Ensuring content compliance with community guidelines is crucial for maintaining healthy online social environments. However, traditional human-based compliance checking struggles with scaling due to the increasing volume of user-generated content and a limited number of moderators. Recent advancements in Natural Language Understanding demonstrated by Large Language Models unlock new opportunities for automated content compliance verification. This work evaluates six AI-agents built on Open-LLMs for automated rule compliance checking in Decentralized Social Networks, a challenging environment due to heterogeneous community scopes and rules. Analyzing over 50,000 posts from hundreds of Mastodon servers, we find that AI-agents effectively detect non-compliant content, grasp linguistic subtleties, and adapt to diverse community contexts. Most agents also show high inter-rater reliability and consistency in score justification and suggestions for compliance. Human-based evaluation with domain experts confirmed the agents' reliability and usefulness, rendering them promising tools for semi-automated or human-in-the-loop content moderation systems.
{"title":"Safeguarding Decentralized Social Media: LLM Agents for Automating Community Rule Compliance","authors":"Lucio La Cava, Andrea Tagarelli","doi":"arxiv-2409.08963","DOIUrl":"https://doi.org/arxiv-2409.08963","url":null,"abstract":"Ensuring content compliance with community guidelines is crucial for\u0000maintaining healthy online social environments. However, traditional\u0000human-based compliance checking struggles with scaling due to the increasing\u0000volume of user-generated content and a limited number of moderators. Recent\u0000advancements in Natural Language Understanding demonstrated by Large Language\u0000Models unlock new opportunities for automated content compliance verification.\u0000This work evaluates six AI-agents built on Open-LLMs for automated rule\u0000compliance checking in Decentralized Social Networks, a challenging environment\u0000due to heterogeneous community scopes and rules. Analyzing over 50,000 posts\u0000from hundreds of Mastodon servers, we find that AI-agents effectively detect\u0000non-compliant content, grasp linguistic subtleties, and adapt to diverse\u0000community contexts. Most agents also show high inter-rater reliability and\u0000consistency in score justification and suggestions for compliance. Human-based\u0000evaluation with domain experts confirmed the agents' reliability and\u0000usefulness, rendering them promising tools for semi-automated or\u0000human-in-the-loop content moderation systems.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249672","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}
Nazgol Tabasi, Mohammad Fereshtehpour, Bardia Roghani
Climate change and rapid urbanization have led to more frequent and severe flooding, causing significant damage. The existing literature on flood risk encompasses a variety of dimensions, such as physical, economic, social, political, environmental, infrastructural, and managerial aspects. This paper aims to provide an extensive review of proposed conceptual frameworks and their components used in flood risk assessment. For this purpose, Initially, conceptual frameworks were extracted to configure the components of flood risk including hazard, vulnerability, exposure, resilience, and susceptibility. Subsequently, a comprehensive set of criteria from the literature were identified, addressing risk components. In this paper, the risk conceptual framework is defined by the intersection of vulnerability and hazard. Vulnerability, shaped by exposure and susceptibility, can be reduced by enhancing resiliency, which includes coping and adaptive capacities. In total, 102 criteria/subcriteria were identified and classified into three hierarchical structures of hazard, susceptibility, and resilience. Finally, flood risk assessment methods were reviewed, with an emphasis on their applicability and characteristics. The review highlighted the strengths and limitations of various methods, providing a comprehensive overview of their suitability for different scenarios. The outcomes of this review could serve as a valuable reference for professionals involved in flood risk assessment, aiding in the identification of the most appropriate risk concepts, assessment criteria, and suitable methods for quantification based on the specific study area and data availability.
