Poverty map inference is a critical area of research, with growing interest in both traditional and modern techniques, ranging from regression models to convolutional neural networks applied to tabular data, images, and networks. Despite extensive focus on the validation of training phases, the scrutiny of final predictions remains limited. Here, we compare the Relative Wealth Index (RWI) inferred by Chi et al. (2021) with the International Wealth Index (IWI) inferred by Lee and Braithwaite (2022) and Esp'in-Noboa et al. (2023) across six Sub-Saharan African countries. Our analysis focuses on identifying trends and discrepancies in wealth predictions over time. Our results show that the predictions by Chi et al. and Esp'in-Noboa et al. align with general GDP trends, with differences expected due to the distinct time-frames of the training sets. However, predictions by Lee and Braithwaite diverge significantly, indicating potential issues with the validity of the model. These discrepancies highlight the need for policymakers and stakeholders in Africa to rigorously audit models that predict wealth, especially those used for decision-making on the ground. These and other techniques require continuous verification and refinement to enhance their reliability and ensure that poverty alleviation strategies are well-founded.
贫困图推断是一个重要的研究领域，人们对传统和现代技术的兴趣与日俱增，从回归模型到应用于表格数据、图像和网络的卷积神经网络，不一而足。在此，我们将 Chi 等人（2021 年）推断的相对财富指数（RWI）与 Lee 和 Braithwaite（2022 年）以及 Esp'in-Noboa 等人（2023 年）在撒哈拉以南非洲六个国家推断的国际财富指数（IWI）进行比较。我们的分析重点是识别财富预测随时间变化的趋势和差异。我们的结果显示，Chi 等人和 Esp'in-Noboa 等人的预测与 GDP 的总体趋势一致，由于训练集的时间框架不同，预计会存在差异。这些差异突出表明，非洲的政策制定者和利益相关者需要严格审核预测财富的模型，尤其是用于实地决策的模型。这些技术和其他技术需要不断验证和完善，以提高其可靠性，确保扶贫战略有充分的依据。
{"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}
We propose a dynamical toy model of agents which possess a quantity and have an interaction radius depending on the amount of the quantity. They exchange the quantity with agents existing within their interaction radii. It is shown in the paper that the distribution of the quantity of agents is robustly governed by Zipf's law for a small density of agents independent of the number of agents and the type of interaction, despite the simplicity of the rules. The model can exhibit other power laws with different exponents and the Gaussian distributions. The difference in the mechanism underlying Zipf's law and other power laws are studied by mapping the systems into graphs and investigating quantities characterizing the mapped graph. Thus, this model suggests one of the origins of Zipf's law, i.e., the most common fundamental characteristics necessary for Zipf's law to appear.
{"title":"Dynamical toy model of interacting $N$ agents robustly exhibiting Zipf's law","authors":"Tohru Tashiro, Megumi Koshiishi, Tetsuo Deguchi","doi":"arxiv-2408.01674","DOIUrl":"https://doi.org/arxiv-2408.01674","url":null,"abstract":"We propose a dynamical toy model of agents which possess a quantity and have\u0000an interaction radius depending on the amount of the quantity. They exchange\u0000the quantity with agents existing within their interaction radii. It is shown\u0000in the paper that the distribution of the quantity of agents is robustly\u0000governed by Zipf's law for a small density of agents independent of the number\u0000of agents and the type of interaction, despite the simplicity of the rules. The\u0000model can exhibit other power laws with different exponents and the Gaussian\u0000distributions. The difference in the mechanism underlying Zipf's law and other\u0000power laws are studied by mapping the systems into graphs and investigating\u0000quantities characterizing the mapped graph. Thus, this model suggests one of\u0000the origins of Zipf's law, i.e., the most common fundamental characteristics\u0000necessary for Zipf's law to appear.","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":"141969459","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}
With the explosive growth of the Coronavirus Pandemic (COVID-19), misinformation on social media has developed into a global phenomenon with widespread and detrimental societal effects. Despite recent progress and efforts in detecting COVID-19 misinformation on social media networks, this task remains challenging due to the complexity, diversity, multi-modality, and high costs of fact-checking or annotation. In this research, we introduce a systematic and multidisciplinary agent-based modeling approach to limit the spread of COVID-19 misinformation and interpret the dynamic actions of users and communities in evolutionary online (or offline) social media networks. Our model was applied to a Twitter network associated with an armed protest demonstration against the COVID-19 lockdown in Michigan state in May, 2020. We implemented a one-median problem to categorize the Twitter network into six key communities (nodes) and identified information exchange (links) within the network. We measured the response time to COVID-19 misinformation spread in the network and employed a cybernetic organizational method to monitor the Twitter network. The overall misinformation mitigation strategy was evaluated, and agents were allocated to interact with the network based on the measured response time and feedback. The proposed model prioritized the communities based on the agents response times at the operational level. It then optimized agent allocation to limit the spread of COVID19 related misinformation from different communities, improved the information diffusion delay threshold to up to 3 minutes, and ultimately enhanced the mitigation process to reduce misinformation spread across the entire network.
