Predicting the Result of a Managerial Game Using a Multi-Label Prediction Models

Abstract

The allocation of human resources in the managerial environment is a hard task to perform and to learn. The cost of a real-life experience is very precious. Therefore, companies develop managerial games to provide near real-life experience for decision-makers. The teaching process could benefit if the outcomes of the managerial game could be predicted. Namely, the teacher could adjust teaching materials according to the expected result. Besides predicting an outcome, one would like to predict the emotions of the decision-maker. Having this in mind, we employed multi-label prediction models for prediction an outcome of the game and emotions of the decision-maker. The AUC ranges 0.62-0.66 for the classification of emotions, and similar to 0.76 for the outcome of the managerial game.