Prepares the draft for submission to a specific journal.

README.md

@@ -1,8 +1,69 @@
-# Paper revisiting Axelrod's second tournament
+# Reviving, Reproducing, and Revisiting Axelrod's Second Tournament
 
-## Setting up the environment
+This repository contains the code and manuscript source files for the project
+***Reviving, Reproducing, and Revisiting Axelrod's Second Tournament***.
 
-- Install https://github.com/Axelrod-Python/TourExec (see instructions there).
-- Create the conda environment:
+The project reproduces Robert Axelrod’s second Iterated Prisoner’s Dilemma
+tournament, originally run in the early 1980s, using the original Fortran
+implementations of the submitted strategies. It uses
+[**Axelrod-Python**](https://github.com/Axelrod-Python/Axelrod) and
+[**TourExec**](https://github.com/Axelrod-Python/TourExec) to run the computer
+tournament simulations.
 
-    conda env create -f environment.yml
+A preprint of this work is available at: https://arxiv.org/abs/2510.15438.
+
+
+## Requirements
+
+* Install [TourExec](https://github.com/Axelrod-Python/TourExec) (see
+  installation instructions in that repository).
+* You will also need Python.
+
+
+## Setting up the Environment
+
+To install all necessary Python dependencies, we recommend creating a **conda
+environment** using the provided configuration file `src/environment.yml`. From
+the root of this repository run:
+
+```bash
+conda env create -f src/environment.yml
+conda activate axelrod_tournament
+```
+
+
+## Source Code
+
+* The code for generating and reproducing the original tournaments is located in
+  the `src/` directory.
+* It includes:
+
+  * **Tournament execution scripts** to reproduce Axelrod’s results.
+  * **Analysis scripts and Jupyter notebooks** for generating all figures used
+    in the manuscript.
+
+To rerun the original tournaments, refer to the
+[documentation](https://github.com/Axelrod-Python/revisiting-axelrod-second/tree/main/src)
+in `src/`.
+
+
+## Data
+
+Running the simulations will generate the tournament data.
+
+If you prefer **not to rerun** the entire simulation, you can download the
+archived data from Zenodo:
+
+1. Download the dataset archive from https://doi.org/10.5281/zenodo.17250038.
+2. Unzip it into the `data/` folder.
+
+This will allow you to reproduce the analysis and figures directly.
+
+
+## Analysis and Figures
+
+All data analysis and manuscript figures were generated using **Python** and
+**Jupyter notebooks**, available under `src/`.
+
+If you have downloaded the archived data, you can simply open and run the
+notebooks to reproduce all figures without rerunning the full simulations.

