Characteristics Of Complex Systems Research Articles

Studying economic complexity with agent-based models: advances, challenges and future perspectives

AbstractAgent-based computational economics has considerable achievements. However, it has gone too quickly into a direction similar to the one of models based on solely analytical—as opposed to algorithmic—dynamic systems of difference equations. An increasingly large focus has been put on matching moments of real-world time series of data, a set of stylised facts, or on estimation. Reasons why this is not desirable are discussed. Firstly, both estimation and inference from models will be biased, unless they represent the real data-generating processes. Secondly, surrendering the attempt to incorporate realistic microfoundations is not only against the original ACE agenda, but also is subject to a form of Lucas critique. Thirdly, characteristics of complex systems, especially differences between feedback loops and emergent phenomena that characterise systems of various levels of complexity, undermine the justification of building structurally simplistic models. That is, an attempt at reducing the interaction of many different sectors, populated with agents using various decision rules will yield information loss, i.e., some phenomena by definition are possible to emerge only in systems of higher levels of complexity. A different research agenda is proposed, with the aim of systematically analysing and uncovering the mechanisms, feedback loops and impact channels of complex multi-sectoral economic and financial systems.

Analyzing lump-type solutions in scalar field models through configurational information measure

In this paper we employ a configurational information measure, specifically the differential configurational complexity (DCC), to quantify the information content of lump-type solutions in various scalar field models, including two modified inverted ϕ4 models, the modified ϕ3 model, as well as two additional families of lump models. Our objective is to complement previous studies by providing an informational perspective that distinguishes different solutions based on their energy configurations. We explore how the DCC measure relates to energy and its applicability in analyzing degenerate states. Our findings indicate that DCC effectively correlates with the energy parameters of the solutions, offering significant insights into their informational properties. This study underscores the value of using informational metrics like DCC to deepen our understanding of the structural and dynamic characteristics of complex systems in theoretical physics.

Transitions in growing networks using a structural complexity approach

Structure changes or transitions are common in growing networks (complex networks, graphs, etc.) and must be precisely determined. The introduced quantitative measure of the structural complexity of the network based on a procedure similar to the renormalization process allows one to reveal such changes. The proposed concept of the network structural complexity accounts for the difference between the actual and averaged network structures on different scales and corresponds to the qualitative comprehension of complexity. The structural complexity can be found for the weighted networks also. The structural complexities for various types of growing networks exhibiting transitions similar to phase transitions were found—the deterministic infinite and finite size artificial networks of different natures including percolation structures, and the time series of various types of cardiac rhythms mapped to complex networks using the parametric visibility graph algorithm. In all the cases the structural complexity of the growing network reaches a maximum near the transition point: the formation of a giant component in the graph or at the percolation threshold for two-dimensional and three-dimensional square lattices when a giant cluster having a fractal structure has emerged. Therefore, the structural complexity of the network allows us to detect and study processes similar to a second-order phase transition in complex networks. The structural complexity of a network node can serve as a kind of centrality index, auxiliary, or generalization to the local clustering coefficient. Such an index provides another new ranking manner for the network nodes. Being an easily computable measure, the network structural complexity might help to reveal different features of complex systems and processes of the real world. Published by the American Physical Society 2024

A Comparative Performance Analysis of Türkiye's Health System within OECD Countries

A country's health system stands as one of its most critical infrastructures, significantly influencing the well-being of its citizens. In line with the World Health Organization's (WHO) widely accepted definition, a country's health system involves all organizations, people, and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization., 2007). As indicated in this definition, a health system carries characteristics of complex systems posing significant challenges in its management and maintenance. The complex nature of the health system also challenges the country's policymakers to improve the outcomes of the health system. Examining specific key performance indicators of the health system can assist policymakers in gaining deeper insights into its internal capacities and identifying areas for improvement. This paper provides a comparative performance analysis of Türkiye’s health system compared to other Organization for Economic Co-operation and Development (OECD) countries using selected key performance indicators of the health system's capacity and resources. The methodology involves a comprehensive review of existing literature, data collection and analysis, and performance evaluation using the Data Envelopment Analysis (DEA) technique. In this performance analysis, health systems capacity-related indicators, such as health expenditure, number of doctors, number of nurses, and number of hospital beds, are used as input variables, and life expectancy at birth and citizens’ satisfaction with the healthcare system are output variables. The analysis intends to identify best practices and potential areas for improvement within Türkiye’s health system by benchmarking insights from other OECD countries.

