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This paper introduces decision-theoretic quantities for perception, prediction, communication, and common sense that share mathematical properties with Shannon entropy, offering a rigorous framework for optimizing information usage in autonomous systems. A key theoretical insight is that perception alone can yield negative value without prediction, whereas combined or standalone predictive actions remain nonnegative, directly informing the design of efficient agent observation strategies. These findings bridge information theory and AI decision-making, providing actionable metrics for determining the optimal sequence of sensing and forecasting in complex environments.
One if by Land, Two if by Sea, Three if by Four Seas, and More to Come -- Values of Perception, Prediction, Communication, and Common Sense in Decision Making
arXiv:2601.06077v2 Announce Type: replace-cross Abstract: This work aims to rigorously define the values of perception, prediction, communication, and common sense in decision making. The defined quantities are decision-theoretic, but have information-theoretic analogues, e.g., they share some simple but key mathematical properties with Shannon entropy and mutual information, and can reduce to these quantities in particular settings. One interesting observation is that, the value of perception without prediction can be negative, while the value of perception together with prediction and the value of prediction alone are always nonnegative. The defined quantities suggest answers to practical questions arising in the design of autonomous decision-making systems. Example questions include: Do we need to observe and predict the behavior of a particular agent? How important is it? What is the best order to observe and predict the agents? The defined quantities may also provide insights to cognitive science and neural science, toward the understanding of how natural decision makers make use of information gained from different sources and operations.
Abstract:This work aims to rigorously define the values of perception, prediction, communication, and common sense in decision making. The defined quantities are decision-theoretic, but have information-theoretic analogues, e.g., they share some simple but key mathematical properties with Shannon entropy and mutual information, and can reduce to these quantities in particular settings. One interesting observation is that, the value of perception without prediction can be negative, while the value of perception together with prediction and the value of prediction alone are always nonnegative. The defined quantities suggest answers to practical questions arising in the design of autonomous decision-making systems. Example questions include: Do we need to observe and predict the behavior of a particular agent? How important is it? What is the best order to observe and predict the agents? The defined quantities may also provide insights to cognitive science and neural science, toward the understanding of how natural decision makers make use of information gained from different sources and operations.
Information Theory (cs.IT); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Optimization and Control (math.OC)
arXiv:2601.06077 [cs.IT]
(or arXiv:2601.06077v2 [cs.IT] for this version)
https://doi.org/10.48550/arXiv.2601.06077 Focus to learn more arXiv-issued DOI via DataCite
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