{"title":"A Review on Flood Risk Conceptual Frameworks and Development of Hierarchical Structures for Assessment Criteria","authors":"Nazgol Tabasi, Mohammad Fereshtehpour, Bardia Roghani","doi":"arxiv-2409.08803","DOIUrl":"https://doi.org/arxiv-2409.08803","url":null,"abstract":"Climate change and rapid urbanization have led to more frequent and severe\u0000flooding, causing significant damage. The existing literature on flood risk\u0000encompasses a variety of dimensions, such as physical, economic, social,\u0000political, environmental, infrastructural, and managerial aspects. This paper\u0000aims to provide an extensive review of proposed conceptual frameworks and their\u0000components used in flood risk assessment. For this purpose, Initially,\u0000conceptual frameworks were extracted to configure the components of flood risk\u0000including hazard, vulnerability, exposure, resilience, and susceptibility.\u0000Subsequently, a comprehensive set of criteria from the literature were\u0000identified, addressing risk components. In this paper, the risk conceptual\u0000framework is defined by the intersection of vulnerability and hazard.\u0000Vulnerability, shaped by exposure and susceptibility, can be reduced by\u0000enhancing resiliency, which includes coping and adaptive capacities. In total,\u0000102 criteria/subcriteria were identified and classified into three hierarchical\u0000structures of hazard, susceptibility, and resilience. Finally, flood risk\u0000assessment methods were reviewed, with an emphasis on their applicability and\u0000characteristics. The review highlighted the strengths and limitations of\u0000various methods, providing a comprehensive overview of their suitability for\u0000different scenarios. The outcomes of this review could serve as a valuable\u0000reference for professionals involved in flood risk assessment, aiding in the\u0000identification of the most appropriate risk concepts, assessment criteria, and\u0000suitable methods for quantification based on the specific study area and data\u0000availability.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249645","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}
Candida M. Greco, Lorenzo Zangari, Davide Picca, Andrea Tagarelli
The way media reports on legal cases can significantly shape public opinion, often embedding subtle biases that influence societal views on justice and morality. Analyzing these biases requires a holistic approach that captures the emotional tone, moral framing, and specific events within the narratives. In this work we introduce E2MoCase, a novel dataset designed to facilitate the integrated analysis of emotions, moral values, and events within legal narratives and media coverage. By leveraging advanced models for emotion detection, moral value identification, and event extraction, E2MoCase offers a multi-dimensional perspective on how legal cases are portrayed in news articles.
{"title":"E2MoCase: A Dataset for Emotional, Event and Moral Observations in News Articles on High-impact Legal Cases","authors":"Candida M. Greco, Lorenzo Zangari, Davide Picca, Andrea Tagarelli","doi":"arxiv-2409.09001","DOIUrl":"https://doi.org/arxiv-2409.09001","url":null,"abstract":"The way media reports on legal cases can significantly shape public opinion,\u0000often embedding subtle biases that influence societal views on justice and\u0000morality. Analyzing these biases requires a holistic approach that captures the\u0000emotional tone, moral framing, and specific events within the narratives. In\u0000this work we introduce E2MoCase, a novel dataset designed to facilitate the\u0000integrated analysis of emotions, moral values, and events within legal\u0000narratives and media coverage. By leveraging advanced models for emotion\u0000detection, moral value identification, and event extraction, E2MoCase offers a\u0000multi-dimensional perspective on how legal cases are portrayed in news\u0000articles.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249671","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 self-similarity of complex systems has been studied intensely across different domains due to its potential applications in system modeling, complexity analysis, etc., as well as for deep theoretical interest. Existing studies rely on scale transformations conceptualized over either a definite geometric structure of the system (very often realized as length-scale transformations) or purely temporal scale transformations. However, many physical and social systems are observed as temporal interactions among agents without any definitive geometry. Yet, one can imagine the existence of an underlying notion of distance as the interactions are mostly localized. Analysing only the time-scale transformations over such systems would uncover only a limited aspect of the complexity. In this work, we propose a novel technique of scale transformation that dissects temporal interaction networks under spatio-temporal scales, namely, flow scales. Upon experimenting with multiple social and biological interaction networks, we find that many of them possess a finite fractal dimension under flow-scale transformation. Finally, we relate the emergence of flow-scale self-similarity to the latent geometry of such networks. We observe strong evidence that justifies the assumption of an underlying, variable-curvature hyperbolic geometry that induces self-similarity of temporal interaction networks. Our work bears implications for modeling temporal interaction networks at different scales and uncovering their latent geometric structures.