The role of structural and insulating material in the structure-borne noise propagation of superyachts

The insulation and noise and vibration damping characteristics of complex systems used by humans, such as automobiles, airplanes and ships are becoming increasingly important from the early design stages to ensure an adequate level of comfort. Especially in the naval fields, the design of insulation plans is crucial to prevent the propagation of noise and vibrations on board of megayachts. Since it is quite complex to identify and correctly model the sources due to their deterministic and random nature, during the early design stage, the focus is on insulation rather than reducing the acoustic levels of the sources themselves. In this article, a method for analyzing sea trials on a superyacht will be proposed with the aim of analyzing how much the insulation materials can limit the levels of vibrational energy on board during the path between sources and receivers. A new a descriptor based on harmonic transmission energy through the structure is proposed for path insertion loss experimental identification. In particular, by using spectral analyses and synchronous averaging of sound levels in various areas of the superyacht, it will be possible to study the vibroacoustic insulation of structural materials and their damping coefficient.

A Comprehensive Model for Innovation Ecosystems

Abstract Research on innovation ecosystems is mostly approached from the point of view of institution-oriented aspects, which typically focus on the structural elements of innovation systems. However, this perspective has limitations to describe a really functioning innovation system, as it does not take into account its complex nature of dynamics and the functional features. The authors run research on models of innovation ecosystems based on complexity theory principles. The presented perspective, based on complexity theory, takes into account not only the structural and the complex but also the dynamic feature of the innovation ecosystems, still using the triple helix approach as a basis. The objective of the article is to present a system model framework for innovation ecosystems, that can be used for development of technology and science parks management models. The approach of the research is based on literature review and discussion of relevant results, followed by explanation and concluding a possible innovation ecosystem framework model, finally demonstrated its interpretation through a specific case. In its first part, the article gives a general overview on complex systems from point of related findings on innovation ecosystems. Based on literature review, the preliminary analysis of the authors in the subject is given. Then, the relevant model features are described, then and the proof of the concept is explained according to the research. The descriptive features of complex systems based on the referenced papers are used to interpret the proposed model. The main outcome of the article is a comprehensive innovation ecosystem model as a tool to understand and analyse such systems based on actors, functions and key factors. The authors demonstrated in the article that features of complex systems not only help describing innovation ecosystems but also support defining key model elements. An empiric case of ZalaZONE Technology Park was shown related to the presented innovation ecosystem model. Finally, the next steps of the model development are outlined and advice is concluded for application of the model in the practice and in further research. The novelty of the article is pointing out that the simple model of triple helix and the complexity approach as a view for modelling the real circumstances, can be well-combined to outline a comprehensive model for innovation ecosystems.

Chaotic laser time series prediction based on an improved logistic mapping algorithm echo state network

Chaos synchronization plays vital functions in the fields of optical chaos secure communication. The synchronization performance can be significantly degraded by parameter mismatches between the chaotic transmitter and receiver. In this paper, the Deep-Logistical Mapping Echo State Network (D-LMESN) is proposed to enhance the performance of chaos synchronization. The network is upgraded by using an improved logical mapping algorithm and a deep reserve pool structure with phase space reconstruction. Results show that D-LMESN exhibits better performance in the prediction of chaotic time series, thanks to the adaptive parameter adjustment, which increases the ability to capture the dynamic characteristics of complex systems. Compared with ESN, the mean square error of this model is reduced by 55% and 72%, respectively, in chaotic laser simulation and actual data experiments. This provides a new possibility, to our knowledge, for the development of chaotic secure communication.

A novel and effective method for characterizing time series correlations based on martingale difference correlation.

Analysis of correlation between time series is an essential step for complex system studies and dynamical characteristics extractions. Martingale difference correlation (MDC) theory is mainly concerned with the correlation of conditional mean values between response variables and predictor variables. It is the generalization and deepening of the Pearson correlation coefficient, Spearman correlation coefficient, Kendall correlation coefficient, and other statistics. In this paper, on the basis of phase space reconstruction, the generalized dependence index (GDI) is proposed by using MDC and martingale difference divergence matrix theories, which can measure the degree of dependence between time series more effectively. Moreover, motivated by the theoretical framework of the refined distance correlation method, the corresponding dependence measure (DE) is employed in this paper to construct the DE-GDI plane, so as to comprehensively and intuitively distinguish different types of data and deeply explore the operating mechanism behind the relevant time series and complex systems. According to the performances tested by the different simulated and real-world data, our proposed method performs relatively reasonably and reliably in dependence measuring and data distinguishing. The proposal of this complex data clustering method can not only recognize the features of complex systems but also distinguish them effectively so as to acquire more relevant detailed information.

