arXiv - PHYS - Physics and Society最新文献_第10页

A Comparative Analysis of Wealth Index Predictions in Africa between three Multi-Source Inference Models 三种多源推理模型对非洲财富指数预测的比较分析

arXiv - PHYS - Physics and Society Pub Date : 2024-08-03 DOI: arxiv-2408.01631

Márton Karsai, János Kertész, Lisette Espín-Noboa

{"title":"A Comparative Analysis of Wealth Index Predictions in Africa between three Multi-Source Inference Models","authors":"Márton Karsai, János Kertész, Lisette Espín-Noboa","doi":"arxiv-2408.01631","DOIUrl":"https://doi.org/arxiv-2408.01631","url":null,"abstract":"Poverty map inference is a critical area of research, with growing interest\u0000in both traditional and modern techniques, ranging from regression models to\u0000convolutional neural networks applied to tabular data, images, and networks.\u0000Despite extensive focus on the validation of training phases, the scrutiny of\u0000final predictions remains limited. Here, we compare the Relative Wealth Index\u0000(RWI) inferred by Chi et al. (2021) with the International Wealth Index (IWI)\u0000inferred by Lee and Braithwaite (2022) and Esp'in-Noboa et al. (2023) across\u0000six Sub-Saharan African countries. Our analysis focuses on identifying trends\u0000and discrepancies in wealth predictions over time. Our results show that the\u0000predictions by Chi et al. and Esp'in-Noboa et al. align with general GDP\u0000trends, with differences expected due to the distinct time-frames of the\u0000training sets. However, predictions by Lee and Braithwaite diverge\u0000significantly, indicating potential issues with the validity of the model.\u0000These discrepancies highlight the need for policymakers and stakeholders in\u0000Africa to rigorously audit models that predict wealth, especially those used\u0000for decision-making on the ground. These and other techniques require\u0000continuous verification and refinement to enhance their reliability and ensure\u0000that poverty alleviation strategies are well-founded.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamical toy model of interacting $N$ agents robustly exhibiting Zipf's law 稳健显示齐普夫定律的 N$ 个相互作用代理的动态玩具模型

arXiv - PHYS - Physics and Society Pub Date : 2024-08-03 DOI: arxiv-2408.01674

Tohru Tashiro, Megumi Koshiishi, Tetsuo Deguchi

引用次数: 0

Reducing COVID-19 Misinformation Spread by Introducing Information Diffusion Delay Using Agent-based Modeling 利用代理建模引入信息扩散延迟，减少 COVID-19 误报传播

arXiv - PHYS - Physics and Society Pub Date : 2024-08-02 DOI: arxiv-2408.01549

Mustafa Alassad, Nitin Agarwal

{"title":"Reducing COVID-19 Misinformation Spread by Introducing Information Diffusion Delay Using Agent-based Modeling","authors":"Mustafa Alassad, Nitin Agarwal","doi":"arxiv-2408.01549","DOIUrl":"https://doi.org/arxiv-2408.01549","url":null,"abstract":"With the explosive growth of the Coronavirus Pandemic (COVID-19),\u0000misinformation on social media has developed into a global phenomenon with\u0000widespread and detrimental societal effects. Despite recent progress and\u0000efforts in detecting COVID-19 misinformation on social media networks, this\u0000task remains challenging due to the complexity, diversity, multi-modality, and\u0000high costs of fact-checking or annotation. In this research, we introduce a\u0000systematic and multidisciplinary agent-based modeling approach to limit the\u0000spread of COVID-19 misinformation and interpret the dynamic actions of users\u0000and communities in evolutionary online (or offline) social media networks. Our\u0000model was applied to a Twitter network associated with an armed protest\u0000demonstration against the COVID-19 lockdown in Michigan state in May, 2020. We\u0000implemented a one-median problem to categorize the Twitter network into six key\u0000communities (nodes) and identified information exchange (links) within the\u0000network. We measured the response time to COVID-19 misinformation spread in the\u0000network and employed a cybernetic organizational method to monitor the Twitter\u0000network. The overall misinformation mitigation strategy was evaluated, and\u0000agents were allocated to interact with the network based on the measured\u0000response time and feedback. The proposed model prioritized the communities\u0000based on the agents response times at the operational level. It then optimized\u0000agent allocation to limit the spread of COVID19 related misinformation from\u0000different communities, improved the information diffusion delay threshold to up\u0000to 3 minutes, and ultimately enhanced the mitigation process to reduce\u0000misinformation spread across the entire network.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cascading failures with group support in interdependent hypergraphs 相互依存超图中具有群体支持的级联故障