{"title":"Self-similarity of temporal interaction networks arises from hyperbolic geometry with time-varying curvature","authors":"Subhabrata Dutta, Dipankar Das, Tanmoy Chakraborty","doi":"arxiv-2409.07733","DOIUrl":"https://doi.org/arxiv-2409.07733","url":null,"abstract":"The self-similarity of complex systems has been studied intensely across\u0000different domains due to its potential applications in system modeling,\u0000complexity analysis, etc., as well as for deep theoretical interest. Existing\u0000studies rely on scale transformations conceptualized over either a definite\u0000geometric structure of the system (very often realized as length-scale\u0000transformations) or purely temporal scale transformations. However, many\u0000physical and social systems are observed as temporal interactions among agents\u0000without any definitive geometry. Yet, one can imagine the existence of an\u0000underlying notion of distance as the interactions are mostly localized.\u0000Analysing only the time-scale transformations over such systems would uncover\u0000only a limited aspect of the complexity. In this work, we propose a novel\u0000technique of scale transformation that dissects temporal interaction networks\u0000under spatio-temporal scales, namely, flow scales. Upon experimenting with\u0000multiple social and biological interaction networks, we find that many of them\u0000possess a finite fractal dimension under flow-scale transformation. Finally, we\u0000relate the emergence of flow-scale self-similarity to the latent geometry of\u0000such networks. We observe strong evidence that justifies the assumption of an\u0000underlying, variable-curvature hyperbolic geometry that induces self-similarity\u0000of temporal interaction networks. Our work bears implications for modeling\u0000temporal interaction networks at different scales and uncovering their latent\u0000geometric structures.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220922","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 review measures of street network structure proposed in the recent literature, establish their relevance to practice, and identify open challenges facing researchers. These measures' empirical values vary substantially across world regions and development eras, indicating street networks' geometric and topological heterogeneity.
{"title":"A review of the structure of street networks","authors":"Marc Barthelemy, Geoff Boeing","doi":"arxiv-2409.08016","DOIUrl":"https://doi.org/arxiv-2409.08016","url":null,"abstract":"We review measures of street network structure proposed in the recent\u0000literature, establish their relevance to practice, and identify open challenges\u0000facing researchers. These measures' empirical values vary substantially across\u0000world regions and development eras, indicating street networks' geometric and\u0000topological heterogeneity.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220940","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 network backbone provides a useful sparse representation of a weighted network by keeping only its most important links, permitting a range of computational speedups and simplifying complex network visualizations. There are many possible criteria for a link to be considered important, and hence many methods have been developed for the task of network backboning for graph sparsification. These methods can be classified as global or local in nature depending on whether they evaluate the importance of an edge in the context of the whole network or an individual node neighborhood. A key limitation of existing network backboning methods is that they either artificially restrict the topology of the backbone to take a specific form (e.g. a tree) or they require the specification of a free parameter (e.g. a significance level) that determines the number of edges to keep in the backbone. Here we develop a completely nonparametric framework for inferring the backbone of a weighted network that overcomes these limitations by automatically selecting the optimal number of edges to retain in the backbone using the Minimum Description Length (MDL) principle from information theory. We develop two encoding schemes that serve as objective functions for global and local network backbones, as well as efficient optimization algorithms to identify the optimal backbones according to these objectives with runtime complexity log-linear in the number of edges. We show that the proposed framework is generalizable to any discrete weight distribution on the edges using a maximum a posteriori (MAP) estimation procedure with an asymptotically equivalent Bayesian generative model of the backbone. We compare the proposed method with existing methods in a range of tasks on real and synthetic networks.
