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

Researchers have developed a formal decision-theoretic framework that quantifies the value of perception, prediction, communication, and common sense in autonomous decision-making systems. The work reveals that perception alone can have negative value, while combined perception-prediction and standalone prediction always yield non-negative returns, with applications to autonomous systems design and cognitive science.

This academic research addresses a fundamental challenge in building intelligent autonomous systems: understanding which information sources matter most and in what order they should be prioritized. The framework transforms qualitative concepts like perception and prediction into mathematically rigorous decision-theoretic quantities, enabling quantitative comparison of their contributions to decision quality.

The finding that perception without prediction can produce negative value has significant implications for system design. It suggests that observing environmental data without the ability to forecast future states may actually degrade decision quality by introducing noise or spurious correlations. This insight challenges the common assumption that more information universally improves outcomes. The non-negative nature of prediction-inclusive approaches indicates that coupling observation with forecasting capability provides a natural safety mechanism.

For autonomous systems developers, this research provides concrete guidance on resource allocation. Rather than implementing comprehensive perception of all available agents and variables, engineers can use these defined quantities to determine which observations are truly necessary and their relative importance. This optimization directly reduces computational burden and system complexity.

The connection to Shannon entropy and mutual information positions this work at the intersection of information theory and decision science, potentially bridging classical information theory with practical AI applications. The authors suggest the framework illuminates how natural intelligence—biological brains—selectively processes information, offering cognitive science perspectives on neural information processing. Future applications could guide the design of more efficient perception systems in robotics, autonomous vehicles, and distributed decision-making networks.

• →Perception without prediction can have negative value in decision-making systems, indicating that observation alone may degrade outcomes without forecasting capability.
• →The framework quantifies information value using decision-theoretic and information-theoretic approaches with connections to Shannon entropy.
• →The research provides practical guidance for autonomous system designers to determine which agents to observe and in what priority order.
• →Mathematical definitions of perception, prediction, and communication value enable optimization of resource allocation in intelligent systems.
• →The work bridges formal information theory with cognitive science, potentially explaining how biological systems process multimodal information.