  This paper presents a sandbox example of how the integration of models
borrowed from Behavioral Economic (specifically Protection-Motivation Theory)
into ML algorithms (specifically Bayesian Networks) can improve the performance
and interpretability of ML algorithms when applied to Behavioral Data. The
integration of Behavioral Economics knowledge to define the architecture of the
Bayesian Network increases the accuracy of the predictions in 11 percentage
points. Moreover, it simplifies the training process, making unnecessary
training computational efforts to identify the optimal structure of the
Bayesian Network. Finally, it improves the explicability of the algorithm,
avoiding illogical relations among variables that are not supported by previous
behavioral cybersecurity literature. Although preliminary and limited to 0ne
simple model trained with a small dataset, our results suggest that the
integration of behavioral economics and complex ML models may open a promising
strategy to improve the predictive power, training costs and explicability of
complex ML models. This integration will contribute to solve the scientific
issue of ML exhaustion problem and to create a new ML technology with relevant
scientific, technological and market implications.
