Complex Systems Research Research Articles

Predicting Dependent Edges in Nonequilibrium Complex Systems Based on Overlapping Module Characteristics

Problem: Predicting dependency relationships in nonequilibrium systems is a critical challenge in complex systems research. Solution proposed: In this paper, we propose a novel method for predicting dependent edges in network models of nonequilibrium complex systems, based on overlapping module features. This approach addresses the many-to-many dependency prediction problem between nonequilibrium complex networks. By transforming node-based network models into edge-based models, we identify overlapping modular structures, enabling the prediction of many-to-many dependent edges. Experimental evaluation: This method is applied to dependency edge prediction in power and gas networks, curriculum and competency networks, and text and question networks. Results: The results indicate that the proposed dependency edge prediction method enhances the robustness of the network in power–gas networks, accurately identifies supporting relationships in curriculum–competency networks, and achieves better information gain in text–question networks. Conclusion: These findings confirm that the overlapping module-based approach effectively predicts dependencies across various nonequilibrium complex systems in diverse fields.

Strategizing the industrial core of the national economy

Strategic provision of technological sovereignty and economic security for the long term are of the highest importance for any state. The availability of its own critically important technologies and high-tech production of products in priority economic areas increase the state’s resilience to external challenges and threats, as well as orient the economy towards the realization of national values and the interests of innovative development of emerging strategic opportunities. The formation of an industrial core as a vector for the implementation of a strategy to ensure technological sovereignty and economic security is a critically important strategic goal of Russia. At the national level, the need to strengthen and strategize the industrial core is confirmed, in which the industry should be based, not only ensuring stable dynamics of the national economy, but also stimulating its development focused on innovative priorities. However, the results of the strategic diagnosis indicate the existing imbalance in the sectoral structure of the national economy caused by premature processes of deindustrialization and primitivization of production capacities. The consequence of this is an increase in the negative impact of outdated technologies and organizational systems on the functioning and development of Russian industries. The study of the regulatory environment and methodological base of modern strategizing of priority industries in Russia conducted in this article indicates the presence of systemic errors in the preparation and implementation of strategic documents for long-term industrial development. In the article, the authors come to the conclusion about the imperfection and fragmentation of the applied methodology for the development and implementation of strategies for the development of industries, including errors of an essential, structural and methodological nature, or the lack of a methodology for strategizing in general. As a solution, the author proposes the use of industrial strategizing methodology, based on the theory of strategy and strategizing methodology of the Russian scientific school developed by the Center for Strategic Research at the Institute of Mathematical Research of Complex Systems and the Economic and Financial Strategy Department at Moscow School of Economics at Lomonosov Moscow State University, and has a number of advantages for the targeted development of industrial sectors and the formation of the industrial core of the national economy based on focusing on the search and implementation of strategic opportunities and the use of existing and newly created competitive advantages.

Beyond regression: Unpacking research of human complex systems with qualitative comparative analysis

This study underscores the effectiveness of Qualitative Comparative Analysis (QCA) when compared to conventional regression analysis (CRA) in the investigation of complex human systems. Utilizing historical secondary cross-national data from Lipset & Man (1960) spanning 18 countries, where CRA may be impractical, the research emphasizes the superior performance of QCA, specifically utilizing both crisp set QCA and fuzzy set QCA. The dataset includes variables such as democracy survival and its precursors, such as gross national product per capita, urbanization, literacy rate, and industrial labor force. In contrast to conventional regression results indicating an insignificant relationship between democracy survival and its antecedents, crisp set QCA identifies two distinct combinations of antecedents associated with high levels of democracy survival, albeit with limited solution coverage. Meanwhile, fuzzy set qualitative comparative analysis (fsQCA) reveals five combinations of antecedents linked to robust democracy survival, providing adequate solution coverage and consistency. These findings suggest that fsQCA excels in capturing the intricacies of real-life scenarios in human complex systems, offering more robust empirical solutions compared to crisp set QCA and conventional regression. As a result, researchers may find value in integrating fsQCA into their new projects focused on human complex systems.

Understanding Emergent Complexity from a Single-Molecule Perspective.

