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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 2601.06077 ↗First posted 29 Dec 2025cs.IT cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 24 references that are in the map. First posted on arXiv 29 Dec 2025.

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References · 9

  1. 2023Rank2Tell: A Multimodal Driving Dataset for Joint Importance Ranking and Reasoningcited by 63
  2. 2020Minimum Excess Risk in Bayesian Learningcited by 35
  3. 2019Goal-oriented Object Importance Estimation in On-road Driving Videoscited by 19
  4. 2020Interaction Graphs for Object Importance Estimation in On-road Driving Videoscited by 15
  5. 2022Important Object Identification with Semi-Supervised Learning for Autonomous Drivingcited by 13
  6. 2019Agent Prioritization for Autonomous Navigationcited by 8
  7. 2025Explaining and Improving Information Complementarities in Multi-Agent Decision-makingcited by 6
  8. 2019Neural Mechanisms of Human Decision-Makingcited by 5
  9. 2022Bayesian Learning for Dynamic Inferencecited by 1

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