One of the major challenges of masking the bitter taste in medications: an overview of quantitative methods for bitterness.

The aim of the present study was to carry out a systematic research on bitterness quantification to provide a reference for scholars and pharmaceutical developers to carry out drug taste masking research. Significance: The bitterness of medications poses a significant concern for clinicians and patients. Scientifically measuring the intensity of drug bitterness is pivotal for enhancing drug palatability and broadening their clinical utility. The current study was carried out by conducting a systematic literature review that identified relevant papers from indexed databases. Numerous studies and research are cited and quoted in this article to summarize the features, strengths, and applicability of quantitative bitterness assessment methods. In our research, we systematically outlined the classification and key advancements in quantitative research methods for assessing drug bitterness, including in vivo quantification techniques such as traditional human taste panel methods, as well as in vitro quantification methods such as electronic tongue analysis. It focused on the quantitative methods and difficulties of bitterness of natural drugs with complex system characteristics and their difficulties in quantification, and proposes possible future research directions. The quantitative methods of bitterness were summarized, which laid an important foundation for the construction of a comprehensive bitterness quantification standard system and the formulation of accurate, efficient and rich taste masking strategies.

Incoherence: A Generalized Measure of Complexity to Quantify Ensemble Divergence in Multi-Trial Experiments and Simulations.

Complex systems pose significant challenges to traditional scientific and statistical methods due to their inherent unpredictability and resistance to simplification. Accurately detecting complex behavior and the uncertainty which comes with it is therefore essential. Using the context of previous studies, we introduce a new information-theoretic measure, termed "incoherence". By using an adapted Jensen-Shannon Divergence across an ensemble of outcomes, we quantify the aleatoric uncertainty of the system. First we compared this measure to established statistical tests using both continuous and discrete data. Before demonstrating how incoherence can be applied to identify key characteristics of complex systems, including sensitivity to initial conditions, criticality, and response to perturbations.

Node-layer duality in networked systems

Real-world networks typically exhibit several aspects, or layers, of interactions among their nodes. By permuting the role of the nodes and the layers, we establish a new criterion to construct the dual of a network. This approach allows to examine connectivity from either a node-centric or layer-centric viewpoint. Through rigorous analytical methods and extensive simulations, we demonstrate that nodewise and layerwise connectivity measure different but related aspects of the same system. Leveraging node-layer duality provides complementary insights, enabling a deeper comprehension of diverse networks across social science, technology and biology. Taken together, these findings reveal previously unappreciated features of complex systems and provide a fresh tool for delving into their structure and dynamics.

Essential New Complexity-Based Themes for Patient-Centered Diagnosis and Treatment of Dementia and Predementia in Older People: Multimorbidity and Multilevel Phenomenology.

Dementia is a highly prevalent condition with devastating clinical and socioeconomic sequela. It is expected to triple in prevalence by 2050. No treatment is currently known to be effective. Symptomatic late-onset dementia and predementia (SLODP) affects 95% of patients with the syndrome. In contrast to trials of pharmacological prevention, no treatment is suggested to remediate or cure these symptomatic patients. SLODP but not young onset dementia is intensely associated with multimorbidity (MUM), including brain-perturbating conditions (BPCs). Recent studies showed that MUM/BPCs have a major role in the pathogenesis of SLODP. Fortunately, most MUM/BPCs are medically treatable, and thus, their treatment may modify and improve SLODP, relieving suffering and reducing its clinical and socioeconomic threats. Regrettably, the complex system features of SLODP impede the diagnosis and treatment of the potentially remediable conditions (PRCs) associated with them, mainly due to failure of pattern recognition and a flawed diagnostic workup. We suggest incorporating two SLODP-specific conceptual themes into the diagnostic workup: MUM/BPC and multilevel phenomenological themes. By doing so, we were able to improve the diagnostic accuracy of SLODP components and optimize detecting and favorably treating PRCs. These revolutionary concepts and their implications for remediability and other parameters are discussed in the paper.

