Complex Systems Theory Research Articles

Systemic approach on the dynamics of the stock market

The stock market constitutes a highly complex and dynamic system, characterized by multiple interconnected elements and emerging behavioral patterns. This research analyzes the implications of adopting a systemic perspective, which allows modeling the interactions and patterns that arise within the dynamics of the stock market. Through methodologies such as agent-based modeling, systemic network analysis and complex systems theory, it is possible to represent the interconnections between various financial assets and evaluate how fluctuations in a specific asset can propagate and influence other components of the asset. market. Likewise, the importance of integrating approaches such as neural networks and machine learning with systemic analysis is emphasized, allowing the non-linear relationships inherent in financial data to be unraveled and predictive models to be developed with greater precision. This methodological synergy provides a deeper understanding of the underlying dynamics of the stock market. The main objective of this study is to explore how simulation can guide the search for the optimal portfolio, maximizing return and minimizing risk by evaluating hypothetical scenarios and making informed decisions in the field of stock investments.

Changes in Language Learners’ Affect: A Complex Dynamic Systems Theory Perspective

AbstractThis study investigated changes in motivation, self‐efficacy beliefs, and a range of emotions, including enjoyment, hope, pride, curiosity, anxiety, boredom, apathy, confusion, and shame, from a complex dynamic systems theory (CDST) perspective over a 2‐year period in the Hungarian English as a Foreign Language (EFL) context. Using the same questionnaire, we collected data four times throughout 4 semesters from 101 participants studying English in two Hungarian high schools. For data analysis, we used latent growth curve modeling (LGCM) to detect the group‐level changes in learners’ motivation, self‐efficacy, and emotions. We also employed dynamic cluster analysis to identify trends in learners’ trajectories regarding these variables. In our panel data, linear models described the data well concerning the ought‐to second language (L2) self, language learning experience, boredom, apathy, and confusion, and for enjoyment, curiosity, anxiety, and shame, nonlinear models had the best fit. We could also identify trajectories depicting attractor states and learner paths that featured influences of perturbations.

The Role of Complex Systems in Predictive Analytics for E-Commerce Innovations in Business Management

This review explores the incorporation of complex systems theory into predictive analytics in the e-commerce sector, particularly emphasizing recent advancements in business management. By analyzing the intersection of these two domains, the review emphasizes the potential of complex systems models—including agent-based modeling and network theory—to improve the precision and efficacy of predictive analytics. It will provide a comprehensive overview of the applications of emergent predictive analytics techniques and tools, including real-time data analysis and machine learning, in inventory optimization, dynamic pricing, and personalization of customer experiences. In addition, this review will suggest future research directions to advance the discipline and address the technical, ethical, and practical challenges encountered during this integration phase.

A knowledge-data dually driven paradigm for accurate identification of key blocks in complex rock slopes

Accurate identification and effective support of key blocks are crucial for ensuring the stability and safety of rock slopes. The number of structural planes and rock blocks were reduced in previous studies. This impairs the ability to characterize complex rock slopes accurately and inhibits the identification of key blocks. In this paper, a knowledge-data dually driven paradigm for accurate identification of key blocks in complex rock slopes is proposed. Our basic idea is to integrate key block theory into data-driven models based on finely characterizing structural features to identify key blocks in complex rock slopes accurately. The proposed novel paradigm consists of (1) representing rock slopes as graph-structured data based on complex systems theory, (2) identifying key nodes in the graph-structured data using graph deep learning, and (3) mapping the key nodes of graph-structured data to corresponding key blocks in the rock slope. Verification experiments and real-case applications are conducted by the proposed method. The verification results demonstrate excellent model performance, strong generalization capability, and effective classification results. Moreover, the real case application is conducted on the northern slope of the Yanqianshan Iron Mine. The results show that the proposed method can accurately identify key blocks in complex rock slopes, which can provide a decision-making basis and rational recommendations for effective support and instability prevention of rock slopes, thereby ensuring the stability of rock engineering and the safety of life and property.

Heterogeneity in momentary affective experiences related to suicidal urges in a non-clinical sample of adult handgun owners and non-owners recruited from the community

Although suicide is complex and heterogeneous, most suicide theories assume that suicidal urges occur primarily in the context of extreme emotional distress. Newer models of suicide based on complex systems theory propose greater heterogeneity in suicidal experiences across individuals and groups, such that some, but not all, suicidal thoughts, urges, and behaviors are associated with extreme negative affect. The present study investigated individual differences in affective states experienced during suicidal urges among 138 adults recruited from the community; 81 (59.1 %) owned handguns and 57 (41.6 %) did not. Participants self-reported their current affect and urge to kill themselves 6 times per day for 28 consecutive days via ecological momentary assessment. Positive and negative affect ratings varied significantly during suicidal urges. The association of positive and negative affect with suicidal urges significantly varied within and between handgun owners and non-owners. Results suggest suicidal urges are characterized by high affective heterogeneity.