Molecules, with structural, scaling, and interaction diversities, are crucial for the emergence of complex behaviors. Interactions are essential prerequisites for complex systems to exhibit emergent properties that surpass the sum of individual component characteristics. Tracing the origin of complex molecular behaviors from interactions is critical to understanding ensemble emergence, and requires insights at the single-molecule level. Electrical signals from single-molecule junctions enable the observation of individual molecular behaviors, as well as intramolecular and intermolecular interactions. This technique provides a foundation for bottom-up explorations of emergent complexity. This Perspective highlights investigations of various interactions via single-molecule junctions, including intramolecular orbital and weak intermolecular interactions and interactions in chemical reactions. It also provides potential directions for future single-molecule junctions in complex system research.

Is language change chiefly a social diffusion affair? The role of entrenchment in frequency increase and in the emergence of complex structural patterns

Complex systems research has chiefly investigated language change from a social dynamics perspective, with undeniable success. However, there is more to language change than social diffusion, i.e., a one-off adoption of an innovative variant by language users. Language use indeed factors in, besides prevalence (the percentage of adopters of the form in the community), lexical diversity (the number of different lexical items a conventionalized pattern combines with), and entrenchment (the average rate at which speakers choose the form in suitable pragmatic environments). Changes in token frequency may reflect changes in any of these three variables. To sort them out, we defined proxies to factor entrenchment out of empirical measures of prevalence and lexical diversity. From a French corpus, we analyzed 25 schematic constructions, featuring an open slot that hosts a variety of fillers. We show that their rise of token frequency across a change episode is mostly explained by entrenchment; however, the magnitude of the change is uniquely explained by the final extent of its lexical diversity. Furthermore, the fillers obey a construction-specific Zipf-Mandelbrot organization, that robustly holds throughout the change episode. We also show that in some cases, the fillers arise simultaneously, hinting at the possibility that such a complex organization emerges all at once, highlighting the role of structural features in language change.

Applying complex adaptive systems research results to combat simulations of the generation-after-next

Combat has seen fundamental changes over the past century—with new domains of warfighting (e.g., cyber, space); the introduction of nuclear and precision weapons; ubiquitous, all source intelligence; mixes of national, coalition, and private forces operating; and numerous other examples. Over this same period, combat simulations, and the attendant training, experimentation, and analyses they support, have increased in scale, scope, and resolution. But with respect to their fundamental effectiveness, have they substantively moved beyond the foundation created by Lanchester in the early 20th century? Differential equations of mean field approximations have been complemented by discrete-event simulations at many scales; however, the underlying conceptual framework of force-on-force conflict and tightly constrained, potentially fragile, service-specific command and control remains. In this position paper, we argue that new conceptualizations, better aligned with the modern warfighting landscape, are needed across all applications of military modeling. We examine the potential role of complex adaptive systems in this endeavor.

Estimating hidden relationships in dynamical systems: Discovering drivers of infection rates of COVID-19.

Discovering causal influences among internal variables is a fundamental goal of complex systems research. This paper presents a framework for uncovering hidden relationships from limited time-series data by combining methods from nonlinear estimation and information theory. The approach is based on two sequential steps: first, we reconstruct a more complete state of the underlying dynamical system, and second, we calculate mutual information between pairs of internal state variables to detail causal dependencies. Equipped with time-series data related to the spread of COVID-19 from the past three years, we apply this approach to identify the drivers of falling and rising infections during the three main waves of infection in the Chicago metropolitan region. The unscented Kalman filter nonlinear estimation algorithm is implemented on an established epidemiological model of COVID-19, which we refine to include isolation, masking, loss of immunity, and stochastic transition rates. Through the systematic study of mutual information between infection rate and various stochastic parameters, we find that increased mobility, decreased mask use, and loss of immunity post sickness played a key role in rising infections, while falling infections were controlled by masking and isolation.

Подготовка экономистов-стратегов как одно из стратегических направлений развития системы образования опорного университета Кузбасса

Strategizing is a relevant area of professional activity that requires new specialists capable of innovative strategic thinking. As a result, the Kemerovo State University has launched a Master’s degree program in Economic and Financial Strategy. It provides high-quality economic education based on the current social demands and advanced trends. Its graduates undergo an in-depth training in economic strategy with prospects of becoming leaders in various aspects of the regional industry. Designed as an analogue of the world-class Master’s program at the Lomonosov Moscow State University, the curriculum has a certain regional specificity. It is based on the local needs in the implementation of the Strategy for the Socio-Economic Development of the Kemerovo Region through 2035. The Strategy with its long-term prospects was developed by the scientific school of strategizing. The school is headed by Professor Vladimir L. Kvint, Doctor of Economics, Foreign Member of the Russian Academy of Sciences, Head of the Center for Strategic Research, Institute for Mathematical Research of Complex Systems, Lomonosov Moscow State University.

