BEAST-Net: Learning novel behavioral insights using a neural network adaptation of a behavioral model

In this paper, we propose a behavioral model called BEAST-Net, which combines the basic logic of BEAST, a psychological theory-based behavioral model, with machine learning (ML) techniques. Our approach is to formalize BEAST mathematically as a differentiable function and parameterize it with a neural network, enabling us to learn the model parameters from data and optimize it using backpropagation. The resulting model, BEAST-Net, is able to scale to larger datasets and adapt to new data with greater ease, while retaining the psychological insights and interpretability of the original model. We evaluate BEAST-Net on the largest public benchmark dataset of human choice tasks and show that it outperforms several baselines, including the original BEAST model. Furthermore, we demonstrate that our model can be used to provide interpretable explanations for choice behavior, allowing us to derive new psychological insights from the data. Our work makes a significant contribution to the field of human decision making by showing that ML techniques can be used to improve the scalability and adaptability of psychological theory based models while preserving their interpretability and ability to provide insights.