arXiv - PHYS - Physics and Society Pub Date : 2024-08-02 DOI: arxiv-2408.01172

Lei Chen, Chunxiao Jia, Run-Ran Liu, Fanyuan Meng

{"title":"Cascading failures with group support in interdependent hypergraphs","authors":"Lei Chen, Chunxiao Jia, Run-Ran Liu, Fanyuan Meng","doi":"arxiv-2408.01172","DOIUrl":"https://doi.org/arxiv-2408.01172","url":null,"abstract":"The functionality of an entity frequently necessitates the support of a group\u0000situated in another layer of the system. To unravel the profound impact of such\u0000group support on a system's resilience against cascading failures, we devise a\u0000framework comprising a double-layer interdependent hypergraph system, wherein\u0000nodes are capable of receiving support via hyperedges. Our central hypothesis\u0000posits that the failure may transcend to another layer when all support groups\u0000of each dependent node fail, thereby initiating a potentially iterative cascade\u0000across layers. Through rigorous analytical methods, we derive the critical\u0000threshold for the initial node survival probability that marks the second-order\u0000phase transition point. A salient discovery is that as the prevalence of\u0000dependent nodes escalates, the system dynamics shift from a second-order to a\u0000first-order phase transition. Notably, irrespective of the collapse pattern,\u0000systems characterized by scale-free hyperdegree distributions within both\u0000hypergraph layers consistently demonstrate superior robustness compared to\u0000those adhering to Poisson hyperdegree distributions. In summary, our research\u0000underscores the paramount significance of group support mechanisms and\u0000intricate network topologies in determining the resilience of interconnected\u0000systems against the propagation of cascading failures. By exploring the\u0000interplay between these factors, we have gained insights into how systems can\u0000be designed or optimized to mitigate the risk of widespread disruptions,\u0000ensuring their continued functionality and stability in the face of adverse\u0000events.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prompt Refinement or Fine-tuning? Best Practices for using LLMs in Computational Social Science Tasks 及时改进还是微调？在计算社会科学任务中使用 LLM 的最佳实践

arXiv - PHYS - Physics and Society Pub Date : 2024-08-02 DOI: arxiv-2408.01346

Anders Giovanni Møller, Luca Maria Aiello

引用次数: 0

Future Directions in Human Mobility Science 人类流动科学的未来发展方向

arXiv - PHYS - Physics and Society Pub Date : 2024-08-01 DOI: arxiv-2408.00702

Luca Pappalardo, Ed Manley, Vedran Sekara, Laura Alessandretti

引用次数: 0

Observing network dynamics through sentinel nodes 通过哨兵节点观察网络动态

arXiv - PHYS - Physics and Society Pub Date : 2024-07-31 DOI: arxiv-2408.00045

Neil G. MacLaren, Baruch Barzel, Naoki Masuda

引用次数: 0

Modelling competition for space: Emergent inefficiency and inequality due to spatial self-organization among a group of crowd-avoiding agents 空间竞争建模：一群避开人群的代理之间的空间自组织导致的新出现的低效率和不平等现象

