Merge branch 'main' into raviq-patch-32

README.md

@@ -10,7 +10,7 @@
 
 ## Overview of the Competition
 
-The field of [computational social choice (COMPSOC)](https://en.wikipedia.org/wiki/Computational_social_choice) combines ideas, techniques, and models from computer science and social choice theory for aggregating collective preferences. This thriving and multidisciplinary field of research has numerous applications to group decision-making, resource allocation, fair division, and election systems. One of the most well-studied problems in COMSOC focuses on designing voting mechanisms for selecting the winning candidates for an election. Paradoxes and impossibility results are commonly encountered when implementing voting rules in electoral systems. Researchers are therefore exploring alternatives to classical voting mechanisms by incorporating, for instance, principles and techniques from Machine Learning. Agent-based simulations can also tackle such challenges, as evidenced by their successful applications in negotiation research, supply chain management, and energy markets. In line with this vision, [The 1st Computational Social Choice Competition at IJCAI 2023 (COMPSOC 2023)](https://compsoc.algocratic.org/) capitalizes on the progress in agent research and computational social choice to drive the development of inclusive, robust, and fair election systems.
+The field of [computational social choice (COMPSOC)](https://en.wikipedia.org/wiki/Computational_social_choice) combines ideas, techniques, and models from computer science and social choice theory for aggregating collective preferences. This thriving and multidisciplinary field of research has numerous applications to group decision-making, resource allocation, fair division, and election systems. One of the most well-studied problems in COMSOC focuses on designing voting mechanisms for selecting the winning candidates for an election. Paradoxes and impossibility results are commonly encountered when implementing voting rules in electoral systems. Researchers are therefore exploring alternatives to classical voting mechanisms by incorporating, for instance, principles and techniques from Machine Learning. Agent-based simulations can also tackle such challenges, as evidenced by their successful applications in negotiation research, supply chain management, and energy markets. In line with this vision, the [Computational Social Choice Competition (COMPSOC)](https://compsoc.algocratic.org/) series capitalizes on the progress in agent research and computational social choice to drive the development of inclusive, robust, and fair election systems.
 
 ## Goals of the Competition
 
@@ -22,7 +22,7 @@ To participate in the competition, you must register on the [COMPSOC registratio
 
 ## General Guidelines
 
-The flow of the competition is illustrated in the figure. In step *(1)*, the competitors register on the [COMPSOC main page](https://compsoc.algocratic.org/) under the menu `COMPSOC 2023` to be allowed to access their accounts. In step *(2)*, the competitors implement their voting rules using this Python SDK and then upload them to the site. In step *(3)*, synthetic voting profiles will be parametrically generated using various state-of-the-art [voter models](https://en.wikipedia.org/wiki/Voter_model). In step *(4)*, we will separately apply the competitors' [voter rules](https://en.wikipedia.org/wiki/Social_choice_theory) to the generated baseline of profiles. In step *(5)*, the optimal voting rules will be selected based on social welfare and how well they satisfy anonymity, neutrality, monotonicity, Pareto optimality, unanimity, and non-imposition.
+The flow of the competition is illustrated in the figure. In step *(1)*, the competitors register on the [COMPSOC main page](https://compsoc.algocratic.org/) under the menu `COMPSOC 20XX` to be allowed to access their accounts. In step *(2)*, the competitors implement their voting rules using this Python SDK and then upload them to the site. In step *(3)*, synthetic voting profiles will be parametrically generated using various state-of-the-art [voter models](https://en.wikipedia.org/wiki/Voter_model). In step *(4)*, we will separately apply the competitors' [voter rules](https://en.wikipedia.org/wiki/Social_choice_theory) to the generated baseline of profiles. In step *(5)*, the optimal voting rules will be selected based on social welfare and how well they satisfy anonymity, neutrality, monotonicity, Pareto optimality, unanimity, and non-imposition.
 
 <p align="center">
 <img src="./figures/misc/overview_.png" style="height:90%; width:90%"/>
@@ -127,7 +127,7 @@ and then call ```run.py``` with the right arguments
 python run.py [-h] [-v] num_candidates num_voters num_iterations num_topn distortion_ratio {gaussian,multinomial_dirichlet,random}
 ```
 
-The competition will run on the [COMPSOC 2023 server](https://compsoc2023.algocratic.org/). You will have to register and then upload the code of your rules. All results will be displayed on the [public result page](https://compsoc2023.algocratic.org/competition/public).
+The competition will run on the [COMPSOC server](https://compsoc2024.algocratic.org/). You will have to register and then upload the code of your rules. All results will be displayed on the [public result page](https://compsoc2024.algocratic.org/competition/public).
 
 ### Examples
 
@@ -325,4 +325,4 @@ details.
 
 If you find this code to be of any use, please let me know. I would also welcome any feedback.
 
-Copyright (c) 2023 Rafik Hadfi, rafik [dot] hadfi [at] gmail [dot] com
+Copyright (c) 2024 Rafik Hadfi, rafik [dot] hadfi [at] gmail [dot] com

tests/simple_rule/README.md

@@ -1,6 +1,8 @@
 To run:
 
-python tournament.py [-h] [-v] num_candidates num_voters num_iterations num_topn voters_model
+```
+python tournament.py  [-h] [-v] num_candidates num_voters num_iterations num_topn voters_model
+```
 
 Example: