
## Metric-Distortion Bounds under Limited Information
Ioannis Anagnostides, Dimitris Fotakis, Panagiotis Patsilinakos
Keywords: 
JAIR/2022/Proceedings/13338 - Metric-Distortion Bounds under Limited Information.pdf
Project URL: 

### Implementation
_Given the documentation given by the authors on the method, how much time investment would it be to re-implement the method from scratch?_

[3]

The authors provide a link to their implementation in footnote 1 (https://github.com/ioannisAnagno/Voting-MetricDistortion). The readme is empty. Code has few comments. No installation instructions. 

### Data
_Given the data description in the documentation, how much effort take to either: Find the same dataset the authors used, or similar datasets and defend the comparability, or acquire one from scratch?_

[2]

(1/1)

The authors use a dataset from Kaggle regarding the Eurovision Song Contest Scores from 1975-2019, and focus on subset of 2004-2008. A link is provided and description is given. Some statistics are given in table 1 but more would be welcome.

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for their results, and compare against their budgetary constraints?_

[1]

The authors method is, based on algorithm 1, parameter free.

### Experimental Procedure
_Given the experimental set-up of the work, how difficult is it to set up a new experiment, similar to those presented in the original work, with the same procedure?_

[2]

The authors evaluate the method on the data set on a year basis and present the top three scores and measure distortion / score but not how this is calculated.

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying on the available documentation?_

[3]

Requires expertise on metric distortion and voting theory.
