies | Yes | We implemented these rules in Gurobi 8.1 |
| Experiment Setup | Yes | For all simulations we perform 50 iterations at each datapoint and plot the mean (σ2 0.002). For our simulated delegations we create instances with |V| = 301, |C| = 60, |S| = 150, and k = 21. We vary α {0, 1.0} in increments of 0.01 and for each setting of α we run 50 iterations. |