Mapping complex public health problems with causal loop diagrams.

This paper presents causal loop diagrams (CLDs) as tools for studying complex public health problems like health inequality. These problems often involve feedback loops-a characteristic of complex systems not fully integrated into mainstream epidemiology. CLDs are conceptual models that visualize connections between system variables. They are commonly developed through literature reviews or participatory methods with stakeholder groups. These diagrams often uncover feedback loops among variables across scales (e.g. biological, psychological and social), facilitating cross-disciplinary insights. We illustrate their use through a case example involving the feedback loop between sleep problems and depressive symptoms. We outline a typical step-by-step process for developing CLDs in epidemiology. These steps are defining a specific problem, identifying the key system variables involved, mapping these variables and analysing the CLD to find new insights and possible intervention targets. Throughout this process, we suggest triangulating between diverse sources of evidence, including domain knowledge, scientific literature and empirical data. CLDs can also be evaluated to guide policy changes and future research by revealing knowledge gaps. Finally, CLDs may be iteratively refined as new evidence emerges. We advocate for more widespread use of complex systems tools, like CLDs, in epidemiology to better understand and address complex public health problems.

How can firms implement smart manufacturing through supply chain collaboration? A multiple-case study from China

The complex system characteristics of Smart manufacturing (SM) require firms collaborating with supply chain members to implement SM transformation. However, the motives, methods, and influencing factors of such collaboration remain unclear. Addressing this gap, we conduct a multi-case study of four manufacturing firms and obtain three findings based on complex adaptive system theory. First, the motives of supply chain collaboration include the adaptation to the environment of high customisation, technical complexity, policy, and horizontal competition. Second, firms collaborate with supply chain members in terms of equipment customisation and information interconnection, to establish the data and equipment foundation of SM. This process is affected by the scale and volume of invested resources. Based on these, firms further build collaborative platforms to realise the integrated management of all supply chain links and improve the SM capability through the iteration of solutions, which are affected by the degree of security in data sharing. Third, supply chain collaboration improves the quality and satisfaction, reduces the cost, and shortens the cycle of SM implementation. This study not only addresses whether and how supply chain collaboration influences SM implementation, but also provides guidance to help companies implement SM through a gradual multi-stage collaboration in supply chain.

“I'm afraid I can't handle students’ questions on the spot!”: Complex interactions between English research writing teachers’ pedagogical content knowledge and instructional practices

AbstractDespite recent proliferation in scholarship on English research writing (ERW) and its teaching, ERW teachers’ developing knowledge remains under‐researched. This multiple case study, featuring four ERW teachers working in the same university in Central China, builds on the pedagogical content knowledge (PCK) framework to delineate the complexities in participants’ knowledge–practice nexus. Data sources include observations, interviews, journal entries, and teaching artifacts. Data analyses reveal that the complex interactions between PCK components have resulted in idiosyncrasies in participants’ instructional decisions or practices concerning selecting teaching points, utilizing own disciplinary articles, deciding on pedagogical design, and assigning writing tasks. It is also found that participants who are more assured in their PCK repertoire tend to feel more secure in teacher–student interactions and adopt more student‐centered pedagogies. Participants’ knowledge–practice nexus exhibits complex system features of self‐organizing and self‐transforming, as well as self‐knowing. Implications for transitional ERW teachers’ practices and professional development are discussed.

TBM construction risk analysis based on fuzzy Bayesian network

ABSTRACT Aiming at the characteristics of various TBM construction risk factors, fuzzy and complex systems, and frequent shutdown accidents, a TBM construction risk analysis methodology based on a fuzzy Bayesian network is proposed. This method mainly selects eighteen risk factors to construct an interpretive structural model. These factors affect the TBM construction from the four aspects of the geological risk, equipment risk, management risk, and technical risk encountered during the construction of a full-face mechanical excavation of the tunnel. A five-level hierarchical directed graph is extracted by matrix operation to determine the relationship between factors. In the case of incomplete data, the Bayesian network model can be established by employing the prior probability to describe the state of the root node through expert scoring, ideal for system predictions lacking data. Finally, the main risk factors affecting the TBM construction are introduced and discussed, and the TBM construction of the West Second Tunnel of the Beijing Water Supply Phase II Project is taken as an example to compare the evaluation results with those of the actual construction status. The obtained results are not much different from those of the actual situation, which proves that the proposed method is scientific and can provide a reliable basis for analogous projects.

