Predicting What Matters: Training AI Models for Better Decisions

Artificial intelligence (AI) models that predict the future behavior of real-world systems and processes (also known as predictive AI models) are central to intelligent decision-making. They are often employed in model-based decision-making frameworks to optimize decisions for real-world tasks based on their predictions. However, decisions optimized using such predictive AI models often result in suboptimal performance when applied in the real world. This is primarily because these models are typically constructed to best fit the behavior of the real-world system, and hence to predict the most likely future rather than to optimize the best possible decisions for a given task. Due to this objective mismatch, their predictions cannot be guaranteed to support optimal decision-making in theory or in practice. In fact, there is increasing empirical evidence and consensus that predictive models must be tailored to decision-making objectives to achieve optimal real-world performance. Supporting this observation, we establish formal (necessary and sufficient) conditions that a predictive model (AI-based or not) must satisfy for a decision-making policy derived using that model to achieve optimal performance in the real world. We then discuss their implications for building predictive AI models for optimal sequential decision-making.