A New Perspective for Computational Social Systems: Fuzzy Modeling and Reasoning for Social Computing in CPSS

The evolution of modern mobile terminals, social networks, and other intelligent services makes everyone become a ubiquitous information perceiver, producer, and propagator. Also known as “social sensor” and “social IoT”, these individuals and communities generate a huge volume of social signals, which has shown prominent value for mining. These unstructured social signals provide a new perspective in the research of complex systems, which makes the traditional cyber–physical system (CPS)-oriented information computing sublimate to the cyber–physical–social system (CPSS)-oriented knowledge computing. However, there still exist great uncertainties, ambiguities, and complexities in modeling behaviors of social individuals or groups. Especially when we apply big-data-driven learning-based models in specific fields and scenarios, the lack of domain expert knowledge and characteristics of system uncertainty severely limits the performance and accuracy of these models. The introduction of fuzzy system modeling integrates data and knowledge in the social computing area, which has shown its unique advantages in solving the above issues and has drawn more attention to this topic. In this article, we conduct a review of recent advances in social computing with fuzzy technologies in CPSS. First, we briefly review the development of social computing, and analyze the characteristics and advantages of social computing through fuzzy methods. Second, we refine core fuzzy system methods for social computing and elaborate on existing fuzzy-technology-empowered social computing methodologies. As in a range of social spaces, we also review and analyze related advances in human-in-the-loop systems. We also reveal the trend of decentralized, autonomous, and organized computing in cyber–physical–social space with fuzzy-based methods and proposed a framework to categorize related studies in CPSS. Finally, we conclude the research trends and hotspots based on current studies, and discuss the challenges for future research directions.

Sustainability: We need to focus on overall system outcomes rather than simplistic targets

Abstract Many of the global challenges that confront humanity are interlinked in a dynamic complex network, with multiple feedback loops, nonlinear interactions and interdependencies that make it difficult, if not impossible, to consider individual threats in isolation. These challenges are mainly dealt with, however, by considering individual threats in isolation (at least in political terms). The mitigation of dual climate and biodiversity threats, for example, is linked to a univariate 1.5°C global warming boundary and a global area conservation target of 30% by 2030. The situation has been somewhat improved by efforts to account for interactions through multidimensional target setting, adaptive and open management and market‐based decision pathways. But the fundamental problem still remains—that complex systems such as those formed by the network of global threats have emergent properties that are more than the sum of their parts. We must learn how to deal with or live with these properties if we are to find effective ways to cope with the threats, individually and collectively. Here, we argue that recent progresses in complex systems research and related fields have enhanced our ability to analyse and model such entwined systems to the extent that it offers the promise of a new approach to sustainability. We discuss how this may be achieved, both in theory and in practice, and how human cultural factors play an important but neglected role that could prove vital to achieving success. Read the free Plain Language Summary for this article on the Journal blog.

Reduction of thermal conductivity in carbon nanotubes by fullerene encapsulation from machine-learning molecular dynamics simulations

The carbon nano-peapod is a representative structure with interlayer van der Waals (vdW) interactions, in which encapsulated fullerene molecules play a critical role in modulating the transport properties of the carbon nanotubes (CNTs). In particular, their influence on the thermal transport characteristics has been the focal point of considerable attention. In this study, we trained an accurate machine learning potential for fullerene-encapsulated CNTs based on the efficient NEP model to investigate their thermal properties. Using equilibrium molecular dynamics simulation along with the spectral decomposition method for thermal conductivity, we find that the thermal conductivity of fullerene-encapsulated CNTs is roughly 55% lower than that of empty CNTs, aligning with experimental observations for CNT bundles with fullerene encapsulation [Kodama et al., Nat. Mater. 16, 892 (2017)]. The research suggests that weak vdW interactions between both the fullerene and CNTs, as well as between fullerene molecules themselves, hinder phonon propagation. The encapsulated fullerene contributes to an increase in phonon scattering within the CNTs, ultimately leading to a reduction in thermal conductivity. We utilized machine learning potential to investigate the structure of fullerene-encapsulated CNTs and their heat transport property. This approach provides valuable insights for performance research of complex systems featuring interlayer vdW interactions.