{"title":"Fast nonparametric inference of network backbones for graph sparsification","authors":"Alec Kirkley","doi":"arxiv-2409.06417","DOIUrl":"https://doi.org/arxiv-2409.06417","url":null,"abstract":"A network backbone provides a useful sparse representation of a weighted\u0000network by keeping only its most important links, permitting a range of\u0000computational speedups and simplifying complex network visualizations. There\u0000are many possible criteria for a link to be considered important, and hence\u0000many methods have been developed for the task of network backboning for graph\u0000sparsification. These methods can be classified as global or local in nature\u0000depending on whether they evaluate the importance of an edge in the context of\u0000the whole network or an individual node neighborhood. A key limitation of\u0000existing network backboning methods is that they either artificially restrict\u0000the topology of the backbone to take a specific form (e.g. a tree) or they\u0000require the specification of a free parameter (e.g. a significance level) that\u0000determines the number of edges to keep in the backbone. Here we develop a\u0000completely nonparametric framework for inferring the backbone of a weighted\u0000network that overcomes these limitations by automatically selecting the optimal\u0000number of edges to retain in the backbone using the Minimum Description Length\u0000(MDL) principle from information theory. We develop two encoding schemes that\u0000serve as objective functions for global and local network backbones, as well as\u0000efficient optimization algorithms to identify the optimal backbones according\u0000to these objectives with runtime complexity log-linear in the number of edges.\u0000We show that the proposed framework is generalizable to any discrete weight\u0000distribution on the edges using a maximum a posteriori (MAP) estimation\u0000procedure with an asymptotically equivalent Bayesian generative model of the\u0000backbone. We compare the proposed method with existing methods in a range of\u0000tasks on real and synthetic networks.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220927","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}
José Alejandro Rojas-Venegas, Pablo Gallarta-Sáenz, Rafael G. Hurtado, Jesús Gómez-Gardeñes, David Soriano-Paños
Obtaining accurate forecasts for the evolution of epidemic outbreaks from deterministic compartmental models represents a major theoretical challenge. Recently, it has been shown that these models typically exhibit trajectories' degeneracy, as different sets of epidemiological parameters yield comparable predictions at early stages of the outbreak but disparate future epidemic scenarios. Here we use the Doi-Peliti approach and extend the classical deterministic SIS and SIR models to a quantum-like formalism to explore whether the uncertainty of epidemic forecasts is also shaped by the stochastic nature of epidemic processes. This approach allows getting a probabilistic ensemble of trajectories, revealing that epidemic uncertainty is not uniform across time, being maximal around the epidemic peak and vanishing at both early and very late stages of the outbreak. Our results therefore show that, independently of the models' complexity, the stochasticity of contagion and recover processes poses a natural constraint for the uncertainty of epidemic forecasts.
最近的研究表明，这些模型通常表现出轨迹退化（trajectories'degeneracy），因为不同的流行病学参数集在流行病爆发的早期阶段会产生相似的预测结果，但未来的流行病情况却各不相同。在此，我们采用 Doi-Peliti 方法，将经典的确定性 SIS 和 SIR 模型扩展到类似量子的形式，以探讨流行病预测的不确定性是否也受流行病过程随机性质的影响。通过这种方法，我们可以得到一组概率轨迹，揭示出疫情的不确定性在不同时间段并不一致，在疫情高峰期前后最大，而在疫情爆发的早期和晚期阶段都会消失。因此，我们的研究结果表明，与模型的复杂性无关，传染和恢复过程的随机性对流行病预测的不确定性构成了天然的约束。
{"title":"Quantum-like approaches unveil the intrinsic limits of predictability in compartmental models","authors":"José Alejandro Rojas-Venegas, Pablo Gallarta-Sáenz, Rafael G. Hurtado, Jesús Gómez-Gardeñes, David Soriano-Paños","doi":"arxiv-2409.06438","DOIUrl":"https://doi.org/arxiv-2409.06438","url":null,"abstract":"Obtaining accurate forecasts for the evolution of epidemic outbreaks from\u0000deterministic compartmental models represents a major theoretical challenge.\u0000Recently, it has been shown that these models typically exhibit trajectories'\u0000degeneracy, as different sets of epidemiological parameters yield comparable\u0000predictions at early stages of the outbreak but disparate future epidemic\u0000scenarios. Here we use the Doi-Peliti approach and extend the classical\u0000deterministic SIS and SIR models to a quantum-like formalism to explore whether\u0000the uncertainty of epidemic forecasts is also shaped by the stochastic nature\u0000of epidemic processes. This approach allows getting a probabilistic ensemble of\u0000trajectories, revealing that epidemic uncertainty is not uniform across time,\u0000being maximal around the epidemic peak and vanishing at both early and very\u0000late stages of the outbreak. Our results therefore show that, independently of\u0000the models' complexity, the stochasticity of contagion and recover processes\u0000poses a natural constraint for the uncertainty of epidemic forecasts.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220925","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}
Roger W. Bryenton, Farrukh A. Chishtie, Mujtaba Hassan, Tom Mommsen, Devyani Singh
Methane (CH4) is a potent greenhouse gas (GHG) with a short atmospheric half-life (~8.4 years) and a high short-term impact on global warming, significantly higher than CO2 (Kleinberg, 2020; Balcombe et al., 2018). Traditional metrics such as the 100-year Global Warming Potential (GWP100) obscure methane's short-term, negative climatic effects, potentially leading to inadequate policy responses (Kleinberg, 2020). This letter examines the limitations of GWP100 in capturing methane's true climate impact, explores alternative metrics, and discusses the implications of underreporting methane emissions. We highlight the necessity of adopting a more immediate perspective on methane to accelerate climate emergency action, while noting the adverse effects of the rapid growth rate of methane emissions on reduction efforts. Additionally, we hope that in the immediate future, during COP29, policymakers will adopt actions that give appropriate attention to methane's short-term warming potential to dramatically reduce emissions and address the immediate climate crisis.
