Pub Date : 2024-07-11DOI: 10.1038/s44260-024-00012-0
Michael W. Macy, Boleslaw K. Szymanski, Janusz A. Hołyst
The centennial of the Ising model marks a century of interdisciplinary contributions that extend well beyond ferromagnets, including the evolution of language, volatility in financial markets, mood swings, scientific collaboration, the persistence of unintended neighborhood segregation, and asymmetric hysteresis in political polarization. The puzzle is how anything could be learned about social life from a toy model of second order ferromagnetic phase transitions on a periodic network. Our answer points to Ising’s deeper contribution: a bottom-up modeling approach that explores phase transitions in population behavior that emerge spontaneously through the interplay of individual choices at the micro-level of interactions among network neighbors.
{"title":"The Ising model celebrates a century of interdisciplinary contributions","authors":"Michael W. Macy, Boleslaw K. Szymanski, Janusz A. Hołyst","doi":"10.1038/s44260-024-00012-0","DOIUrl":"10.1038/s44260-024-00012-0","url":null,"abstract":"The centennial of the Ising model marks a century of interdisciplinary contributions that extend well beyond ferromagnets, including the evolution of language, volatility in financial markets, mood swings, scientific collaboration, the persistence of unintended neighborhood segregation, and asymmetric hysteresis in political polarization. The puzzle is how anything could be learned about social life from a toy model of second order ferromagnetic phase transitions on a periodic network. Our answer points to Ising’s deeper contribution: a bottom-up modeling approach that explores phase transitions in population behavior that emerge spontaneously through the interplay of individual choices at the micro-level of interactions among network neighbors.","PeriodicalId":501707,"journal":{"name":"npj Complexity","volume":" ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44260-024-00012-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141597210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-02DOI: 10.1038/s44260-024-00011-1
Thomas F. Varley
In the last decade, there has been an explosion of interest in the field of multivariate information theory and the study of emergent, higher-order interactions. These “synergistic” dependencies reflect information that is in the “whole” but not any of the “parts.” Arguably the most successful framework for exploring synergies is the partial information decomposition (PID). Despite its considerable power, the PID has a number of limitations that restrict its general applicability. Subsequently, other heuristic measures, such as the O-information, have been introduced, although these measures typically only provide a summary statistic of redundancy/synergy dominance, rather than direct insight into the synergy itself. To address this issue, we present an alternative decomposition that is synergy-first, scales much more gracefully than the PID, and has a straightforward interpretation. We define synergy as that information encoded in the joint state of a set of elements that would be lost following the minimally invasive perturbation on any single element. By generalizing this idea to sets of elements, we construct a totally ordered “backbone” of partial synergy atoms that sweeps the system’s scale. This approach applies to the entropy, the Kullback-Leibler divergence, and by extension, to the total correlation and the single-target mutual information (thus recovering a “backbone” PID). Finally, we show that this approach can be used to decompose higher-order interactions beyond information theory by showing how synergistic combinations of edges in a graph support global integration via communicability. We conclude by discussing how this perspective on synergistic structure can deepen our understanding of part-whole relationships in complex systems.
{"title":"A scalable synergy-first backbone decomposition of higher-order structures in complex systems","authors":"Thomas F. Varley","doi":"10.1038/s44260-024-00011-1","DOIUrl":"10.1038/s44260-024-00011-1","url":null,"abstract":"In the last decade, there has been an explosion of interest in the field of multivariate information theory and the study of emergent, higher-order interactions. These “synergistic” dependencies reflect information that is in the “whole” but not any of the “parts.” Arguably the most successful framework for exploring synergies is the partial information decomposition (PID). Despite its considerable power, the PID has a number of limitations that restrict its general applicability. Subsequently, other heuristic measures, such as the O-information, have been introduced, although these measures typically only provide a summary statistic of redundancy/synergy dominance, rather than direct insight into the synergy itself. To address this issue, we present an alternative decomposition that is synergy-first, scales much more gracefully than the PID, and has a straightforward interpretation. We define synergy as that information encoded in the joint state of a set of elements that would be lost following the minimally invasive perturbation on any single element. By generalizing this idea to sets of elements, we construct a totally ordered “backbone” of partial synergy atoms that sweeps the system’s scale. This approach applies to the entropy, the Kullback-Leibler divergence, and by extension, to the total correlation and the single-target mutual information (thus recovering a “backbone” PID). Finally, we show that this approach can be used to decompose higher-order interactions beyond information theory by showing how synergistic combinations of edges in a graph support global integration via communicability. We conclude by discussing how this perspective on synergistic structure can deepen our understanding of part-whole relationships in complex systems.","PeriodicalId":501707,"journal":{"name":"npj Complexity","volume":" ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44260-024-00011-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-07DOI: 10.1038/s44260-024-00008-w
Kristina Lerman, Dan Feldman, Zihao He, Ashwin Rao
Members of different political groups not only disagree about issues but also dislike and distrust each other. While social media can amplify this emotional divide—called affective polarization by political scientists—there is a lack of agreement on its strength and prevalence. We measure affective polarization on social media by quantifying the emotions and toxicity of reply interactions. We demonstrate that, as predicted by affective polarization, interactions between users with same ideology (in-group replies) tend to be positive, while interactions between opposite-ideology users (out-group replies) are characterized by negativity and toxicity. Second, we show that affective polarization generalizes beyond the in-group/out-group dichotomy and can be considered a structural property of social networks. Specifically, we show that emotions vary with network distance between users, with closer interactions eliciting positive emotions and more distant interactions leading to anger, disgust, and toxicity. Finally, we show that similar information exhibits different dynamics when spreading in emotionally polarized groups. These findings are consistent across diverse datasets spanning discussions on topics such as the COVID-19 pandemic and abortion in the US. Our research provides insights into the complex social dynamics of affective polarization in the digital age and its implications for political discourse.
{"title":"Affective polarization and dynamics of information spread in online networks","authors":"Kristina Lerman, Dan Feldman, Zihao He, Ashwin Rao","doi":"10.1038/s44260-024-00008-w","DOIUrl":"10.1038/s44260-024-00008-w","url":null,"abstract":"Members of different political groups not only disagree about issues but also dislike and distrust each other. While social media can amplify this emotional divide—called affective polarization by political scientists—there is a lack of agreement on its strength and prevalence. We measure affective polarization on social media by quantifying the emotions and toxicity of reply interactions. We demonstrate that, as predicted by affective polarization, interactions between users with same ideology (in-group replies) tend to be positive, while interactions between opposite-ideology users (out-group replies) are characterized by negativity and toxicity. Second, we show that affective polarization generalizes beyond the in-group/out-group dichotomy and can be considered a structural property of social networks. Specifically, we show that emotions vary with network distance between users, with closer interactions eliciting positive emotions and more distant interactions leading to anger, disgust, and toxicity. Finally, we show that similar information exhibits different dynamics when spreading in emotionally polarized groups. These findings are consistent across diverse datasets spanning discussions on topics such as the COVID-19 pandemic and abortion in the US. Our research provides insights into the complex social dynamics of affective polarization in the digital age and its implications for political discourse.","PeriodicalId":501707,"journal":{"name":"npj Complexity","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44260-024-00008-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141287016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-07DOI: 10.1038/s44260-024-00007-x
P. L. Krapivsky, S. Redner
We introduce a simple multiplicative model to describe the temporal behavior and the ultimate outcome of an epidemic. Our model accounts, in a minimalist way, for the competing influences of imposing public-health restrictions when the epidemic is severe, and relaxing restrictions when the epidemic is waning. Our primary results are that different instances of an epidemic with identical starting points have disparate outcomes and each epidemic temporal history is strongly fluctuating.
{"title":"Epidemic forecast follies","authors":"P. L. Krapivsky, S. Redner","doi":"10.1038/s44260-024-00007-x","DOIUrl":"10.1038/s44260-024-00007-x","url":null,"abstract":"We introduce a simple multiplicative model to describe the temporal behavior and the ultimate outcome of an epidemic. Our model accounts, in a minimalist way, for the competing influences of imposing public-health restrictions when the epidemic is severe, and relaxing restrictions when the epidemic is waning. Our primary results are that different instances of an epidemic with identical starting points have disparate outcomes and each epidemic temporal history is strongly fluctuating.","PeriodicalId":501707,"journal":{"name":"npj Complexity","volume":" ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44260-024-00007-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141287019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-29DOI: 10.1038/s44260-024-00005-z
Małgorzata Fic, Chaitanya S. Gokhale
Collective beliefs can catalyse cooperation in a population of selfish individuals. We study this transformative power of collective beliefs, an effect that intriguingly persists even when beliefs lack moralising components. Besides the process itself, we consider the structure of human populations explicitly. We incorporate the intricate structure of human populations into our model, acknowledging the bias brought by social and cultural identities in interaction networks. Hence, we develop our model by assuming a heterogeneous group size and structured population. We recognise that beliefs, typically complex story systems, might not spontaneously emerge in society, resulting in different spreading rates for actions and beliefs within populations. As the degree of connectedness can vary among individuals perpetuating a belief, we examine the speed of trust build-up in networks with different connection densities. We then scrutinise the timing, speed and dynamics of trust and belief spread across specific network structures, including random Erdös-Rényi networks, scale-free Barabási-Albert networks, and small-world Newman-Watts-Strogatz networks. By comparing these characteristics across various network topologies, we disentangle the effects of structure, group size diversity, and evolutionary dynamics on the evolution of trust and belief.
{"title":"Catalysing cooperation: the power of collective beliefs in structured populations","authors":"Małgorzata Fic, Chaitanya S. Gokhale","doi":"10.1038/s44260-024-00005-z","DOIUrl":"10.1038/s44260-024-00005-z","url":null,"abstract":"Collective beliefs can catalyse cooperation in a population of selfish individuals. We study this transformative power of collective beliefs, an effect that intriguingly persists even when beliefs lack moralising components. Besides the process itself, we consider the structure of human populations explicitly. We incorporate the intricate structure of human populations into our model, acknowledging the bias brought by social and cultural identities in interaction networks. Hence, we develop our model by assuming a heterogeneous group size and structured population. We recognise that beliefs, typically complex story systems, might not spontaneously emerge in society, resulting in different spreading rates for actions and beliefs within populations. As the degree of connectedness can vary among individuals perpetuating a belief, we examine the speed of trust build-up in networks with different connection densities. We then scrutinise the timing, speed and dynamics of trust and belief spread across specific network structures, including random Erdös-Rényi networks, scale-free Barabási-Albert networks, and small-world Newman-Watts-Strogatz networks. By comparing these characteristics across various network topologies, we disentangle the effects of structure, group size diversity, and evolutionary dynamics on the evolution of trust and belief.","PeriodicalId":501707,"journal":{"name":"npj Complexity","volume":" ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44260-024-00005-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141165068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-18DOI: 10.1038/s44260-024-00006-y
Juniper Lovato, Jonathan St-Onge, Randall Harp, Gabriela Salazar Lopez, Sean P. Rogers, Ijaz Ul Haq, Laurent Hébert-Dufresne, Jeremiah Onaolapo
Social media platforms often assume that users can self-correct against misinformation. However, social media users are not equally susceptible to all misinformation as their biases influence what types of misinformation might thrive and who might be at risk. We call “diverse misinformation” the complex relationships between human biases and demographics represented in misinformation. To investigate how users’ biases impact their susceptibility and their ability to correct each other, we analyze classification of deepfakes as a type of diverse misinformation. We chose deepfakes as a case study for three reasons: (1) their classification as misinformation is more objective; (2) we can control the demographics of the personas presented; (3) deepfakes are a real-world concern with associated harms that must be better understood. Our paper presents an observational survey (N = 2016) where participants are exposed to videos and asked questions about their attributes, not knowing some might be deepfakes. Our analysis investigates the extent to which different users are duped and which perceived demographics of deepfake personas tend to mislead. We find that accuracy varies by demographics, and participants are generally better at classifying videos that match them. We extrapolate from these results to understand the potential population-level impacts of these biases using a mathematical model of the interplay between diverse misinformation and crowd correction. Our model suggests that diverse contacts might provide “herd correction” where friends can protect each other. Altogether, human biases and the attributes of misinformation matter greatly, but having a diverse social group may help reduce susceptibility to misinformation.
{"title":"Diverse misinformation: impacts of human biases on detection of deepfakes on networks","authors":"Juniper Lovato, Jonathan St-Onge, Randall Harp, Gabriela Salazar Lopez, Sean P. Rogers, Ijaz Ul Haq, Laurent Hébert-Dufresne, Jeremiah Onaolapo","doi":"10.1038/s44260-024-00006-y","DOIUrl":"10.1038/s44260-024-00006-y","url":null,"abstract":"Social media platforms often assume that users can self-correct against misinformation. However, social media users are not equally susceptible to all misinformation as their biases influence what types of misinformation might thrive and who might be at risk. We call “diverse misinformation” the complex relationships between human biases and demographics represented in misinformation. To investigate how users’ biases impact their susceptibility and their ability to correct each other, we analyze classification of deepfakes as a type of diverse misinformation. We chose deepfakes as a case study for three reasons: (1) their classification as misinformation is more objective; (2) we can control the demographics of the personas presented; (3) deepfakes are a real-world concern with associated harms that must be better understood. Our paper presents an observational survey (N = 2016) where participants are exposed to videos and asked questions about their attributes, not knowing some might be deepfakes. Our analysis investigates the extent to which different users are duped and which perceived demographics of deepfake personas tend to mislead. We find that accuracy varies by demographics, and participants are generally better at classifying videos that match them. We extrapolate from these results to understand the potential population-level impacts of these biases using a mathematical model of the interplay between diverse misinformation and crowd correction. Our model suggests that diverse contacts might provide “herd correction” where friends can protect each other. Altogether, human biases and the attributes of misinformation matter greatly, but having a diverse social group may help reduce susceptibility to misinformation.","PeriodicalId":501707,"journal":{"name":"npj Complexity","volume":" ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44260-024-00006-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141069197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-17DOI: 10.1038/s44260-024-00003-1
Golnar Gharooni-Fard, Morgan Byers, Varad Deshmukh, Elizabeth Bradley, Carissa Mayo, Chad M. Topaz, Orit Peleg
A primary challenge in understanding collective behavior is characterizing the spatiotemporal dynamics of the group. We employ topological data analysis to explore the structure of honeybee aggregations that form during trophallaxis, which is the direct exchange of food among nestmates. From the positions of individual bees, we build topological summaries called CROCKER matrices to track the morphology of the group as a function of scale and time. Each column of a CROCKER matrix records the number of topological features, such as the number of components or holes, that exist in the data for a range of analysis scales, at a given point in time. To detect important changes in the morphology of the group from this information, we first apply dimensionality reduction techniques to these matrices and then use classic clustering and change-point detection algorithms on the resulting scalar data. A test of this methodology on synthetic data from an agent-based model of honeybees and their trophallaxis behavior shows two distinct phases: a dispersed phase that occurs before food is introduced, followed by a food-exchange phase during which aggregations form. We then move to laboratory data, successfully detecting the same two phases across multiple experiments. Interestingly, our method reveals an additional phase change towards the end of the experiments, suggesting the possibility of another dispersed phase that follows the food-exchange phase.
{"title":"A computational topology-based spatiotemporal analysis technique for honeybee aggregation","authors":"Golnar Gharooni-Fard, Morgan Byers, Varad Deshmukh, Elizabeth Bradley, Carissa Mayo, Chad M. Topaz, Orit Peleg","doi":"10.1038/s44260-024-00003-1","DOIUrl":"10.1038/s44260-024-00003-1","url":null,"abstract":"A primary challenge in understanding collective behavior is characterizing the spatiotemporal dynamics of the group. We employ topological data analysis to explore the structure of honeybee aggregations that form during trophallaxis, which is the direct exchange of food among nestmates. From the positions of individual bees, we build topological summaries called CROCKER matrices to track the morphology of the group as a function of scale and time. Each column of a CROCKER matrix records the number of topological features, such as the number of components or holes, that exist in the data for a range of analysis scales, at a given point in time. To detect important changes in the morphology of the group from this information, we first apply dimensionality reduction techniques to these matrices and then use classic clustering and change-point detection algorithms on the resulting scalar data. A test of this methodology on synthetic data from an agent-based model of honeybees and their trophallaxis behavior shows two distinct phases: a dispersed phase that occurs before food is introduced, followed by a food-exchange phase during which aggregations form. We then move to laboratory data, successfully detecting the same two phases across multiple experiments. Interestingly, our method reveals an additional phase change towards the end of the experiments, suggesting the possibility of another dispersed phase that follows the food-exchange phase.","PeriodicalId":501707,"journal":{"name":"npj Complexity","volume":" ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44260-024-00003-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140606532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-17DOI: 10.1038/s44260-024-00002-2
Minzhang Zheng, Richard F. Sear, Lucia Illari, Nicholas J. Restrepo, Neil F. Johnson
Online hate is dynamic, adaptive— and may soon surge with new AI/GPT tools. Establishing how hate operates at scale is key to overcoming it. We provide insights that challenge existing policies. Rather than large social media platforms being the key drivers, waves of adaptive links across smaller platforms connect the hate user base over time, fortifying hate networks, bypassing mitigations, and extending their direct influence into the massive neighboring mainstream. Data indicates that hundreds of thousands of people globally, including children, have been exposed. We present governing equations derived from first principles and a tipping-point condition predicting future surges in content transmission. Using the U.S. Capitol attack and a 2023 mass shooting as case studies, our findings offer actionable insights and quantitative predictions down to the hourly scale. The efficacy of proposed mitigations can now be predicted using these equations.
{"title":"Adaptive link dynamics drive online hate networks and their mainstream influence","authors":"Minzhang Zheng, Richard F. Sear, Lucia Illari, Nicholas J. Restrepo, Neil F. Johnson","doi":"10.1038/s44260-024-00002-2","DOIUrl":"10.1038/s44260-024-00002-2","url":null,"abstract":"Online hate is dynamic, adaptive— and may soon surge with new AI/GPT tools. Establishing how hate operates at scale is key to overcoming it. We provide insights that challenge existing policies. Rather than large social media platforms being the key drivers, waves of adaptive links across smaller platforms connect the hate user base over time, fortifying hate networks, bypassing mitigations, and extending their direct influence into the massive neighboring mainstream. Data indicates that hundreds of thousands of people globally, including children, have been exposed. We present governing equations derived from first principles and a tipping-point condition predicting future surges in content transmission. Using the U.S. Capitol attack and a 2023 mass shooting as case studies, our findings offer actionable insights and quantitative predictions down to the hourly scale. The efficacy of proposed mitigations can now be predicted using these equations.","PeriodicalId":501707,"journal":{"name":"npj Complexity","volume":" ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44260-024-00002-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140606539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-17DOI: 10.1038/s44260-024-00001-3
Dan Braha
Phase transitions, characterized by abrupt shifts between macroscopic patterns of organization, are ubiquitous in complex systems. Despite considerable research in the physical and natural sciences, the empirical study of this phenomenon in societal systems is relatively underdeveloped. The goal of this study is to explore whether the dynamics of collective civil unrest can be plausibly characterized as a sequence of recurrent phase shifts, with each phase having measurable and identifiable latent characteristics. Building on previous efforts to characterize civil unrest as a self-organized critical system, we introduce a macro-level statistical model of civil unrest and evaluate its plausibility using a comprehensive dataset of civil unrest events in 170 countries from 1946 to 2017. Our findings demonstrate that the macro-level phase model effectively captures the characteristics of civil unrest data from diverse countries globally and that universal mechanisms may underlie certain aspects of the dynamics of civil unrest. We also introduce a scale to quantify a country’s long-term unrest per unit of time and show that civil unrest events tend to cluster geographically, with the magnitude of civil unrest concentrated in specific regions. Our approach has the potential to identify and measure phase transitions in various collective human phenomena beyond civil unrest, contributing to a better understanding of complex social systems.
{"title":"Phase transitions of civil unrest across countries and time","authors":"Dan Braha","doi":"10.1038/s44260-024-00001-3","DOIUrl":"10.1038/s44260-024-00001-3","url":null,"abstract":"Phase transitions, characterized by abrupt shifts between macroscopic patterns of organization, are ubiquitous in complex systems. Despite considerable research in the physical and natural sciences, the empirical study of this phenomenon in societal systems is relatively underdeveloped. The goal of this study is to explore whether the dynamics of collective civil unrest can be plausibly characterized as a sequence of recurrent phase shifts, with each phase having measurable and identifiable latent characteristics. Building on previous efforts to characterize civil unrest as a self-organized critical system, we introduce a macro-level statistical model of civil unrest and evaluate its plausibility using a comprehensive dataset of civil unrest events in 170 countries from 1946 to 2017. Our findings demonstrate that the macro-level phase model effectively captures the characteristics of civil unrest data from diverse countries globally and that universal mechanisms may underlie certain aspects of the dynamics of civil unrest. We also introduce a scale to quantify a country’s long-term unrest per unit of time and show that civil unrest events tend to cluster geographically, with the magnitude of civil unrest concentrated in specific regions. Our approach has the potential to identify and measure phase transitions in various collective human phenomena beyond civil unrest, contributing to a better understanding of complex social systems.","PeriodicalId":501707,"journal":{"name":"npj Complexity","volume":" ","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44260-024-00001-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140606552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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