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This paper presents a sandbox example of how the integration of modelsborrowed from Behavioral Economic (specifically Protection-Motivation Theory)into ML algorithms (specifically Bayesian Networks) can improve the performanceand interpretability of ML algorithms when applied to Behavioral Data. Theintegration of Behavioral Economics knowledge to define the architecture of theBayesian Network increases the accuracy of the predictions in 11 percentagepoints. Moreover, it simplifies the training process, making unnecessarytraining computational efforts to identify the optimal structure of theBayesian Network. Finally, it improves the explicability of the algorithm,avoiding illogical relations among variables that are not supported by previousbehavioral cybersecurity literature. Although preliminary and limited to 0nesimple model trained with a small dataset, our results suggest that theintegration of behavioral economics and complex ML models may open a promisingstrategy to improve the predictive power, training costs and explicability ofcomplex ML models. This integration will contribute to solve the scientificissue of ML exhaustion problem and to create a new ML technology with relevantscientific, technological and market implications.

Integration of Behavioral Economic Models to Optimize ML performance and interpretability: a sandbox example