Optimization model for urban ecological network connectivity considering geospatial constraints

ABSTRACT Connectivity enhancement of ecological networks is an efficient and advantageous spatial planning strategy, and it is a hot research topic to analyze ecological networks based on complex systems theory and perform model optimization. As a typical geographic network, the evolution process of ecological network, such as node and edge growth, will be subject to geospatial constraints, and the current research lacks optimization models that take the geospatial constraints into account. This study focused on urban ecological network connectivity and the distribution of nodes as well as structural fractures where the connectivity of the ecological network was hindered were analyzed. Then it introduced a local world network model to optimize network connectivity and designed a network growth mechanism with limited added nodes and optimal connections. An ecological network optimization model considering geospatial constraints was constructed, and the network closeness index was used to fit the objective function curve to select optimization schemes. This study used Shenzhen, China as a case study, and the results indicated that the initial ecological network was significantly fractured by urban construction. In the optimization scenario, the network evolution reached its optimal state at the simulation step size of t = 26. At this point, the closeness index of the optimized network was increased by 15%. The optimization model constructed in this paper emphasizes the importance of functional restoration of existing network nodes, and the results of the study can provide support for ecospatial layout optimization and management.

Factors influencing fidelity to guideline implementation strategies for improving pain care at cancer centres: a qualitative sub-study of the Stop Cancer PAIN Trial

BackgroundThe Stop Cancer PAIN Trial was a phase III pragmatic stepped wedge cluster randomised controlled trial which compared effectiveness of screening and guidelines with or without implementation strategies for improving pain in adults with cancer attending six Australian outpatient comprehensive cancer centres (n = 688). A system for pain screening was introduced before observation of a ‘control’ phase. Implementation strategies introduced in the ‘intervention’ phase included: (1) audit of adherence to guideline recommendations, with feedback to clinical teams; (2) health professional education via an email-administered ‘spaced education’ module; and (3) a patient education booklet and self-management resource. Selection of strategies was informed by the Capability, Opportunity and Motivation Behaviour (COM-B) Model (Michie et al., 2011) and evidence for each strategy’s stand-alone effectiveness. A consultant physician at each centre supported the intervention as a ‘clinical champion’. However, fidelity to the intervention was limited, and the Trial did not demonstrate effectiveness. This paper reports a sub-study of the Trial which aimed to identify factors inhibiting or enabling fidelity to inform future guideline implementation initiatives.MethodsThe qualitative sub-study enabled in-depth exploration of factors from the perspectives of personnel at each centre. Clinical champions, clinicians and clinic receptionists were invited to participate in semi-structured interviews. Analysis used a framework method and a largely deductive approach based on the COM-B Model.ResultsTwenty-four people participated, including 15 physicians, 8 nurses and 1 clinic receptionist. Coding against the COM-B Model identified ‘capability’ to be the most influential component, with ‘opportunity’ and ‘motivation’ playing largely subsidiary roles. Findings suggest that fidelity could have been improved by: considering the readiness for change of each clinical setting; better articulating the intervention’s value proposition; defining clinician roles and responsibilities, addressing perceptions that pain care falls beyond oncology clinicians’ scopes of practice; integrating the intervention within existing systems and processes; promoting patient-clinician partnerships; investing in clinical champions among senior nursing and junior medical personnel, supported by medical leaders; and planning for slow incremental change rather than rapid uptake.ConclusionsFuture guideline implementation interventions may require a ‘meta-implementation’ approach based on complex systems theory to successfully integrate multiple strategies.Trial registrationRegistry: Australian New Zealand Clinical Trials Registry; number: ACTRN 12615000064505; data: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspxid=367236&isReview=true.

Solutions of fractional differential models by using Sumudu transform method and its hybrid

This paper presents the Sumudu transform method and its hybrid for the construction of solutions of differential equations, both with integer-order and fractional derivatives. The paper discusses the construction of solutions of fractional differential equations with varying delay proportional to the independent variable by using two methods. The paper considers the mathematical model for the decay of Iodine 135 as an application of fractional differential equations in nuclear physics. The application indicates that fractional differential equations with variable delay proportional to the independent variable are a useful tool for the modeling of many anomalous phenomena in nature and in the theory of complex systems.

A multi‐dimensional evaluation system for e‐government development: Based on complex systems theory

A multi‐dimensional evaluation system for e‐government development: Based on complex systems theory

Using complex systems theory to comprehend the coordinated control effects of PM2.5 and O3 in Yangtze River Delta industrial base in China

Using complex systems theory to comprehend the coordinated control effects of PM2.5 and O3 in Yangtze River Delta industrial base in China

The First Five Years of a Phase Theory for Complex Systems and Networks

The First Five Years of a Phase Theory for Complex Systems and Networks

Optimizing Resource Allocation for Regional Employment Governance:A Dynamic Fuzzy-Set QCA Analysis of Low-Carbon Pilot Cities in China

<p style="text-align:justify"><span style="font-size:10.5pt"><span style="line-height:115%"><span style="font-family:等线"><a name="_Hlk171796579"><span lang="EN-US" style="font-family:"Times New Roman",serif">This study employs a dynamic fuzzy-set qualitative comparative analysis (fsQCA) approach, utilizing panel data from 121 low-carbon pilot cities in China from 2007 to 2019. Grounded in complex systems theory and the triple bottom line framework (Economy-Society-Environment), the research aims to optimize resource allocation to enhance regional employment governance performance. The key findings include that the initial implementation of low-carbon policies resulted in a short-term decline in employment levels, with minimal long-term impact on overall employment figures but a significant effect on high-level urban wages. Significant disparities in employment levels were observed among pilot cities, driven by regional population sizes and economic development levels. Four development models for low-carbon cities were identified: human resources-driven, energy transition-driven, industrial cluster-driven, and comprehensive factor-driven models. These models provide strategic pathways for promoting low-carbon urban development and enhancing employment. The findings offer valuable insights into governance strategies for China’s low-carbon pilot cities, facilitating the context-specific promotion of sustainable urban development and improved employment opportunities.</span></a></span></span></span></p>

Complexity Theory Approach to Innovation Ecosystems

Purpose: By nowadays, in addition to the innovation systems definitions, the research area of innovation ecosystems operation is also developing significantly. The aim of the research is to examine how the concept of the innovation ecosystem and the conceptual scope of complexity theory are related. Theoretical reference: The paper gives an overview of development of innovation ecosystems literature, and also introduces summary of general systems theory. The authors point out that the joint discussion of the two areas are very rare in the related research works. There are few examples of referring Complex Adaptive Systems as potential approach for innovation ecosystems, but detailed theoretical analysis of the topic is limited. Method: The paper analyses the relationship and possible overlap of fields of innovation ecosystems and complexity theory by evaluation of findings of content analysis and a keyword-based method. As part of the methodology presented, the research of the theme is carried out from two aspects. First, based on review-type and theoretical works, a content analysis was made to define specific key feature word sets in both innovation ecosystems and complex theory areas. Then, based on these keyword set, validation was executed based on abstracts of selected papers in order to define possible relations of the two fields. Results and Conclusion: As a result of the research, authors draw conclusions about the relationship between the definition of innovation ecosystems and complexity theory. Both the joint keywords and the non-relevant words have been pointed out. The authors concluded that there is apparent theoretical relation of the two areas from content point of view, based on analysis of relevant scientific papers. Implications of research: The outcomes of the research fundamentally influence the treatment of innovation ecosystems, which has been traditionally assumed linear, deterministic and controllable behaviour. Introducing the potential of complex systems allow opening new aspects to manage such challenges of innovation ecosystems which could not been handled in the usual way. Originality/value: The joint discussion of the topic of innovation ecosystems and complex systems theory is rather rare in the related scientific works. The novelty of the current paper is the systematic approach defined for this analysis purpose and way of validation of the pre-defined keyword set through abstracts of relevant papers. The present research addressed the preliminary discussion of the given research question, further research and analysis can be made based on larger-scale paper base.

Exploring the Nexus between Food Systems and the Global Syndemic among Children under Five Years of Age through the Complex Systems Approach.

The intricate relationship between food systems and health outcomes, known as the food-nutrition-health nexus, intersects with environmental concerns. However, there's still a literature gap in evaluating food systems alongside the global syndemic using the complex systems theory, especially concerning vulnerable populations like children. This research aimed to design a system dynamics model to advance a theoretical understanding of the connections between food systems and the global syndemic, particularly focusing on their impacts on children under five years of age. The framework was developed through a literature review and authors' insights into the relationships between the food, health, and environmental components of the global syndemic among children. The conceptual model presented 17 factors, with 26 connections and 6 feedback loops, categorized into the following 5 groups: environmental, economic, school-related, family-related, and child-related. It delineated and elucidated mechanisms among the components of the global syndemic encompassing being overweight, suffering from undernutrition, and climate change. The findings unveiled potential interactions within food systems and health outcomes. Furthermore, the model integrated elements of the socio-ecological model by incorporating an external layer representing the environment and its natural resources. Consequently, the development of public policies and interventions should encompass environmental considerations to effectively tackle the complex challenges posed by the global syndemic.

A fRAmework of the DetermInants of Arts aNd Cultural Engagement (RADIANCE): integrated insights from ecological, behavioural and complex adaptive systems theories.

