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
- URL: http://arxiv.org/abs/2601.06077v1
- Date: Mon, 29 Dec 2025 19:18:19 GMT
- Title: 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
- Authors: Aolin Xu,
- Abstract summary: This work aims to rigorously define the values of perception, prediction, communication, and common sense in decision making.<n>The defined quantities suggest answers to practical questions arising in the design of autonomous decision-making systems.
- Score: 1.218340575383456
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Performative Prediction on Games and Mechanism Design [69.7933059664256]
We study a collective risk dilemma where agents decide whether to trust predictions based on past accuracy.<n>As predictions shape collective outcomes, social welfare arises naturally as a metric of concern.<n>We show how to achieve better trade-offs and use them for mechanism design.
arXiv Detail & Related papers (2024-08-09T16:03:44Z) - The Relative Value of Prediction in Algorithmic Decision Making [0.0]
We ask: What is the relative value of prediction in algorithmic decision making?
We identify simple, sharp conditions determining the relative value of prediction vis-a-vis expanding access.
We illustrate how these theoretical insights may be used to guide the design of algorithmic decision making systems in practice.
arXiv Detail & Related papers (2023-12-13T20:52:45Z) - Rationalizing Predictions by Adversarial Information Calibration [65.19407304154177]
We train two models jointly: one is a typical neural model that solves the task at hand in an accurate but black-box manner, and the other is a selector-predictor model that additionally produces a rationale for its prediction.
We use an adversarial technique to calibrate the information extracted by the two models such that the difference between them is an indicator of the missed or over-selected features.
arXiv Detail & Related papers (2023-01-15T03:13:09Z) - What Should I Know? Using Meta-gradient Descent for Predictive Feature
Discovery in a Single Stream of Experience [63.75363908696257]
computational reinforcement learning seeks to construct an agent's perception of the world through predictions of future sensations.
An open challenge in this line of work is determining from the infinitely many predictions that the agent could possibly make which predictions might best support decision-making.
We introduce a meta-gradient descent process by which an agent learns what predictions to make, 2) the estimates for its chosen predictions, and 3) how to use those estimates to generate policies that maximize future reward.
arXiv Detail & Related papers (2022-06-13T21:31:06Z) - Empirical Estimates on Hand Manipulation are Recoverable: A Step Towards
Individualized and Explainable Robotic Support in Everyday Activities [80.37857025201036]
Key challenge for robotic systems is to figure out the behavior of another agent.
Processing correct inferences is especially challenging when (confounding) factors are not controlled experimentally.
We propose equipping robots with the necessary tools to conduct observational studies on people.
arXiv Detail & Related papers (2022-01-27T22:15:56Z) - Finding Useful Predictions by Meta-gradient Descent to Improve
Decision-making [1.384055225262046]
We focus on predictions expressed as General Value Functions: temporally extended estimates of the accumulation of a future signal.
One challenge is determining from the infinitely many predictions that the agent could possibly make which might support decision-making.
By learning, rather than manually specifying these predictions, we enable the agent to identify useful predictions in a self-supervised manner.
arXiv Detail & Related papers (2021-11-18T20:17:07Z) - A Number Sense as an Emergent Property of the Manipulating Brain [16.186932790845937]
We study the mechanism through which humans acquire and develop the ability to manipulate numbers and quantities.
Our model acquires the ability to estimate numerosity, i.e. the number of objects in the scene.
We conclude that important aspects of a facility with numbers and quantities may be learned with supervision from a simple pre-training task.
arXiv Detail & Related papers (2020-12-08T00:37:35Z) - When Does Uncertainty Matter?: Understanding the Impact of Predictive
Uncertainty in ML Assisted Decision Making [68.19284302320146]
We carry out user studies to assess how people with differing levels of expertise respond to different types of predictive uncertainty.
We found that showing posterior predictive distributions led to smaller disagreements with the ML model's predictions.
This suggests that posterior predictive distributions can potentially serve as useful decision aids which should be used with caution and take into account the type of distribution and the expertise of the human.
arXiv Detail & Related papers (2020-11-12T02:23:53Z) - Maximizing Information Gain in Partially Observable Environments via
Prediction Reward [64.24528565312463]
This paper tackles the challenge of using belief-based rewards for a deep RL agent.
We derive the exact error between negative entropy and the expected prediction reward.
This insight provides theoretical motivation for several fields using prediction rewards.
arXiv Detail & Related papers (2020-05-11T08:13:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.