Research progress on preparation and applications of covalent organic framework-based chromatographic stationary phases

Given continuous developments in industrial and scientific research, the separation and analysis of complex systems with high sensitivity, throughput, and selectivity is facing new challenges. Chromatography plays an irreplaceable role in separation science and is widely applied in environmental monitoring, pharmaceutical analysis, and food safety. Owing to their outstanding advantages, such as high loading capacity, precise quantification, and good reproducibility, chromatographic separation techniques based on various retention mechanisms have been utilized to detect different analytes. The stationary phase is the core material of chromatographic columns and has an extremely important influence on their separation performance. The selectivity and efficiency of separation largely depend on the chromatographic stationary phase. However, traditional stationary phases, such as silicon-based matrices, are characterized by complex preparation processes, poor permeability, large mass transfer resistance, and a narrow pH range. In addition, polymer matrices show poor mechanical stability and susceptibility to swelling, which limit their applications in the field of separation. Therefore, the development of novel stationary phases with the advantages of traditional stationary phases has become a research emphasis in the field of analytical science in efforts to meet separation requirements under different environments. Various stationary phases based on novel porous materials, such as metal organic frameworks (MOFs), porous organic cages (POCs), and covalent organic frameworks (COFs), are used for chromatographic separation. As mesh crystalline porous materials, MOFs have the advantages of a large surface area, adjustable structure, and easy functionalization; thus, they are widely used as chromatographic stationary phases in reverse-phase chromatography, hydrophilic-mode chromatography, mixed-mode chromatography, and other separation modes. However, because the pore size of MOFs is small and most MOFs demonstrate poor chemical stability under acidic or alkaline conditions, their applications in chromatographic separation are greatly limited. COFs are porous, crystalline polymer materials composed of light elements (H, O, C, N, B, and Si) connected via covalent bonds. Their advantages include a low density, large specific surface area, high porosity, good chemical and thermal stability, regular pores, and adjustable pore sizes. Because of their unique structures and properties, COFs are widely used in many fields such as catalysis, enrichment, gas capture, and sensing. COF materials are also suitable for separation analysis and considered ideal materials for novel chromatographic stationary phases. This review summarizes the latest research progress on the preparation and applications of COF-based chromatographic stationary phases over the past five years. First, the preparation of COF-based stationary phases (SiO2@COFs stationary phase, COFs monolithic stationary phase, pure COFs stationary phase and COFs-coated stationary phase) is introduced. The latest applications of COF-based stationary phases in the separation of organic compounds, isomers, and chiral compounds are then described in detail. Finally, the future development trends and challenges of chromatographic stationary phases based on COFs are discussed to provide new ideas for the future design and development of novel chromatographic stationary phases based on COFs.

Complexity Study on Multi-Field Coupling Systems for Underground Coal Fires

Underground coal fires are a major disaster that needs to be urgently addressed in the coal mining industry, as they can cause waste of resources, environmental pollution, threaten the life and health of species, and cause serious damage to society and the economy. Currently, the research on the prevention and control of underground coal fires focuses more on the technical level and lacks scientific guidance at the methodological level. Based on this research gap, this paper uses CiteSpace 6.1.R6 to analyze the research hotspots and subjects in the field of underground coal fires and gives a comprehensive research overview of its microscopic chemical reaction substance and macroscopic multi-field coupling characteristics. Then, from the methodological level, it summarizes the complex system characteristics of underground coal fires and puts forward the elements and principles for the prevention and control of underground coal fire disasters under the paradigm of complex system research. This study proposes a new way of thinking for the prevention and control of underground coal fires and helps to build a whole-process prevention and control system.