{"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}
The functionality of an entity frequently necessitates the support of a group situated in another layer of the system. To unravel the profound impact of such group support on a system's resilience against cascading failures, we devise a framework comprising a double-layer interdependent hypergraph system, wherein nodes are capable of receiving support via hyperedges. Our central hypothesis posits that the failure may transcend to another layer when all support groups of each dependent node fail, thereby initiating a potentially iterative cascade across layers. Through rigorous analytical methods, we derive the critical threshold for the initial node survival probability that marks the second-order phase transition point. A salient discovery is that as the prevalence of dependent nodes escalates, the system dynamics shift from a second-order to a first-order phase transition. Notably, irrespective of the collapse pattern, systems characterized by scale-free hyperdegree distributions within both hypergraph layers consistently demonstrate superior robustness compared to those adhering to Poisson hyperdegree distributions. In summary, our research underscores the paramount significance of group support mechanisms and intricate network topologies in determining the resilience of interconnected systems against the propagation of cascading failures. By exploring the interplay between these factors, we have gained insights into how systems can be designed or optimized to mitigate the risk of widespread disruptions, ensuring their continued functionality and stability in the face of adverse events.
{"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}
Large Language Models are expressive tools that enable complex tasks of text understanding within Computational Social Science. Their versatility, while beneficial, poses a barrier for establishing standardized best practices within the field. To bring clarity on the values of different strategies, we present an overview of the performance of modern LLM-based classification methods on a benchmark of 23 social knowledge tasks. Our results point to three best practices: select models with larger vocabulary and pre-training corpora; avoid simple zero-shot in favor of AI-enhanced prompting; fine-tune on task-specific data, and consider more complex forms instruction-tuning on multiple datasets only when only training data is more abundant.
{"title":"Prompt Refinement or Fine-tuning? Best Practices for using LLMs in Computational Social Science Tasks","authors":"Anders Giovanni Møller, Luca Maria Aiello","doi":"arxiv-2408.01346","DOIUrl":"https://doi.org/arxiv-2408.01346","url":null,"abstract":"Large Language Models are expressive tools that enable complex tasks of text\u0000understanding within Computational Social Science. Their versatility, while\u0000beneficial, poses a barrier for establishing standardized best practices within\u0000the field. To bring clarity on the values of different strategies, we present\u0000an overview of the performance of modern LLM-based classification methods on a\u0000benchmark of 23 social knowledge tasks. Our results point to three best\u0000practices: select models with larger vocabulary and pre-training corpora; avoid\u0000simple zero-shot in favor of AI-enhanced prompting; fine-tune on task-specific\u0000data, and consider more complex forms instruction-tuning on multiple datasets\u0000only when only training data is more abundant.","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":"141945882","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}
Luca Pappalardo, Ed Manley, Vedran Sekara, Laura Alessandretti
We provide a brief review of human mobility science and present three key areas where we expect to see substantial advancements. We start from the mind and discuss the need to better understand how spatial cognition shapes mobility patterns. We then move to societies and argue the importance of better understanding new forms of transportation. We conclude by discussing how algorithms shape mobility behaviour and provide useful tools for modellers. Finally, we discuss how progress in these research directions may help us address some of the challenges our society faces today.
