Extending the Entropic Potential of Events for Uncertainty Quantification and Decision-Making in Artificial Intelligence

• URL: http://arxiv.org/abs/2508.10241v1
• Date: Wed, 13 Aug 2025 23:52:12 GMT
• Title: Extending the Entropic Potential of Events for Uncertainty Quantification and Decision-Making in Artificial Intelligence
• Authors: Mark Zilberman,
• Abstract summary: The entropic potential of events is a parameter quantifying the influence of discrete events on the expected future entropy of a system.<n>The framework is adapted for AI by introducing an event-centric measure that captures how actions, observations, or other discrete occurrences impact uncertainty at future time horizons.<n> Applications are explored in policy evaluation, intrinsic reward design, explainable AI, and anomaly detection.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work demonstrates how the concept of the entropic potential of events -- a parameter quantifying the influence of discrete events on the expected future entropy of a system -- can enhance uncertainty quantification, decision-making, and interpretability in artificial intelligence (AI). Building on its original formulation in physics, the framework is adapted for AI by introducing an event-centric measure that captures how actions, observations, or other discrete occurrences impact uncertainty at future time horizons. Both the original and AI-adjusted definitions of entropic potential are formalized, with the latter emphasizing conditional expectations to account for counterfactual scenarios. Applications are explored in policy evaluation, intrinsic reward design, explainable AI, and anomaly detection, highlighting the metric's potential to unify and strengthen uncertainty modeling in intelligent systems. Conceptual examples illustrate its use in reinforcement learning, Bayesian inference, and anomaly detection, while practical considerations for computation in complex AI models are discussed. The entropic potential framework offers a theoretically grounded, interpretable, and versatile approach to managing uncertainty in AI, bridging principles from thermodynamics, information theory, and machine learning.

Related papers

• PAI-Bench: A Comprehensive Benchmark For Physical AI [70.22914615084215]
  Video generative models often struggle to maintain physically coherent dynamics.<n>Multi-modal large language models exhibit limited performance in forecasting and causal interpretation.<n>These observations suggest that current systems are still at an early stage in handling the perceptual and predictive demands of Physical AI.
  arXiv  Detail & Related papers  (2025-12-01T18:47:39Z)
• A Conjecture on a Fundamental Trade-Off between Certainty and Scope in Symbolic and Generative AI [0.0]
  Article introduces a conjecture that formalises a fundamental trade-off between provable correctness and broad data-mapping capacity in AI systems.<n>By making this implicit trade-off explicit and open to rigorous verification, the conjecture significantly reframes both engineering ambitions and philosophical expectations for AI.
  arXiv  Detail & Related papers  (2025-06-11T19:18:13Z)
• Exploring Energy Landscapes for Minimal Counterfactual Explanations: Applications in Cybersecurity and Beyond [3.6963146054309597]
  Counterfactual explanations have emerged as a prominent method in Explainable Artificial Intelligence (XAI)<n>We present a novel framework that integrates perturbation theory and statistical mechanics to generate minimal counterfactual explanations.<n>Our approach systematically identifies the smallest modifications required to change a model's prediction while maintaining plausibility.
  arXiv  Detail & Related papers  (2025-03-23T19:48:37Z)
• From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence [19.369216778200034]
  Uncertainty quantification (UQ) is a critical aspect of artificial intelligence (AI) systems.<n>This review explores the evolution of uncertainty quantification techniques in AI.<n>We examine the diverse applications of UQ across various fields, emphasizing its impact on decision-making, predictive accuracy, and system robustness.
  arXiv  Detail & Related papers  (2025-01-05T23:14:47Z)
• Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution [110.99891169486366]
  We propose a method that integrates efficient and precise uncertainty quantification into a deep learning-based surrogate model. Our method endows deep learning-based surrogate models with robust and efficient uncertainty quantification capabilities for both forward and inverse problems. Our method excels at propagating uncertainty over extended auto-regressive rollouts, making it suitable for scenarios involving long-term predictions.
  arXiv  Detail & Related papers  (2024-02-13T11:22:59Z)
• Predictable Artificial Intelligence [77.1127726638209]
  This paper introduces the ideas and challenges of Predictable AI.<n>It explores the ways in which we can anticipate key validity indicators of present and future AI ecosystems.<n>We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI ecosystems.
  arXiv  Detail & Related papers  (2023-10-09T21:36:21Z)
• Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications [0.32302664881848275]
  Evidential deep learning is a technique that extends parametric deep learning to higher-order distributions. This study compares the uncertainty derived from evidential neural networks to those obtained from ensembles. We show evidential deep learning models attaining predictive accuracy rivaling standard methods, while robustly quantifying both sources of uncertainty.
  arXiv  Detail & Related papers  (2023-09-22T23:04:51Z)
• The Unreasonable Effectiveness of Deep Evidential Regression [72.30888739450343]
  A new approach with uncertainty-aware regression-based neural networks (NNs) shows promise over traditional deterministic methods and typical Bayesian NNs. We detail the theoretical shortcomings and analyze the performance on synthetic and real-world data sets, showing that Deep Evidential Regression is a quantification rather than an exact uncertainty.
  arXiv  Detail & Related papers  (2022-05-20T10:10:32Z)
• Active Inference in Robotics and Artificial Agents: Survey and Challenges [51.29077770446286]
  We review the state-of-the-art theory and implementations of active inference for state-estimation, control, planning and learning. We showcase relevant experiments that illustrate its potential in terms of adaptation, generalization and robustness.
  arXiv  Detail & Related papers  (2021-12-03T12:10:26Z)
• Counterfactual Explanations as Interventions in Latent Space [62.997667081978825]
  Counterfactual explanations aim to provide to end users a set of features that need to be changed in order to achieve a desired outcome. Current approaches rarely take into account the feasibility of actions needed to achieve the proposed explanations. We present Counterfactual Explanations as Interventions in Latent Space (CEILS), a methodology to generate counterfactual explanations.
  arXiv  Detail & Related papers  (2021-06-14T20:48:48Z)

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.