Teaching complexity in biology through agent-based simulations: the relationship between students’ knowledge of complex systems and metamodeling knowledge

Real-world complex systems research seeks to understand how systems in the world can follow the same rules of complexity. Scientists have found similarities in processes—such as self-organization, micro-to macro-level emergence, and feedback loops—in seemingly disparate phenomena such as the spread of infectious diseases and how traffic patterns are formed. Our project, BioGraph 2.0, was developed to respond to the issue of students’ disjointed understanding of biology due to the fragmented nature of how high school biology is taught in high school classrooms. We hypothesized that by framing multiple biology concepts through the lens of complexity using dynamic simulations, or models featuring complex systems processes, students would be able to see complex systems as a unifying concept throughout biology. We built a series of units modeling phenomena on biological concepts such as gene regulation, ecology, and evolution using an agent-based modeling tool called StarLogo Nova. While previous research over the last decade of this project has highlighted students’ growth in complex systems understanding, in this study, we explored the relationship between complex systems and agent-based models. We investigated pre and post intervention data from over 300 high school students to determine how their metamodeling knowledge influenced their understanding of complex systems. Through a regression analysis, we demonstrate that growth in students’ modeling understanding significantly predicted growth in complex systems understanding. We further triangulate our findings with interview data from students who highlight the importance of the modeling tool to support their complex systems learning.

Privacy Risk Assessment of Smart Home System Based on a STPA–FMEA Method

Although the smart home industry is rapidly emerging, it faces the risk of privacy security that cannot be neglected. As this industry now has a complex combination system involving multiple subjects, it is difficult for the traditional risk assessment method to meet these new security requirements. In this study, a privacy risk assessment method based on the combination of system theoretic process analysis–failure mode and effect analysis (STPA–FMEA) is proposed for a smart home system, considering the interaction and control of ‘user-environment-smart home product’. A total of 35 privacy risk scenarios of ‘component-threat-failure-model-incident’ combinations are identified. The risk priority numbers (RPN) was used to quantitatively assess the level of risk for each risk scenario and the role of user and environmental factors in influencing the risk. According to the results, the privacy management ability of users and the security state of the environment have significant effects on the quantified values of the privacy risks of smart home systems. The STPA–FMEA method can identify the privacy risk scenarios of a smart home system and the insecurity constraints in the hierarchical control structure of the system in a relatively comprehensive manner. Additionally, the proposed risk control measures based on the STPA–FMEA analysis can effectively reduce the privacy risk of the smart home system. The risk assessment method proposed in this study can be widely applied to the field of risk research of complex systems, and this study can contribute to the improvement of privacy security of smart home systems.

Dispersion and mixing dynamics of complex oil-in-water emulsions in Taylor-Couette flows.

The seminal study by G. I. Taylor (1923) has inspired generations of work in exploring and characterizing Taylor-Couette (TC) flow instabilities and laid the foundation for research of complex fluid systems requiring a controlled hydrodynamic environment. Here, TC flow with radial fluid injection is used to study the mixing dynamics of complex oil-in-water emulsions. Concentrated emulsion simulating oily bilgewater is radially injected into the annulus between rotating inner and outer cylinders, and the emulsion is allowed to disperse through the flow field. The resultant mixing dynamics are investigated, and effective intermixing coefficients are calculated through measured changes in the intensity of light reflected by the emulsion droplets in fresh and salty water. The impacts of the flow field and mixing conditions on the emulsion stability are tracked via changes in droplet size distribution (DSD), and the use of emulsified droplets as tracer particles is discussed in terms of changes in the dispersive Péclet, Capillary and Weber numbers. For oily wastewater systems, the formation of larger droplets is known to yield better separation during a water treatment process, and the final DSD observed here is found to be tunable based on salt concentration, observation time and mixing flow state in the TC cell. This article is part of the theme issue 'Taylor-Couette and related flows on the centennial of Taylor's seminal Philosophical Transactions paper (Part 2)'.

Some scientifically-methodical questions of research of complex systems

The contents of scientifically-methodical questions of research of complex systems (СS) is certain. Ways of quantitative estimations of characteristics СS are examined and examples of the scientifically-practical device of their realization are brought. The block diagram of a modelling complex as basic practical device of research СS is brought. Its primary goals, requirements to it and variants of its realization are analysed.

How, Why and When Tsallis Statistical Mechanics Provides Precise Descriptions of Natural Phenomena.

