Machine Learning in Application to Behavioral Economics

Behavioral economics is one of the most perspective sphere of the economy, as it includes the study of the decision-making process by people in various conditions and the analysis of factors influencing these decisions, thereby moving away from the classical theory of rationality. In order to predict the choice of a person and reveal the most important factors in this process, good tools are needed, which can be machine learning (ML). ML algorithms can self-learn from experience, recognize patterns in data and predict their future development. Thus, machine learning could facilitate the interpretation of experiments to understand people's decision-making processes and could predict human behavior in individual situations. The purpose of this papper is to study the possibility of using machine learning in a behavioral economics by analyzing articles on this topic and create a machine learning model on specific data. In order to create a model that can predict the task execution time (lottery selection) in limited time conditions, as well as determine the factors that influence this, data were taken from the experiment “Risk, lack of time and selection effects” Martin J. Kocher and his colleagues.