Entropy最新文献_第7页

A Quantum Probability Approach to Improving Human-AI Decision Making.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-02-02 DOI: 10.3390/e27020152

Scott Humr, Mustafa Canan, Mustafa Demir

{"title":"A Quantum Probability Approach to Improving Human-AI Decision Making.","authors":"Scott Humr, Mustafa Canan, Mustafa Demir","doi":"10.3390/e27020152","DOIUrl":"10.3390/e27020152","url":null,"abstract":"Artificial intelligence is set to incorporate additional decision space that has traditionally been the purview of humans. However, AI systems that support decision making also entail the rationalization of AI outputs by humans. Yet, incongruencies between AI and human rationalization processes may introduce uncertainties in human decision making, which require new conceptualizations to improve the predictability of these interactions. The application of quantum probability theory (QPT) to human cognition is on the ascent and warrants potential consideration to human-AI decision making to improve these outcomes. This perspective paper explores how QPT may be applied to human-AI interactions and contributes by integrating these concepts into human-in-the-loop decision making. To capture this and offer a more comprehensive conceptualization, we use human-in-the-loop constructs to explicate how recent applications of QPT can ameliorate the models of interaction by providing a novel way to capture these behaviors. Followed by a summary of the challenges posed by human-in-the-loop systems, we discuss newer theories that advance models of the cognitive system by using quantum probability formalisms. We conclude by outlining areas of promising future research in human-AI decision making in which the proposed methods may apply.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 2","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11854726/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143499917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effects of Multiplicative Noise in Bistable Dynamical Systems.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-02-02 DOI: 10.3390/e27020155

Sara C Quintanilha Valente, Rodrigo da Costa Lima Bruni, Zochil González Arenas, Daniel G Barci

{"title":"Effects of Multiplicative Noise in Bistable Dynamical Systems.","authors":"Sara C Quintanilha Valente, Rodrigo da Costa Lima Bruni, Zochil González Arenas, Daniel G Barci","doi":"10.3390/e27020155","DOIUrl":"10.3390/e27020155","url":null,"abstract":"This study explores the escape dynamics of bistable systems influenced by multiplicative noise, extending the classical Kramers rate formula to scenarios involving state-dependent diffusion in asymmetric potentials. Using a generalized stochastic calculus framework, we derive an analytical expression for the escape rate and corroborate it with numerical simulations. The results highlight the critical role of the equilibrium potential Ueq(x), which incorporates noise intensity, stochastic prescription, and diffusion properties. We show how asymmetries and stochastic calculus prescriptions influence transition rates and equilibrium configurations. Using path integral techniques and weak noise approximations, we analyze the interplay between noise and potential asymmetry, uncovering phenomena such as barrier suppression and metastable state decay. The agreement between numerical and analytical results underscores the robustness of the proposed framework. This work provides a comprehensive foundation for studying noise-induced transitions in stochastic systems, offering insights into a broad range of applications in physics, chemistry, and biology.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 2","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11854221/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143499704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Levy Noise Affects Ornstein-Uhlenbeck Memory.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-02-02 DOI: 10.3390/e27020157

Iddo Eliazar

引用次数: 0

Solutions of a Class of Switch Dynamical Systems.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-02-02 DOI: 10.3390/e27020158

Marius-F Danca

引用次数: 0

A Network Analysis of the Impact of the Coronavirus Pandemic on the US Economy: A Comparison of the Return and the Momentum Picture.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-02-01 DOI: 10.3390/e27020148

Janusz Miśkiewicz

引用次数: 0

Algebraic Representations of Entropy and Fixed-Sign Information Quantities.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-02-01 DOI: 10.3390/e27020151

Keenan J A Down, Pedro A M Mediano

引用次数: 0

As One and Many: Relating Individual and Emergent Group-Level Generative Models in Active Inference.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-02-01 DOI: 10.3390/e27020143

Peter Thestrup Waade, Christoffer Lundbak Olesen, Jonathan Ehrenreich Laursen, Samuel William Nehrer, Conor Heins, Karl Friston, Christoph Mathys