{"title":"Is methane the 'climate culprit'? Fixing the 'Broken Record' while unmasking the dangers of using imprecise, long-term GWP for methane to address the climate emergency","authors":"Roger W. Bryenton, Farrukh A. Chishtie, Mujtaba Hassan, Tom Mommsen, Devyani Singh","doi":"arxiv-2409.06212","DOIUrl":"https://doi.org/arxiv-2409.06212","url":null,"abstract":"Methane (CH4) is a potent greenhouse gas (GHG) with a short atmospheric\u0000half-life (~8.4 years) and a high short-term impact on global warming,\u0000significantly higher than CO2 (Kleinberg, 2020; Balcombe et al., 2018).\u0000Traditional metrics such as the 100-year Global Warming Potential (GWP100)\u0000obscure methane's short-term, negative climatic effects, potentially leading to\u0000inadequate policy responses (Kleinberg, 2020). This letter examines the\u0000limitations of GWP100 in capturing methane's true climate impact, explores\u0000alternative metrics, and discusses the implications of underreporting methane\u0000emissions. We highlight the necessity of adopting a more immediate perspective\u0000on methane to accelerate climate emergency action, while noting the adverse\u0000effects of the rapid growth rate of methane emissions on reduction efforts.\u0000Additionally, we hope that in the immediate future, during COP29, policymakers\u0000will adopt actions that give appropriate attention to methane's short-term\u0000warming potential to dramatically reduce emissions and address the immediate\u0000climate crisis.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220928","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}
Shengkai Li, Trung V. Phan, Luca Di Carlo, Gao Wang, Van H. Do, Elia Mikhail, Robert H. Austin, Liyu Liu
We used physical agents with deep memories of past events and left/right ideologies but different fixed personalities to study what drives the polarization of the dynamic population ideology. We find that agents have a critical memory depth below which complete ideology polarization of the collective cannot occur and above which it is inevitable. However, depending on the details of the personalities, the ideologies polarization can be static or dynamic in time, even chaotic. Thus, agents with different personalities and levels of memory (mnemomatter) can serve as a physics analogue of the ideology dynamics among ideological beings, illuminating how decisions influenced by individual memories of past interactions can shape and influence subsequent ideology polarization. Each constituent agent harbors a private stack memory and an onboard microcomputer/controller which both measures and controls its physical spin handedness, which is a proxy for ideology. The agent's decision to change or retain its current spin is determined by each agent's private algorithm for decisions (the personality) and the time-weighted stack history of present and previous interactions. Depending on a given agent's personality for evaluating its memory and experiences, an agent can act as a curmudgeon who never changes its ideology, a pushover who always accepts change, a contrarian who always does the opposite of what is expected, an opportunist who weighs recent events more heavily than past events in making decisions, and a traditionalist who weighs past events more heavily than recent events in decision making. We develop a field theory which maps agent ideological polarization over into a dynamic potential landscape. Perhaps such applications of physics-based systems to political systems will help us to understand the ideological instability observed in the world today.
