Physica A: Statistical Mechanics and its Applications最新文献

英文中文

Microcanonical Monte Carlo simulation of opinion dynamics under the influence of mass media

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-03-09 DOI: 10.1016/j.physa.2025.130516

Yasmín Navarrete , Carlos Femenías , Sergio Davis , Claudia Loyola

The formation of large social groups having uniform opinions influenced by mass media is currently an important topic in the social sciences. In this work, we explore and extend an off-lattice, two-dimensional Potts model (Eur. Phys. J. B 87, 78 [2014]) that describes the formation and dynamics of opinions in social groups according to individual consequence and agreement between neighbors. This model was originally obtained by the application of the maximum entropy principle, a general method in statistical inference, and using the same methodology we have now included the influence of mass media as a constant external field. By means of microcanonical Monte Carlo Metropolis simulations on a setup with two regions with opposing external influences, we have shown the presence of metastable states associated to the formation of clusters aligned with the locally imposed opinion. Our results suggest that, for some values of the total energy of the system, only a single cluster with a uniform opinion survives, thus the presence of two large, opposing groups is not a thermodynamically stable configuration.

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引用次数: 0

A double power-law memory kernel for magnetic fluctuations in the magnetosphere–ionosphere system observed close to the geomagnetic equator

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-03-08 DOI: 10.1016/j.physa.2025.130502

Víctor A. Samboni-Beltrán, Carlos A. García-Cadena, L.F. Rojas-Ochoa

We present a method for studying dynamic correlations in the magnetosphere–ionosphere system. This method solves the Generalized Langevin equation using a memory kernel derived from the experimental autocorrelation function of the random fluctuating force that drives electric currents near Earth’s geomagnetic equator. Our results show that the correlations exhibit a double power-law decay that depends on Earth’s geomagnetic activity level. This approach accurately describes statistical observables close to the geomagnetic equator, such as the mean squared fluctuation of the magnetosphere, without relying on specific interparticle interactions. The long-term memory effect identified in our study could have significant implications for monitoring Earth’s space weather.

我们提出了一种研究磁层-电离层系统动态相关性的方法。该方法利用从地球地磁赤道附近驱动电流的随机波动力的实验自相关函数中导出的记忆核来求解广义朗格文方程。我们的研究结果表明，相关性呈现出取决于地球地磁活动水平的双幂律衰减。这种方法可以准确描述地磁赤道附近的统计观测值，如磁层的平均平方波动，而无需依赖特定的粒子间相互作用。我们的研究中发现的长期记忆效应可能对监测地球空间天气具有重要意义。

引用次数: 0

Computational analysis of a normalized time-fractional Fokker–Planck equation

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-03-08 DOI: 10.1016/j.physa.2025.130500

Jian Wang , Keyong Chen , Junseok Kim

We propose a normalized time-fractional Fokker–Planck equation (TFFPE). A finite difference method is used to develop a computational method for solving the equation, and the system’s dynamics are investigated through computational simulations. The proposed model demonstrates accuracy and efficiency in approximating analytical solutions. Numerical tests validate the method’s effectiveness and highlight the impact of various fractional orders on the dynamics of the normalized time-fractional Fokker–Planck equation. The numerical tests emphasize the significant impact of different fractional orders on the temporal evolution of the system. Specifically, the computational results demonstrate how varying the fractional order influences the diffusion process, with lower orders exhibiting stronger memory effects and slower diffusion, while higher orders lead to faster propagation and a transition towards classical diffusion behavior. This work contributes to the understanding of fractional dynamics and provides a robust tool for simulating time-fractional systems.

引用次数: 0

Group size matters: Synergistic effects and reduced inequality in performance rankings

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-03-08 DOI: 10.1016/j.physa.2025.130496

Sandro M. Reia , Dieter Pfoser , Paulo R.A. Campos

Contemporary society often employs ranking systems to evaluate and reward individual performances based on meritocratic principles. However, these systems can have unintended consequences, impacting creativity and equality. This study presents a statistical analysis of a ranking performance model to examine the implications of these systems on social dynamics. Using a simulation approach, we explore the role of imitation in determining individuals’ success and how these dynamics influence overall group productivity and inequality. Our results unveil a synergistic effect of increasing group size and enhancing collective output while reducing individual performance inequality. Additionally, we investigate the social dynamics on random graphs, scale-free, and small-world networks to understand the influence of network topology, showing that connectivity within the group significantly influences both performance and inequality. Our results also demonstrate that high clustering combined with short path lengths reduces inequalities. These findings provide insights into optimizing ranking systems to balance merit-based recognition with the need for innovation and equality, suggesting strategies to enhance group synergy.

