Sub Optimal

Suboptimal decision-making, a pervasive challenge across diverse fields, focuses on understanding and mitigating the consequences of non-optimal choices by agents, whether human or artificial. Current research investigates this through various models, including Markov Decision Processes, Monte Carlo Tree Search, and reinforcement learning algorithms augmented with techniques like curriculum learning and constraint generation to improve efficiency and robustness. This research is significant because it addresses limitations in existing models that assume perfect rationality, leading to more realistic and effective solutions in areas such as autonomous driving, human-robot interaction, and economic modeling.