arXiv - PHYS - Physics and Society Pub Date : 2024-07-31 DOI: arxiv-2407.21537

Ann Mary Mathew, V Sasidevan

{"title":"Modelling competition for space: Emergent inefficiency and inequality due to spatial self-organization among a group of crowd-avoiding agents","authors":"Ann Mary Mathew, V Sasidevan","doi":"arxiv-2407.21537","DOIUrl":"https://doi.org/arxiv-2407.21537","url":null,"abstract":"Competition for a limited resource is the hallmark of many complex systems,\u0000and often, that resource turns out to be the physical space itself. In this\u0000work, we study a novel model designed to elucidate the dynamics and emergence\u0000in complex adaptive systems in which agents compete for some spatially spread\u0000resource. Specifically, in the model, the dynamics result from the agents\u0000trying to position themselves in the quest to avoid physical crowding\u0000experienced locally. We characterize in detail the dependence of the emergent\u0000behavior of the model on the population density of the system and the\u0000individual-level agent traits such as the extent of space an agent considers as\u0000her neighborhood, the limit of occupation density one tolerates within that\u0000neighborhood, and the information accessibility of the agents about\u0000neighborhood occupancy. We show that the efficiency with which the agents\u0000utilize the physical space shows transitions at two values of densities. The\u0000first of these transitions demarcates efficient and inefficient phases of the\u0000system, and the second one signifies the density at which the inefficiency is\u0000maximum. We show that the variation of inefficiency with respect to the\u0000information accessible to the agents shows opposing behavior above and below\u0000this second transition density. We also look into the inequality of resource\u0000sharing in the model and show that although inefficiency can be a non-monotonic\u0000function of information depending upon the parameters of the model, inequality,\u0000in general, decreases with information. Our study sheds light on the role of\u0000competition, spatial constraints, and agent traits within complex adaptive\u0000systems, offering insights into their emergent behaviors.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141872505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hidden high-risky states identification from routine urban traffic 从常规城市交通中识别隐藏的高风险状态

arXiv - PHYS - Physics and Society Pub Date : 2024-07-30 DOI: arxiv-2407.20478

Shiyan Liu, Mingyang Bai, Shengmin Guo, Jianxi Gao, Huijun Sun, Ziyou Gao, Daqing Li

引用次数: 0

Data-driven physics-based modeling of pedestrian dynamics 基于数据的行人动力学物理建模

arXiv - PHYS - Physics and Society Pub Date : 2024-07-30 DOI: arxiv-2407.20794

Caspar A. S. Pouw, Geert G. M. van der Vleuten, Alessandro Corbetta, Federico Toschi

{"title":"Data-driven physics-based modeling of pedestrian dynamics","authors":"Caspar A. S. Pouw, Geert G. M. van der Vleuten, Alessandro Corbetta, Federico Toschi","doi":"arxiv-2407.20794","DOIUrl":"https://doi.org/arxiv-2407.20794","url":null,"abstract":"Pedestrian crowds encompass a complex interplay of intentional movements\u0000aimed at reaching specific destinations, fluctuations due to personal and\u0000interpersonal variability, and interactions with each other and the\u0000environment. Previous work showed the effectiveness of Langevin-like equations\u0000in capturing the statistical properties of pedestrian dynamics in simple\u0000settings, such as almost straight trajectories. However, modeling more complex\u0000dynamics, e.g. when multiple routes and origin-destinations are involved,\u0000remains a significant challenge. In this work, we introduce a novel and generic\u0000framework to describe the dynamics of pedestrians in any geometric setting,\u0000significantly extending previous works. Our model is based on Langevin dynamics\u0000with two timescales. The fast timescale corresponds to the stochastic\u0000fluctuations present when a pedestrian is walking. The slow timescale is\u0000associated with the dynamics that a pedestrian plans to follow, thus a smoother\u0000path. Employing a data-driven approach inspired by statistical field theories,\u0000we learn the complex potentials directly from the data, namely a\u0000high-statistics database of real-life pedestrian trajectories. This approach\u0000makes the model generic as the potentials can be read from any trajectory data\u0000set and the underlying Langevin structure enables physics-based insights. We\u0000validate our model through a comprehensive statistical analysis, comparing\u0000simulated trajectories with actual pedestrian measurements across five\u0000complementary settings, including a real-life train platform scenario,\u0000underscoring its practical societal relevance. We show that our model\u0000effectively captures fluctuation statistics in pedestrian motion. Beyond\u0000providing fundamental insights and predictive capabilities in pedestrian\u0000dynamics, our model could be used to investigate generic active dynamics such\u0000as vehicular traffic and collective animal behavior.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141872521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0