{"title":"Group size matters: Synergistic effects and reduced inequality in performance rankings","authors":"Sandro M. Reia , Dieter Pfoser , Paulo R.A. Campos","doi":"10.1016/j.physa.2025.130496","DOIUrl":"10.1016/j.physa.2025.130496","url":null,"abstract":"<div><div>Contemporary society often employs ranking systems to evaluate and reward individual performances based on meritocratic principles. However, these systems can have unintended consequences, impacting creativity and equality. This study presents a statistical analysis of a ranking performance model to examine the implications of these systems on social dynamics. Using a simulation approach, we explore the role of imitation in determining individuals’ success and how these dynamics influence overall group productivity and inequality. Our results unveil a synergistic effect of increasing group size and enhancing collective output while reducing individual performance inequality. Additionally, we investigate the social dynamics on random graphs, scale-free, and small-world networks to understand the influence of network topology, showing that connectivity within the group significantly influences both performance and inequality. Our results also demonstrate that high clustering combined with short path lengths reduces inequalities. These findings provide insights into optimizing ranking systems to balance merit-based recognition with the need for innovation and equality, suggesting strategies to enhance group synergy.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"665 ","pages":"Article 130496"},"PeriodicalIF":2.8,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Polarization in social media

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-03-08 DOI: 10.1016/j.physa.2025.130487

Congli Zhang , Xiaolei Wang , Yong Min , Shanqing Yu , Ye Wu , Qi Xuan , Chenbo Fu

The development of social media has changed the way in which information is consumed by the public. However, it also promotes polarization, especially among the more controversial topics. Furthermore, recommendation systems commonly used in social media have been shown to emerge an echo chamber effect, amplifying user groups’ polarization. Most of current studies focus on analyzing the generation of polarization phenomena on social media but rarely investigate how to quantify polarization. In this study, we construct user opinion networks and utilize random walk to quantify the polarization of related topics. The experiments on three real datasets, i.e., Bilibili, YouTube and Reddit, demonstrate that there is polarization in Bilibili and YouTube, especially in Bilibili. Our work complements quantitative measurements of polarization in social media platforms.

引用次数: 0

Anomalous diffusions of the composite processes: Generalized Lévy walk with jumps or rests

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-03-08 DOI: 10.1016/j.physa.2025.130503

X. Luo , X.J. Dai , Y.P. Li , J. Song , W.Y. Fan

Composite processes appear in a wide field, such as biology, ecology and natural science, so it is necessary to establish corresponding models to describe them. This manuscript builds up two two-mode random walk models which are the generalized Lévy walk with jumps (GLWJ) model and the generalized Lévy walk with rests (GLWR) model. The GLW processes in these two models will be interrupted by jumps and rest events, respectively, and move at a new velocity which is coupled with the motion time. The motion time and waiting time densities follow power-law forms and the jump density follows a Lévy form. We discuss the diffusive behaviors by analytically calculating the mean square displacement (MSD) and numerically simulating the probability density function (PDF). The results reveal their MSDs in both models exhibit crossover phenomena during the evolution processes. Meanwhile, the diffusion type of the GLWJ can be determined by either the largest exponent term or the largest pre-coefficient term. However, since the GLWR does not have random fluctuations from another mechanism that competes with the GLW. Without considering the scales of two time density functions, its diffusion type is determined only by the largest exponent team. The studies of these two models provide theoretical guidances for practical observations of some complex processes such as intermittent search strategies, human activity patterns in cities and cold atom fluctuations.

