Prediction oriented behavioral research and its relationship to classical decision research

This paper argues that two of the common methods used in behavioral and social sciences to reduce the chances that models overfit the available data, namely heavy reliance on benchmark models and rigorous parameter estimation techniques, can slow the advancement of these sciences. An examination of classical decision research highlights how applying these methods shaped the field but have also led to limited success. As an alternative, the paper proposes a prediction-oriented approach to the development of behavioral models. Evaluating and comparing models based on their predictive power inherently guards against overfitting and also facilitates accumulation of knowledge. The paper reviews research employing the prediction-oriented approach in behavioral decision research and demonstrates that, in contrast to a common misconception, the focus on predictions can also facilitate better understanding of the underlying processes.