{"title":"As One and Many: Relating Individual and Emergent Group-Level Generative Models in Active Inference.","authors":"Peter Thestrup Waade, Christoffer Lundbak Olesen, Jonathan Ehrenreich Laursen, Samuel William Nehrer, Conor Heins, Karl Friston, Christoph Mathys","doi":"10.3390/e27020143","DOIUrl":"10.3390/e27020143","url":null,"abstract":"Active inference under the Free Energy Principle has been proposed as an across-scales compatible framework for understanding and modelling behaviour and self-maintenance. Crucially, a collective of active inference agents can, if they maintain a group-level Markov blanket, constitute a larger group-level active inference agent with a generative model of its own. This potential for computational scale-free structures speaks to the application of active inference to self-organizing systems across spatiotemporal scales, from cells to human collectives. Due to the difficulty of reconstructing the generative model that explains the behaviour of emergent group-level agents, there has been little research on this kind of multi-scale active inference. Here, we propose a data-driven methodology for characterising the relation between the generative model of a group-level agent and the dynamics of its constituent individual agents. We apply methods from computational cognitive modelling and computational psychiatry, applicable for active inference as well as other types of modelling approaches. Using a simple Multi-Armed Bandit task as an example, we employ the new ActiveInference.jl library for Julia to simulate a collective of agents who are equipped with a Markov blanket. We use sampling-based parameter estimation to make inferences about the generative model of the group-level agent, and we show that there is a non-trivial relationship between the generative models of individual agents and the group-level agent they constitute, even in this simple setting. Finally, we point to a number of ways in which this methodology might be applied to better understand the relations between nested active inference agents across scales.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 2","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11853804/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143499944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantum Dynamics Framework with Quantum Tunneling Effect for Numerical Optimization.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-02-01 DOI: 10.3390/e27020150

Quan Tang, Peng Wang

{"title":"Quantum Dynamics Framework with Quantum Tunneling Effect for Numerical Optimization.","authors":"Quan Tang, Peng Wang","doi":"10.3390/e27020150","DOIUrl":"10.3390/e27020150","url":null,"abstract":"In recent years, optimization algorithms have developed rapidly, especially those which introduce quantum ideas, which perform excellently. Inspired by quantum thought, this paper proposes a quantum dynamics framework (QDF) which converts optimization problems into the problem of the constrained ground state of the quantum system and analyzes optimization algorithms by simulating the dynamic evolution process of physical optimization systems in the ground state. Potential energy equivalence and Taylor expansion are performed on the objective function to obtain the basic iterative operations of optimization algorithms. Furthermore, a quantum dynamics framework based on the quantum tunneling effect (QDF-TE) is proposed which adopts dynamic multiple group collaborative sampling to improve the quantum tunneling effect of the QDF, thereby increasing the population diversity and improving algorithm performance. The probability distribution of solutions can be visually observed through the evolution of the wave function, which also indicates that the QDF-TE can strengthen the tunneling effect. The QDF-TE was evaluated on the CEC 2017 test suite and shown to be competitive with other heuristic optimization algorithms. The experimental results reveal the effectiveness of introducing a quantum mechanism to analyze and improve optimization algorithms.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 2","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11854244/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143500073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Epistemic Uncertainty Gradient in Spaces of Random Projections.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-02-01 DOI: 10.3390/e27020144

Jeffrey F Queißer, Jun Tani, Jochen J Steil

{"title":"The Epistemic Uncertainty Gradient in Spaces of Random Projections.","authors":"Jeffrey F Queißer, Jun Tani, Jochen J Steil","doi":"10.3390/e27020144","DOIUrl":"10.3390/e27020144","url":null,"abstract":"This work presents a novel approach to handling epistemic uncertainty estimates with motivation from Bayesian linear regression. We propose treating the model-dependent variance in the predictive distribution-commonly associated with epistemic uncertainty-as a model for the underlying data distribution. Using high-dimensional random feature transformations, this approach allows for a computationally efficient, parameter-free representation of arbitrary data distributions. This allows assessing whether a query point lies within the distribution, which can also provide insights into outlier detection and generalization tasks. Furthermore, given an initial input, minimizing the uncertainty using gradient descent offers a new method of querying data points that are close to the initial input and belong to the distribution resembling the training data, much like auto-completion in associative networks. We extend the proposed method to applications such as local Gaussian approximations, input-output regression, and even a mechanism for unlearning of data. This reinterpretation of uncertainty, alongside the geometric insights it provides, offers an innovative and novel framework for addressing classical machine learning challenges.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 2","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11854594/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143499836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Understanding the Impact of Evaluation Metrics in Kinetic Models for Consensus-Based Segmentation.

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-02-01 DOI: 10.3390/e27020149

Raffaella Fiamma Cabini, Horacio Tettamanti, Mattia Zanella

引用次数: 0