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引用次数: 0

Causality and dynamic volatility spillover between the cryptocurrency implied exchange rate and the official exchange rate 加密货币隐含汇率与官方汇率之间的因果关系和动态波动溢出效应

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-03-08 DOI: 10.1016/j.physa.2025.130513

Shiqun Ma, Chao Feng, Lijin Xiang, Zhichao Yin

This study adopts transfer entropy approach and Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) Connectedness approach to explore the information flows, namely causality, and volatility spillovers among different exchange rates, which encompasses both the official exchange rate and the cryptocurrency-based implied exchange rate of each sampled currency against the US dollar. The aims are to clarify the potential impact of the cryptocurrency implied exchange rate on the official exchange rate, and then to provide policymakers with decision-making references and practical insights. Empirical findings indicate that: (i) Significant interactions exist between the official and implied exchange rates for currencies. Predominantly, the implied exchange rate demonstrates a more substantial capacity for information transmission and exerts a stronger causal influence on the official exchange rate. (ii) The correlation between implied and official exchange rates is particularly strong and immediate within developed economies. Conversely, the influence of implied exchange rates on official rates in developing countries manifests in a more opaque manner. (iii) The volatility risk spillover from the implied exchange rate to the official exchange rate, via the indirect network pathways is notably more prevalent, exhibiting time-varying characteristics. (iv) The official exchange rates of developing countries’ currencies are the leading transmission hubs of the volatility risk of each currency’s implied exchange rate.

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引用次数: 0

Car following dynamics in mixed traffic flow of autonomous and human-driven vehicles: Complex networks approach

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-03-07 DOI: 10.1016/j.physa.2025.130519

Junjie Hu , Jaeyoung Jay Lee Ph.D.

Autonomous driving technologies have demonstrated exceptional performance in improving traffic operational efficiency and safety, contributing to the growing market penetration rate of autonomous vehicles (AVs). This study focuses on analyzing the interaction between AVs and human-driven vehicles (HVs) in mixed traffic flow, with an emphasis on the behavioral differences among various car-following (CF) vehicle pair types. While previous research has primarily relied on simulation and statistical methods to quantify the interaction between AVs and HVs, these approaches might overlook real-world driving nuances and fail to capture the dynamic changes in driving behavior. To address the limitations, we utilize a mixed traffic flow dataset (i.e., Lyft Level-5 Open Dataset), and apply a coarse-grained phase-space algorithm to model the dynamic changes in CF behavior. The interactions of different vehicle pairs are represented as directed, weighted complex networks. By analyzing network metrics, extracting core subgraphs, and calculating network similarities, the result indicates that the type of car following vehicle pair significantly influences following behavior. Moreover, changes in the leading or following vehicles within a platoon can lead to shifts in following behavior, and the introduction of AVs contributes positively to enhancing both the safety and efficiency of traffic flow. These network-based findings enrich the understanding of interactions between different vehicle types in mixed traffic flow and provide a solid foundation for designing mixed traffic flow control algorithms that account for vehicle type heterogeneity.

{"title":"Car following dynamics in mixed traffic flow of autonomous and human-driven vehicles: Complex networks approach","authors":"Junjie Hu , Jaeyoung Jay Lee Ph.D.","doi":"10.1016/j.physa.2025.130519","DOIUrl":"10.1016/j.physa.2025.130519","url":null,"abstract":"<div><div>Autonomous driving technologies have demonstrated exceptional performance in improving traffic operational efficiency and safety, contributing to the growing market penetration rate of autonomous vehicles (AVs). This study focuses on analyzing the interaction between AVs and human-driven vehicles (HVs) in mixed traffic flow, with an emphasis on the behavioral differences among various car-following (CF) vehicle pair types. While previous research has primarily relied on simulation and statistical methods to quantify the interaction between AVs and HVs, these approaches might overlook real-world driving nuances and fail to capture the dynamic changes in driving behavior. To address the limitations, we utilize a mixed traffic flow dataset (i.e., Lyft Level-5 Open Dataset), and apply a coarse-grained phase-space algorithm to model the dynamic changes in CF behavior. The interactions of different vehicle pairs are represented as directed, weighted complex networks. By analyzing network metrics, extracting core subgraphs, and calculating network similarities, the result indicates that the type of car following vehicle pair significantly influences following behavior. Moreover, changes in the leading or following vehicles within a platoon can lead to shifts in following behavior, and the introduction of AVs contributes positively to enhancing both the safety and efficiency of traffic flow. These network-based findings enrich the understanding of interactions between different vehicle types in mixed traffic flow and provide a solid foundation for designing mixed traffic flow control algorithms that account for vehicle type heterogeneity.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"665 ","pages":"Article 130519"},"PeriodicalIF":2.8,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A modified Hegselmann–Krause model for interacting voters and political parties