Pioneering a Framework for Robust Telemedicine Technology Assessment (Telemechron Study)

The field of technology assessment in telemedicine is garnering increasing attention due to the widespread adoption of this discipline and its complex and heterogeneous system characteristics, making its application complex. As part of a national telemedicine project, the National Center for Innovative Technologies in Public Health at the Italian National Institute of Health played the role of promoting and utilizing technology assessment tools within partnership projects. This study aims to outline the design, development, and application of assessment methodologies within the telemedicine project proposed by the ISS team, utilizing a specific framework developed within the project. The sub-objectives include evaluating the proposed methodology’s effectiveness and feasibility, gathering feedback for improvement, and assessing its impact on various project components. The study emphasizes the multifaceted nature of action domains and underscores the crucial role of technology assessments in telemedicine, highlighting its impact across diverse realms through iterative interaction cycles with project partners. Both the impact and the acceptance of the methodology have been assessed by means of specific computer-aided web interviewing (CAWI) tools. The proposed methodology received significant acceptance, providing valuable insights for refining future frameworks. The impact assessment revealed a consistent quality improvement trend in the project’s products, evident in methodological consolidations. The overall message encourages similar initiatives in this domain, shedding light on the intricacies of technology assessment implementation. In conclusion, the study serves as a comprehensive outcome of the national telemedicine project, witnessing the success and adaptability of the technology assessment methodology and advocating for further exploration and implementation in analogous contexts.

Structural complexity predicts consensus readability in online discussions

The intricate relationship between structure and function spans various disciplines, from biology to management, offering insights into predicting interesting features of complex systems. This interplay is evident in online forums, where the organization of the threads interacts with the message’s meaning. Assessing readability in these discussions is vital for ensuring information comprehension among diverse audiences. This assessment is challenging due to the complexity of natural language compounded by the social and temporal dynamics within social networks. One practical approach involves aggregating multiple readability metrics as a consensus alignment. In this study, we explore whether the structural complexity of online discussions can predict consensus readability without delving into the semantics of the messages. We propose a consensus readability metric derived from well-known readability tests and a complexity metric applied to the tree structures of Reddit discussions. Our findings indicate that this proposed metric effectively predicts consensus readability based on the complexity of discourse structure.

What about adaptiveness? The case of organisational resilience and cognition

This paper makes the very simple, perhaps straightforward point that adaptiveness cannot be taken for granted when analysing a complex system. The paradigm of Complex Adaptive Systems (CAS) theory makes it clear that a key feature of complex systems is the ability to adapt to changes in their environment. This is, indeed, relevant to many systems (e.g., living and social systems) since change is embedded in the way in which systems evolve over time. At the same time, adaptiveness is a strong assumption to make, since it prioritises change over stability and it can be a straight jacket, especially when it comes to studying complexity in the context of human social organising. By using a Case Study, this paper highlights the limits of a focus on adaptiveness and pushes for a more “neutral” perspective that allows researchers to appreciate a wider set of mechanisms, norms, and behaviours pertaining to complex social systems.

Self-organization as a mechanism of resilience in dryland ecosystems

Self-organized spatial patterns are a common feature of complex systems, ranging from microbial communities to mussel beds and drylands. While the theoretical implications of these patterns for ecosystem-level processes, such as functioning and resilience, have been extensively studied, empirical evidence remains scarce. To address this gap, we analyzed global drylands along an aridity gradient using remote sensing, field data, and modeling. We found that the spatial structure of the vegetation strengthens as aridity increases, which is associated with the maintenance of a high level of soil multifunctionality, even as aridity levels rise up to a certain threshold. The combination of these results with those of two individual-based models indicate that self-organized vegetation patterns not only form in response to stressful environmental conditions but also provide drylands with the ability to adapt to changing conditions while maintaining their functioning, an adaptive capacity which is lost in degraded ecosystems. Self-organization thereby plays a vital role in enhancing the resilience of drylands. Overall, our findings contribute to a deeper understanding of the relationship between spatial vegetation patterns and dryland resilience. They also represent a significant step forward in the development of indicators for ecosystem resilience, which are critical tools for managing and preserving these valuable ecosystems in a warmer and more arid world.