assets/original_tournament_rankings.tex

@@ -1,21 +1,21 @@
-\begin{tabular}{llcccc}
+\begin{tabular}{llccccr}
 \toprule
- & \makecell[c]{Author} & \makecell[c]{Average Score} & \makecell[c]{Reproduced Rank} & \makecell[c]{Original Rank} & \makecell[c]{Average\\Cooperation Rate} \\
+ & \makecell[c]{Author} & \makecell[c]{Average Score} & \makecell[c]{Reproduced Rank} & \makecell[c]{Original Rank} & \makecell[c]{Average\\Cooperation Rate} & \makecell[c]{Win\\proportion} \\
 \midrule
-k92r & Anatol Rapoport & 2.878 & 1 & 1 & 0.922 \\
-k61r & Danny C Champion & 2.791 & 12 & 2 & 0.954 \\
-k42r & Otto Borufsen & 2.861 & 2 & 3 & 0.916 \\
-k49r & Rob Cave & 2.826 & 4 & 4 & 0.892 \\
-k44r & William Adams & 2.826 & 3 & 5 & 0.882 \\
-k60r & Jim Graaskamp and Ken Katzen & 2.810 & 9 & 6 & 0.844 \\
-k41r & Herb Weiner & 2.811 & 8 & 7 & 0.865 \\
-k75r & Paul D Harrington & 2.826 & 5 & 8 & 0.802 \\
-k84r & T Nicolaus Tideman and Paula Chieruzz & 2.812 & 7 & 9 & 0.882 \\
-k32r & Charles Kluepfel & 2.817 & 6 & 10 & 0.873 \\
-k35r & Abraham Getzler & 2.801 & 11 & 11 & 0.888 \\
-k68r & Fransois Leyvraz & 2.808 & 10 & 12 & 0.917 \\
-k72r & Edward C White Jr & 2.776 & 13 & 13 & 0.924 \\
-k46r & Graham J Eatherley & 2.772 & 14 & 14 & 0.948 \\
-k83r & Paul E Black & 2.743 & 15 & 15 & 0.935 \\
+k92r & Anatol Rapoport & 2.8785 & 1 & 1 & 0.9218 & 0.6306 \\
+k61r & Danny C Champion & 2.7907 & 12 & 2 & 0.9542 & 0.0000 \\
+k42r & Otto Borufsen & 2.8607 & 2 & 3 & 0.9161 & 0.3149 \\
+k49r & Rob Cave & 2.8260 & 4 & 4 & 0.8922 & 0.0000 \\
+k44r & William Adams & 2.8260 & 3 & 5 & 0.8819 & 0.0038 \\
+k60r & Jim Graaskamp and Ken Katzen & 2.8102 & 9 & 6 & 0.8440 & 0.0102 \\
+k41r & Herb Weiner & 2.8107 & 8 & 7 & 0.8650 & 0.0000 \\
+k75r & Paul D Harrington & 2.8258 & 5 & 8 & 0.8019 & 0.0224 \\
+k84r & T Nicolaus Tideman and Paula Chieruzz & 2.8124 & 7 & 9 & 0.8818 & 0.0000 \\
+k32r & Charles Kluepfel & 2.8170 & 6 & 10 & 0.8726 & 0.0080 \\
+k35r & Abraham Getzler & 2.8010 & 11 & 11 & 0.8878 & 0.0002 \\
+k68r & Fransois Leyvraz & 2.8085 & 10 & 12 & 0.9169 & 0.0000 \\
+k72r & Edward C White Jr & 2.7762 & 13 & 13 & 0.9242 & 0.0000 \\
+k46r & Graham J Eatherley & 2.7722 & 14 & 14 & 0.9483 & 0.0000 \\
+k83r & Paul E Black & 2.7425 & 15 & 15 & 0.9347 & 0.0000 \\
 \bottomrule
 \end{tabular}

bibliography.bib

@@ -489,3 +489,90 @@ @dataset{knight_2025_17250038
   doi          = {10.5281/zenodo.17250038},
   url          = {https://doi.org/10.5281/zenodo.17250038},
 }
+
+@article{open_science_collaboration2015,
+  title={Estimating the reproducibility of psychological science},
+  author={{Open Science Collaboration}},
+  journal={Science},
+  year={2015},
+  volume={349},
+  number={6251},
+  pages={aac4716},
+}
+
+@article{camerer2016,
+  title={Evaluating replicability of laboratory experiments in economics},
+  author={Camerer, Colin F. and others},
+  journal={Science},
+  year={2016},
+  volume={351},
+  number={6280},
+  pages={1433--1436},
+}
+
+@article{camerer2018,
+  title={Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015},
+  author={Camerer, Colin F. and others},
+  journal={Nature Human Behaviour},
+  year={2018},
+  volume={2},
+  pages={637--644},
+}
+
+
+@article{silberzahn2018,
+  title={Many analysts, one dataset: Making transparent how variations in analytical choices affect results},
+  author={Silberzahn, Raphael and others},
+  journal={Advances in Methods and Practices in Psychological Science},
+  year={2018},
+  volume={1},
+  number={3},
+  pages={337--356},
+}
+
+@article{breznau2022,
+  title={Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty},
+  author={Breznau, Nate and others},
+  journal={PNAS},
+  year={2022},
+  volume={119},
+  number={44},
+  pages={e2203150119},
+}
+
+@article{sandve2013,
+  title={Ten simple rules for reproducible computational research},
+  author={Sandve, Geir Kjetil and others},
+  journal={PLoS Computational Biology},
+  year={2013},
+  volume={9},
+  number={10},
+  pages={e1003285},
+}
+
+@article{wilson2017,
+  title={Good enough practices in scientific computing},
+  author={Wilson, Greg and others},
+  journal={PLoS Computational Biology},
+  year={2017},
+  volume={13},
+  number={6},
+  pages={e1005510},
+}
+
+@article{stodden2018,
+  title={Enhancing reproducibility for computational methods},
+  author={Stodden, Victoria and Seiler, Jennifer and Ma, Zhaokun},
+  journal={PNAS},
+  year={2018},
+  volume={115},
+  number={11},
+  pages={2561--2570},
+}
+
+@book{turingway2022,
+  title={The Turing Way: A Handbook for Reproducible, Ethical and Collaborative Data Science},
+  author={{The Turing Way Community}},
+  year={2022},
+  note={Zenodo. DOI: 10.5281/zenodo.3233853}
+}