Mechanomics Code - JVT حسب Carlo Vittorio Cannistraci (5854046)

"…The functions were tested respectively in: MATLAB 2018a or youger, Python 3.9.4, R 4.0.3.…"

Decoding fairness motivations - repository حسب Sebastian Speer (6489207)

"…All analysis were conducted in Python 2.7.</div><div><br></div><div><b>Behavioral Data:</b><br></div><div><br></div><div><u>Files:</u> </div><div><br></div><div><i>DifffereceOffers.csv </i>- Offers made by participants in Study 1</div><div><i>Diffs_W.csv</i> - Offers made by participants in Study 2</div><div><br></div><div><i>Individual-differences-in-offers2.png</i> - Plot of individual differences as illustrated in the paper</div><div><i>Individual-differences-MeanOffers.png </i>- Individual differences in mean offers in both games as illustrated in the Appendix</div><div><i>SocialvsNonSocial2.png </i>- Difference in Offers between Selfish and strategic players when playing against humans and computers</div><div><br></div><div>Behavioral Data, specfically Ultimatum Game and Dictator Game Offers and Plots resulting from behavioral analysis reported in the following paper:</div><div><br></div><div>S.P.H. …"

Code حسب Baoqiang Chen (21099509)

"…We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …"

Core data حسب Baoqiang Chen (21099509)

"…We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …"