Pub Date : 2022-12-31DOI: 10.1142/S021952592250014X
F. Schweitzer, George Andres, Giona Casiraghi, Christoph Gote, Ramona Roller, Ingo Scholtes, Giacomo Vaccario, C. Zingg
Resilience denotes the capacity of a system to withstand shocks and its ability to recover from them. We develop a framework to quantify the resilience of highly volatile, non-equilibrium social organizations, such as collectives or collaborating teams. It consists of four steps: (i) emph{delimitation}, i.e., narrowing down the target systems, (ii) emph{conceptualization}, .e., identifying how to approach social organizations, (iii) formal emph{representation} using a combination of agent-based and network models, (iv) emph{operationalization}, i.e. specifying measures and demonstrating how they enter the calculation of resilience. Our framework quantifies two dimensions of resilience, the emph{robustness} of social organizations and their emph{adaptivity}, and combines them in a novel resilience measure. It allows monitoring resilience instantaneously using longitudinal data instead of an ex-post evaluation.
{"title":"Modeling social resilience: Questions, answers, open problems","authors":"F. Schweitzer, George Andres, Giona Casiraghi, Christoph Gote, Ramona Roller, Ingo Scholtes, Giacomo Vaccario, C. Zingg","doi":"10.1142/S021952592250014X","DOIUrl":"https://doi.org/10.1142/S021952592250014X","url":null,"abstract":"Resilience denotes the capacity of a system to withstand shocks and its ability to recover from them. We develop a framework to quantify the resilience of highly volatile, non-equilibrium social organizations, such as collectives or collaborating teams. It consists of four steps: (i) emph{delimitation}, i.e., narrowing down the target systems, (ii) emph{conceptualization}, .e., identifying how to approach social organizations, (iii) formal emph{representation} using a combination of agent-based and network models, (iv) emph{operationalization}, i.e. specifying measures and demonstrating how they enter the calculation of resilience. Our framework quantifies two dimensions of resilience, the emph{robustness} of social organizations and their emph{adaptivity}, and combines them in a novel resilience measure. It allows monitoring resilience instantaneously using longitudinal data instead of an ex-post evaluation.","PeriodicalId":50871,"journal":{"name":"Advances in Complex Systems","volume":"8 1","pages":"2250014:1-2250014:50"},"PeriodicalIF":0.4,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75002740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-29DOI: 10.1142/s0219525922500138
Souvik Roy, Sukanta Das
{"title":"Clouds in the basins of Fully Asynchronous Cellular Automata","authors":"Souvik Roy, Sukanta Das","doi":"10.1142/s0219525922500138","DOIUrl":"https://doi.org/10.1142/s0219525922500138","url":null,"abstract":"","PeriodicalId":50871,"journal":{"name":"Advances in Complex Systems","volume":"25 1","pages":"2250013:1-2250013:26"},"PeriodicalIF":0.4,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75909371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-29DOI: 10.1142/s0219525922500126
Jianxia Wang, Mengqi Hao, Jinlong Ma, Sufeng Li
{"title":"Evolutionary Analysis of Prisoner's Dilemma Games based on mixed Random-Conformity Selecting Model","authors":"Jianxia Wang, Mengqi Hao, Jinlong Ma, Sufeng Li","doi":"10.1142/s0219525922500126","DOIUrl":"https://doi.org/10.1142/s0219525922500126","url":null,"abstract":"","PeriodicalId":50871,"journal":{"name":"Advances in Complex Systems","volume":"20 1","pages":"2250012:1-2250012:15"},"PeriodicalIF":0.4,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87857082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1142/s0219525922500114
Shuo Liu, Michael Mäs, Haoxiang Xia, A. Flache
{"title":"When Intuition Fails: the Complex effects of Assimilative and Repulsive Influence on Opinion polarization","authors":"Shuo Liu, Michael Mäs, Haoxiang Xia, A. Flache","doi":"10.1142/s0219525922500114","DOIUrl":"https://doi.org/10.1142/s0219525922500114","url":null,"abstract":"","PeriodicalId":50871,"journal":{"name":"Advances in Complex Systems","volume":"28 1","pages":"2250011:1-2250011:30"},"PeriodicalIF":0.4,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82038786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-15DOI: 10.25088/complexsystems.31.4.363
Suvadip Hazra, M. Dalui
Cellular automata (CAs) are simple mathematical models that are effectively being used to analyze and understand the behavior of complex systems. Researchers from a wide range of fields are interested in CAs due to their potential for representing a variety of physical, natural and real-world phenomena. Three-neighborhood one-dimensional CAs, a special class of CAs, have been utilized to develop various applications in the field of very large-scale integration (VLSI) design, error-correcting codes, test pattern generation, cryptography and others. A thorough analysis of a three-neighborhood cellular automaton (CA) with two states per cell is presented in this paper. A graph-based tool called the next-state rule minterm transition diagram (NSRTD) is presented for analyzing the state transition behavior of CAs with fixed points. A linear time mechanism has been proposed for synthesizing a special class of irreversible CAs referred to as single length cycle two-attractor CAs (TACAs), having only two fixed points.
{"title":"Characterization of Single Length Cycle Two-Attractor Cellular Automata Using Next-State Rule Minterm Transition Diagram","authors":"Suvadip Hazra, M. Dalui","doi":"10.25088/complexsystems.31.4.363","DOIUrl":"https://doi.org/10.25088/complexsystems.31.4.363","url":null,"abstract":"Cellular automata (CAs) are simple mathematical models that are effectively being used to analyze and understand the behavior of complex systems. Researchers from a wide range of fields are interested in CAs due to their potential for representing a variety of physical, natural and real-world phenomena. Three-neighborhood one-dimensional CAs, a special class of CAs, have been utilized to develop various applications in the field of very large-scale integration (VLSI) design, error-correcting codes, test pattern generation, cryptography and others. A thorough analysis of a three-neighborhood cellular automaton (CA) with two states per cell is presented in this paper. A graph-based tool called the next-state rule minterm transition diagram (NSRTD) is presented for analyzing the state transition behavior of CAs with fixed points. A linear time mechanism has been proposed for synthesizing a special class of irreversible CAs referred to as single length cycle two-attractor CAs (TACAs), having only two fixed points.","PeriodicalId":50871,"journal":{"name":"Advances in Complex Systems","volume":"207 1","pages":"363-388"},"PeriodicalIF":0.4,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76458665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-15DOI: 10.25088/complexsystems.31.4.389
Victor Iapascurta
Electroencephalography (EEG) as an example of electrophysiological monitoring methods has a rather long history of successful application for the diagnosis and treatment of diseases, and this success would not have been possible without effective methods of mathematical, and more recently, computer analysis. Most of these methods are based on statistics. Among the methods of EEG analysis, there is a group of methods that use different versions of Shannon’s entropy estimation as a “main component” and that do not differ significantly from traditional statistical approaches. Despite the external similarity, another approach is to use the Kolmogorov–Chaitin definition of complexity and the concepts of algorithmic information dynamics. The algorithmic dynamics toolbox includes techniques (e.g., block decomposition method) that appear to be applicable to EEG analysis. The current paper is an attempt to use the block decomposition method along with the recent addition to the management of EEG data provided by machine learning, with the ultimate goal of making this data more useful to researchers and medical practitioners.
{"title":"Combining Algorithmic Information Dynamics Concepts and Machine Learning for Electroencephalography Analysis: What Can We Get?","authors":"Victor Iapascurta","doi":"10.25088/complexsystems.31.4.389","DOIUrl":"https://doi.org/10.25088/complexsystems.31.4.389","url":null,"abstract":"Electroencephalography (EEG) as an example of electrophysiological monitoring methods has a rather long history of successful application for the diagnosis and treatment of diseases, and this success would not have been possible without effective methods of mathematical, and more recently, computer analysis. Most of these methods are based on statistics. Among the methods of EEG analysis, there is a group of methods that use different versions of Shannon’s entropy estimation as a “main component” and that do not differ significantly from traditional statistical approaches. Despite the external similarity, another approach is to use the Kolmogorov–Chaitin definition of complexity and the concepts of algorithmic information dynamics. The algorithmic dynamics toolbox includes techniques (e.g., block decomposition method) that appear to be applicable to EEG analysis. The current paper is an attempt to use the block decomposition method along with the recent addition to the management of EEG data provided by machine learning, with the ultimate goal of making this data more useful to researchers and medical practitioners.","PeriodicalId":50871,"journal":{"name":"Advances in Complex Systems","volume":"25 1","pages":"389-413"},"PeriodicalIF":0.4,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90660079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-18DOI: 10.1142/s0219525922500102
Owais A. Hussain, M. Ahmad, Faraz Zaidi
{"title":"Benchmarking the Influential Nodes in Complex Networks","authors":"Owais A. Hussain, M. Ahmad, Faraz Zaidi","doi":"10.1142/s0219525922500102","DOIUrl":"https://doi.org/10.1142/s0219525922500102","url":null,"abstract":"","PeriodicalId":50871,"journal":{"name":"Advances in Complex Systems","volume":"83 1","pages":"2250010:1-2250010:33"},"PeriodicalIF":0.4,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85834874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-11DOI: 10.1142/s0219525922020027
Ramona Roller, Maximilian Schich, Hyejin Youn, M. Tamm
{"title":"Editorial: a Topical Issue on Cultural Complexity","authors":"Ramona Roller, Maximilian Schich, Hyejin Youn, M. Tamm","doi":"10.1142/s0219525922020027","DOIUrl":"https://doi.org/10.1142/s0219525922020027","url":null,"abstract":"","PeriodicalId":50871,"journal":{"name":"Advances in Complex Systems","volume":"15 1","pages":"2202002:1-2202002:3"},"PeriodicalIF":0.4,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74692628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-15DOI: 10.25088/complexsystems.31.3.287
W. Langdon
Large arithmetic expressions are dissipative: they lose information and are robust to perturbations. Lack of conservation gives resilience to fluctuations. The limited precision of floating point and the mixture of linear and nonlinear operations make such functions anti-fragile and give a largely stable locally flat plateau a rich fitness landscape. This slows long-term evolution of complex programs, suggesting a need for depth-aware crossover and mutation operators in tree-based genetic programming. It also suggests that deeply nested computer program source code is error tolerant because disruptions tend to fail to propagate, and therefore the optimal placement of test oracles is as close to software defects as practical.
{"title":"Dissipative Arithmetic","authors":"W. Langdon","doi":"10.25088/complexsystems.31.3.287","DOIUrl":"https://doi.org/10.25088/complexsystems.31.3.287","url":null,"abstract":"Large arithmetic expressions are dissipative: they lose information and are robust to perturbations. Lack of conservation gives resilience to fluctuations. The limited precision of floating point and the mixture of linear and nonlinear operations make such functions anti-fragile and give a largely stable locally flat plateau a rich fitness landscape. This slows long-term evolution of complex programs, suggesting a need for depth-aware crossover and mutation operators in tree-based genetic programming. It also suggests that deeply nested computer program source code is error tolerant because disruptions tend to fail to propagate, and therefore the optimal placement of test oracles is as close to software defects as practical.","PeriodicalId":50871,"journal":{"name":"Advances in Complex Systems","volume":"1 1","pages":"287-309"},"PeriodicalIF":0.4,"publicationDate":"2022-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76334228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-15DOI: 10.25088/complexsystems.31.3.311
Samuele Mazzi, D. Zarzoso
A detailed parametric analysis is presented, where the recent method based on the reservoir computing paradigm, including its statistical robustness, is studied. It is observed that the prediction capabilities of the reservoir computing approach strongly depend on the random initialization of both the input and the reservoir layers. Special emphasis is put on finding the region in the hyperparameter space where the ensemble-averaged training and generalization errors together with their variance are minimized. The statistical analysis presented here is based on the projection on proper elements method.
{"title":"Parametric Validation of the Reservoir Computing-Based Machine Learning Algorithm Applied to Lorenz System Reconstructed Dynamics","authors":"Samuele Mazzi, D. Zarzoso","doi":"10.25088/complexsystems.31.3.311","DOIUrl":"https://doi.org/10.25088/complexsystems.31.3.311","url":null,"abstract":"A detailed parametric analysis is presented, where the recent method based on the reservoir computing paradigm, including its statistical robustness, is studied. It is observed that the prediction capabilities of the reservoir computing approach strongly depend on the random initialization of both the input and the reservoir layers. Special emphasis is put on finding the region in the hyperparameter space where the ensemble-averaged training and generalization errors together with their variance are minimized. The statistical analysis presented here is based on the projection on proper elements method.","PeriodicalId":50871,"journal":{"name":"Advances in Complex Systems","volume":"41 1","pages":"311-339"},"PeriodicalIF":0.4,"publicationDate":"2022-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82815056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：