This research proposes a theoretical framework to characterize the optimal balance between Decision Ability (D-Ability) and Prediction Ability (P-Ability) in intelligent systems, formalizing these abilities and identifying the Balance Ability Point (BAP) where marginal gains in prediction accuracy yield diminishing returns in decision quality.
Key findings
D-Ability is formalized as the capacity to select actions that maximize expected utility.
P-Ability is formalized as the capacity to forecast future states with minimal error.
The optimal balance depends on the decision sensitivity of the task environment and the complementarity coefficient between abilities.
A unified optimization framework identifies the Balance Ability Point (BAP) where marginal gains in prediction accuracy yield diminishing returns in decision quality.
Limitations & open questions
The framework assumes general conditions and may not apply to all contexts.
Further experimental validation is required to confirm the theoretical findings in diverse real-world domains.