The limit of validity of ordinary statistical mechanics and the pertinence of Tsallis statistics beyond it is explained considering the most probable evolution of complex systems processes. To this purpose we employ a dissipative Landau-Ginzburg kinetic equation that becomes a generic one-dimensional nonlinear iteration map for discrete time. We focus on the Renormalization Group (RG) fixed-point maps for the three routes to chaos. We show that all fixed-point maps and their trajectories have analytic closed-form expressions, not only (as known) for the intermittency route to chaos but also for the period-doubling and the quasiperiodic routes. These expressions have the form of q-exponentials, while the kinetic equation's Lyapunov function becomes the Tsallis entropy. That is, all processes described by the evolution of the fixed-point trajectories are accompanied by the monotonic progress of the Tsallis entropy. In all cases the action of the fixed-point map attractor imposes a severe impediment to access the system's built-in configurations, leaving only a subset of vanishing measure available. Only those attractors that remain chaotic have ineffective configuration set reduction and display ordinary statistical mechanics. Finally, we provide a brief description of complex system research subjects that illustrates the applicability of our approach.

Attractor landscapes: a unifying conceptual model for understanding behaviour change across scales of observation

ABSTRACT Models and theories in behaviour change science are not in short supply, but they almost exclusively pertain to a particular facet of behaviour, such as automaticity or reasoned action, or to a single scale of observation such as individuals or communities. We present a highly generalisable conceptual model which is widely used in complex systems research from biology to physics, in an accessible form to behavioural scientists. The proposed model of attractor landscapes can be used to understand human behaviour change on different levels, from individuals to dyads, groups and societies. We use the model as a tool to present neglected ideas in contemporary behaviour change science, such as hysteresis and nonlinearity. The model of attractor landscapes can deepen understanding of well-known features of behaviour change (research), including short-livedness of intervention effects, problematicity of focusing on behavioural initiation while neglecting behavioural maintenance, continuum and stage models of behaviour change understood within a single accommodating framework, and the concept of resilience. We also demonstrate potential methods of analysis and outline avenues for future research.

Strategizing the National Economy during a Period of Burgeoning Technological Sovereignty

Topicality. The growing instability of economic systems, the transition of the global market space from a stationary state to a large extent during an extraordinary period are accompanied by an intensification of interregional and international competition for resources and factors of expanded reproduction. This is associated with such significant structural changes and transformations that they actualize the search and justification of new strategic vectors of socio-economic development of national economies for the period of “post-normality” and a longer extraordinary period. It is the application of an effective strategizing methodology that allows the developers of any strategy to focus on the search for new strategic opportunities and breakthrough transformations, focuses on determining and choosing the right and long-term vector of development of the strategizing object. The authors emphasize that “new horizons” strategies or innovative development strategies based on technological competitive advantages are the most effective, which, in the context of limited access to imports of high-tech developments and the impossibility of their cross-border transfer, in particular to Russia, forms a predominant focus on the avant-garde domestic technological developments in the justification of strategic priorities. This causes the activation of processes to form the national economy technological sovereignty and emphasizes its strategic importance. Aim. Determine the basic directions of strategizing the national economy during a period of burgeoning technological sovereignty. Tasks. Designate the strategic role of scientific and industrial sectors in the process of national economy transition to technological independence, formulate the financial and the human resources development aspects of strategizing the national economy during a period of burgeoning technological sovereignty. Methods. The scientific research is based on the general theory of Strategy and strategizing methodology developed by the Center for Strategic Research at the Institute of Mathematical Research of Complex Systems and the Economic and Financial Strategy Department at Moscow School of Economics at Lomonosov Moscow State University with the leadership of Foreign Member of the Russian Academy of Sciences, professor Vladimir L. Kvint. Results. The authors have shown the relationship between strategizing and the processes of formation the national economy technological sovereignty, clarified the role of industrial strategizing in the national economy during a period of burgeoning technological sovereignty, formulated key aspects of the financial strategy and the labor potential development strategy of the national economy during a period of burgeoning technological sovereignty. Conclusions. The process of forming the national economy during a period of burgeoning technological sovereignty must comply with the strategizing methodology, follow the long-term regulated stages and methodological principles for the development and implementation of an appropriate national strategy for the transition to technological independence. Particular attention should be paid to industrial and financial strategizing of the transition to technological independence and strategizing the labor potential development of the national economy during a period of burgeoning technological sovereignty.