{"title":"Memory and Personality in Ideological Polarization: The Politico-physics of Mnemomatter","authors":"Shengkai Li, Trung V. Phan, Luca Di Carlo, Gao Wang, Van H. Do, Elia Mikhail, Robert H. Austin, Liyu Liu","doi":"arxiv-2409.06660","DOIUrl":"https://doi.org/arxiv-2409.06660","url":null,"abstract":"We used physical agents with deep memories of past events and left/right\u0000ideologies but different fixed personalities to study what drives the\u0000polarization of the dynamic population ideology. We find that agents have a\u0000critical memory depth below which complete ideology polarization of the\u0000collective cannot occur and above which it is inevitable. However, depending on\u0000the details of the personalities, the ideologies polarization can be static or\u0000dynamic in time, even chaotic. Thus, agents with different personalities and\u0000levels of memory (mnemomatter) can serve as a physics analogue of the ideology\u0000dynamics among ideological beings, illuminating how decisions influenced by\u0000individual memories of past interactions can shape and influence subsequent\u0000ideology polarization. Each constituent agent harbors a private stack memory\u0000and an onboard microcomputer/controller which both measures and controls its\u0000physical spin handedness, which is a proxy for ideology. The agent's decision\u0000to change or retain its current spin is determined by each agent's private\u0000algorithm for decisions (the personality) and the time-weighted stack history\u0000of present and previous interactions. Depending on a given agent's personality\u0000for evaluating its memory and experiences, an agent can act as a curmudgeon who\u0000never changes its ideology, a pushover who always accepts change, a contrarian\u0000who always does the opposite of what is expected, an opportunist who weighs\u0000recent events more heavily than past events in making decisions, and a\u0000traditionalist who weighs past events more heavily than recent events in\u0000decision making. We develop a field theory which maps agent ideological\u0000polarization over into a dynamic potential landscape. Perhaps such applications\u0000of physics-based systems to political systems will help us to understand the\u0000ideological instability observed in the world today.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220924","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}
Alice C. Schwarze, Jessica Jiang, Jonny Wray, Mason A. Porter
Networks are useful descriptions of the structure of many complex systems. Unsurprisingly, it is thus important to analyze the robustness of networks in many scientific disciplines. In applications in communication, logistics, finance, ecology, biomedicine, and many other fields, researchers have studied the robustness of networks to the removal of nodes, edges, or other subnetworks to identify and characterize robust network structures. A major challenge in the study of network robustness is that researchers have reported that different and seemingly contradictory network properties are correlated with a network's robustness. Using a framework by Alderson and Doyle~cite{Alderson2010}, we categorize several notions of network robustness and we examine these ostensible contradictions. We survey studies of network robustness with a focus on (1)~identifying robustness specifications in common use, (2)~understanding when these specifications are appropriate, and (3)~understanding the conditions under which one can expect different notions of robustness to yield similar results. With this review, we aim to give researchers an overview of the large, interdisciplinary body of work on network robustness and develop practical guidance for the design of computational experiments to study a network's robustness.
{"title":"Structural Robustness and Vulnerability of Networks","authors":"Alice C. Schwarze, Jessica Jiang, Jonny Wray, Mason A. Porter","doi":"arxiv-2409.07498","DOIUrl":"https://doi.org/arxiv-2409.07498","url":null,"abstract":"Networks are useful descriptions of the structure of many complex systems.\u0000Unsurprisingly, it is thus important to analyze the robustness of networks in\u0000many scientific disciplines. In applications in communication, logistics,\u0000finance, ecology, biomedicine, and many other fields, researchers have studied\u0000the robustness of networks to the removal of nodes, edges, or other subnetworks\u0000to identify and characterize robust network structures. A major challenge in\u0000the study of network robustness is that researchers have reported that\u0000different and seemingly contradictory network properties are correlated with a\u0000network's robustness. Using a framework by Alderson and\u0000Doyle~cite{Alderson2010}, we categorize several notions of network robustness\u0000and we examine these ostensible contradictions. We survey studies of network\u0000robustness with a focus on (1)~identifying robustness specifications in common\u0000use, (2)~understanding when these specifications are appropriate, and\u0000(3)~understanding the conditions under which one can expect different notions\u0000of robustness to yield similar results. With this review, we aim to give\u0000researchers an overview of the large, interdisciplinary body of work on network\u0000robustness and develop practical guidance for the design of computational\u0000experiments to study a network's robustness.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227423","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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