{"title":"Future Directions in Human Mobility Science","authors":"Luca Pappalardo, Ed Manley, Vedran Sekara, Laura Alessandretti","doi":"arxiv-2408.00702","DOIUrl":"https://doi.org/arxiv-2408.00702","url":null,"abstract":"We provide a brief review of human mobility science and present three key\u0000areas where we expect to see substantial advancements. We start from the mind\u0000and discuss the need to better understand how spatial cognition shapes mobility\u0000patterns. We then move to societies and argue the importance of better\u0000understanding new forms of transportation. We conclude by discussing how\u0000algorithms shape mobility behaviour and provide useful tools for modellers.\u0000Finally, we discuss how progress in these research directions may help us\u0000address some of the challenges our society faces today.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141883743","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}
A fundamental premise of statistical physics is that the particles in a physical system are interchangeable, and hence the state of each specific component is representative of the system as a whole. This assumption breaks down for complex networks, in which nodes may be extremely diverse, and no single component can truly represent the state of the entire system. It seems, therefore, that to observe the dynamics of social, biological or technological networks, one must extract the dynamic states of a large number of nodes -- a task that is often practically prohibitive. To overcome this challenge, we use machine learning techniques to detect the network's sentinel nodes, a set of network components whose combined states can help approximate the average dynamics of the entire network. The method allows us to assess the state of a large complex system by tracking just a small number of carefully selected nodes. The resulting sentinel node set offers a natural probe by which to practically observe complex network dynamics.
{"title":"Observing network dynamics through sentinel nodes","authors":"Neil G. MacLaren, Baruch Barzel, Naoki Masuda","doi":"arxiv-2408.00045","DOIUrl":"https://doi.org/arxiv-2408.00045","url":null,"abstract":"A fundamental premise of statistical physics is that the particles in a\u0000physical system are interchangeable, and hence the state of each specific\u0000component is representative of the system as a whole. This assumption breaks\u0000down for complex networks, in which nodes may be extremely diverse, and no\u0000single component can truly represent the state of the entire system. It seems,\u0000therefore, that to observe the dynamics of social, biological or technological\u0000networks, one must extract the dynamic states of a large number of nodes -- a\u0000task that is often practically prohibitive. To overcome this challenge, we use\u0000machine learning techniques to detect the network's sentinel nodes, a set of\u0000network components whose combined states can help approximate the average\u0000dynamics of the entire network. The method allows us to assess the state of a\u0000large complex system by tracking just a small number of carefully selected\u0000nodes. The resulting sentinel node set offers a natural probe by which to\u0000practically observe complex network dynamics.","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":"141883746","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}
Competition for a limited resource is the hallmark of many complex systems, and often, that resource turns out to be the physical space itself. In this work, we study a novel model designed to elucidate the dynamics and emergence in complex adaptive systems in which agents compete for some spatially spread resource. Specifically, in the model, the dynamics result from the agents trying to position themselves in the quest to avoid physical crowding experienced locally. We characterize in detail the dependence of the emergent behavior of the model on the population density of the system and the individual-level agent traits such as the extent of space an agent considers as her neighborhood, the limit of occupation density one tolerates within that neighborhood, and the information accessibility of the agents about neighborhood occupancy. We show that the efficiency with which the agents utilize the physical space shows transitions at two values of densities. The first of these transitions demarcates efficient and inefficient phases of the system, and the second one signifies the density at which the inefficiency is maximum. We show that the variation of inefficiency with respect to the information accessible to the agents shows opposing behavior above and below this second transition density. We also look into the inequality of resource sharing in the model and show that although inefficiency can be a non-monotonic function of information depending upon the parameters of the model, inequality, in general, decreases with information. Our study sheds light on the role of competition, spatial constraints, and agent traits within complex adaptive systems, offering insights into their emergent behaviors.