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-03-07 DOI: 10.1016/j.physa.2025.130490

Patrick Cahill, Georg A. Gottwald

The Hegselmann–Krause model is a prototypical model for opinion dynamics. It models the stochastic time evolution of an agent’s or voter’s opinion in response to the opinion of other like-minded agents. The Hegselmann–Krause model only considers the opinions of voters; we extend it here by incorporating the dynamics of political parties which influence and are influenced by the voters. We show in numerical simulations for 1- and 2-dimensional opinion spaces that, as for the original Hegselmann–Krause model, the modified model exhibits opinion cluster formation as well as a phase transition from disagreement to consensus. We provide an analytical sufficient condition for the formation of unanimous consensus in which voters and parties collapse to the same point in opinion space in the deterministic case. Using mean-field theory, we further derive an approximation for the critical noise strength delineating consensus from non-consensus in the stochastically driven modified Hegselmann–Krause model. We compare our analytical findings with simulations of the modified Hegselmann–Krause model.

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引用次数: 0

Co-authorship prediction method based on degree of gravity and article keywords similarity

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-03-06 DOI: 10.1016/j.physa.2025.130511

Herman Yuliansyah , Zulaiha Ali Othman , Azuraliza Abu Bakar

Link prediction is a technique for predicting future relationships among candidate node pairs. The co-authorship prediction measures the candidate by examining the unobserved node pairs using the link prediction technique. Previous studies have proposed co-authorship prediction and focused solely on using a topology or content articles to conduct the co-authorship prediction. However, many unobserved node pairs hinder the co-authorship prediction process. A new co-authorship prediction method is required by considering both topological information and research interest due to the authors collaborating to publish scientific papers based on research similarities, although still considering the network topology. The objective of this research is to propose a co-authorship prediction method based on a two-phase process: pruning candidate node pairs based on article content similarities to avoid a large number of candidate co-authors and predicting potential co-authors based on the Degree of Gravity for Link Prediction (DGLP) method. The proposed method is examined using the real-world co-authorship network and assessed using the area under the curve and the paired samples t-test to show a significant improvement. The experiment results show that combining DGLP, keyword extraction, and keyword similarities can help obtain the best performance and outperform the benchmark methods for co-authorship prediction in the unweighted network.

{"title":"Co-authorship prediction method based on degree of gravity and article keywords similarity","authors":"Herman Yuliansyah , Zulaiha Ali Othman , Azuraliza Abu Bakar","doi":"10.1016/j.physa.2025.130511","DOIUrl":"10.1016/j.physa.2025.130511","url":null,"abstract":"<div><div>Link prediction is a technique for predicting future relationships among candidate node pairs. The co-authorship prediction measures the candidate by examining the unobserved node pairs using the link prediction technique. Previous studies have proposed co-authorship prediction and focused solely on using a topology or content articles to conduct the co-authorship prediction. However, many unobserved node pairs hinder the co-authorship prediction process. A new co-authorship prediction method is required by considering both topological information and research interest due to the authors collaborating to publish scientific papers based on research similarities, although still considering the network topology. The objective of this research is to propose a co-authorship prediction method based on a two-phase process: pruning candidate node pairs based on article content similarities to avoid a large number of candidate co-authors and predicting potential co-authors based on the Degree of Gravity for Link Prediction (DGLP) method. The proposed method is examined using the real-world co-authorship network and assessed using the area under the curve and the paired samples t-test to show a significant improvement. The experiment results show that combining DGLP, keyword extraction, and keyword similarities can help obtain the best performance and outperform the benchmark methods for co-authorship prediction in the unweighted network.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"665 ","pages":"Article 130511"},"PeriodicalIF":2.8,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Physica A: Statistical Mechanics and its Applications

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