Less can be More: Effects of a Forgetting Function on an AI-based Policy Capturing Tool Performance

Artificial intelligence (AI) systems need to adapt to changing circumstances to maintain relevance in dynamic environments. Inspired by the adaptive advantages of human forgetting, this study investigates the integration of a forgetting function into an AI system. We implemented this mechanism as a training window within the Cognitive Shadow (CS) system, an AI designed to learn and emulate human decision models. This training window hyperparameter-applicable to supervised machine learning algorithms-aims to address the issue of concept drift by prioritizing recent information. The effectiveness of this addition was tested with a simple strategy game similar in dynamics to rock-paper-scissors. Participants played individually against an AI opponent for three 60-round sessions. CS was trained during Session 1 to learn the decision patterns of the player and actively predicted and countered human decisions in Sessions 2 and 3. Analyses showed that including the training window significantly improved prediction accuracy in both Sessions 2 and 3 by emphasizing recent, relevant data. These findings highlight the potential of incorporating human-inspired forgetting mechanisms to enhance AI performance in interactive and dynamic environments, with implications for future decision support systems.