{"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}
One of the core risk management tasks is to identify hidden high-risky states that may lead to system breakdown, which can provide valuable early warning knowledge. However, due to high dimensionality and nonlinear interaction embedded in large-scale complex systems like urban traffic, it remains challenging to identify hidden high-risky states from huge system state space where over 99% of possible system states are not yet visited in empirical data. Based on maximum entropy model, we infer the underlying interaction network from complicated dynamical processes of urban traffic, and construct system energy landscape. In this way, we can locate hidden high-risky states that have never been observed from real data. These states can serve as risk signals with high probability of entering hazardous minima in energy landscape, which lead to huge recovery cost. Our finding might provide insights for complex system risk management.
{"title":"Hidden high-risky states identification from routine urban traffic","authors":"Shiyan Liu, Mingyang Bai, Shengmin Guo, Jianxi Gao, Huijun Sun, Ziyou Gao, Daqing Li","doi":"arxiv-2407.20478","DOIUrl":"https://doi.org/arxiv-2407.20478","url":null,"abstract":"One of the core risk management tasks is to identify hidden high-risky states\u0000that may lead to system breakdown, which can provide valuable early warning\u0000knowledge. However, due to high dimensionality and nonlinear interaction\u0000embedded in large-scale complex systems like urban traffic, it remains\u0000challenging to identify hidden high-risky states from huge system state space\u0000where over 99% of possible system states are not yet visited in empirical data.\u0000Based on maximum entropy model, we infer the underlying interaction network\u0000from complicated dynamical processes of urban traffic, and construct system\u0000energy landscape. In this way, we can locate hidden high-risky states that have\u0000never been observed from real data. These states can serve as risk signals with\u0000high probability of entering hazardous minima in energy landscape, which lead\u0000to huge recovery cost. Our finding might provide insights for complex system\u0000risk management.","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":"141873439","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}
Caspar A. S. Pouw, Geert G. M. van der Vleuten, Alessandro Corbetta, Federico Toschi
Pedestrian crowds encompass a complex interplay of intentional movements aimed at reaching specific destinations, fluctuations due to personal and interpersonal variability, and interactions with each other and the environment. Previous work showed the effectiveness of Langevin-like equations in capturing the statistical properties of pedestrian dynamics in simple settings, such as almost straight trajectories. However, modeling more complex dynamics, e.g. when multiple routes and origin-destinations are involved, remains a significant challenge. In this work, we introduce a novel and generic framework to describe the dynamics of pedestrians in any geometric setting, significantly extending previous works. Our model is based on Langevin dynamics with two timescales. The fast timescale corresponds to the stochastic fluctuations present when a pedestrian is walking. The slow timescale is associated with the dynamics that a pedestrian plans to follow, thus a smoother path. Employing a data-driven approach inspired by statistical field theories, we learn the complex potentials directly from the data, namely a high-statistics database of real-life pedestrian trajectories. This approach makes the model generic as the potentials can be read from any trajectory data set and the underlying Langevin structure enables physics-based insights. We validate our model through a comprehensive statistical analysis, comparing simulated trajectories with actual pedestrian measurements across five complementary settings, including a real-life train platform scenario, underscoring its practical societal relevance. We show that our model effectively captures fluctuation statistics in pedestrian motion. Beyond providing fundamental insights and predictive capabilities in pedestrian dynamics, our model could be used to investigate generic active dynamics such as vehicular traffic and collective animal behavior.
{"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}
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