Arts and cultural engagement (ACEng) is ubiquitous across every human culture since palaeolithic times, but in contemporary society, ACEng is unevenly distributed, demographically, socio-economically, geographically and politically. But what are the "determinants" of ACEng (i.e., the facilitators or barriers to people's engagement) and how can they be optimised? Despite a large body of theory and evidence on individual determinants, this work has largely occurred in disciplinary silos, which has led variously to contrasting discourses and approaches, criticism, and inconsistent findings. What we lack is a rigorous comprehensive understanding of these determinants (both those already theorised and those that have been little recognised as determinants to date) that goes beyond descriptively showing inequalities, instead explaining why these inequalities exist and how they can be overcome. This paper explores the currently recognised determinants of ACEng, and existing theoretical approaches to these determinants. Drawing on the theoretical bases of ecological systems theory, ecosocial theory and complex adaptive systems science, we conducted a review and iterative theorising process. We propose a new theoretical framework of the determinants of arts and cultural engagement (RADIANCE) developed through cross-disciplinary literature reviewing, domain mapping, and consensus building. Overall, we identified 35 different factors that can act as determinants of ACEng across micro, meso, exo, macro and chrono levels. We broadly categorised these as social (i.e. a primary feature being the interaction of people), tangible (i.e. a primary feature involving physical assets or resources or the production of physical assets), and intangible (i.e. constructs that do not have a primary physical basis but instead have a virtual or imaginary basis). The relevance and implications of this framework for broader research, policy, and practice and case studies of it in use are presented.

Human Versus Machine Intelligence: Assessing Natural Language Generation Models Through Complex Systems Theory.

The introduction of Transformer architectures - with the self-attention mechanism - in automatic Natural Language Generation (NLG) is a breakthrough in solving general task-oriented problems, such as the simple production of long text excerpts that resemble ones written by humans. While the performance of GPT-X architectures is there for all to see, many efforts are underway to penetrate the secrets of these black-boxes in terms of intelligent information processing whose output statistical distributions resemble that of natural language. In this work, through the complexity science framework, a comparative study of the stochastic processes underlying the texts produced by the English version of GPT-2 with respect to texts produced by human beings, notably novels in English and programming codes, is offered. The investigation, of a methodological nature, consists first of all of an analysis phase in which the Multifractal Detrended Fluctuation Analysis and the Recurrence Quantification Analysis - together with Zipf's law and approximate entropy - are adopted to characterize long-term correlations, regularities and recurrences in human and machine-produced texts. Results show several peculiarities and trends in terms of long-range correlations and recurrences in the last case. The synthesis phase, on the other hand, uses the complexity measures to build synthetic text descriptors - hence a suitable text embedding - which serve to constitute the features for feeding a machine learning system designed to operate feature selection through an evolutionary technique. Using multivariate analysis, it is then shown the grouping tendency of the three analyzed text types, allowing to place GTP-2 texts in between natural language texts and computer codes. Similarly, the classification task demonstrates that, given the high accuracy obtained in the automatic discrimination of text classes, the proposed set of complexity measures is highly informative. These interesting results allow us to add another piece to the theoretical understanding of the surprising results obtained by NLG systems based on deep learning and let us to improve the design of new informetrics or text mining systems for text classification, fake news detection, or even plagiarism detection.

A universal exponent governing foreign exchange rate risks

Departing from previous studies, this paper uses power laws to model foreign exchange rate risks in terms of realized foreign exchange rate (FX) variances for daily and weekly data. Empirical tests based on daily data provide strong evidence for emergent market risk behavior manifested in a common power-law exponent governing the cross section of realized FX variances. We show that this emergent market risk behavior is invariant across various time frequencies. Based on modern bootstrapping techniques, we derive a novel joint test for investigating the presence of total invariance—that is, invariance of emergent market risk behavior across time frequencies and over time. Our novel test provides strong evidence for total invariance of realized FX variances. We argue that the results are in line with the theory of complex systems—that is, even though FX risk exhibits idiosyncratic features that may originate from market-distinct factors (inflation, interest rates, public debts, etc.), emergent market behavior manifests itself in a universal power-law exponent governing the cross section of FX risks.

Strategy To Maintain The Balance Of The Marine Ecosystem By Utilizing Recycle Economy To Face The Threat Of El Nino

Human dependence on natural resources, especially in the Blue Economy sector or marine ecosystems, has led to a dominant role in ecosystem management. The El Niño phenomenon is a serious challenge that can disrupt marine ecosystems and sustainable economies. This effort aims to reduce the negative impact of El Nino on marine ecosystems, by recycling the economy from wastes in the sea. This research uses a descriptive qualitative approach to reduce the negative impact of El Niño on marine ecosystems through recycling of marine waste, creating a sustainable solution for economic growth and environmental sustainability. SWOT analysis and complex systems theory highlighted several potential strategies to stabilize the marine ecosystem, address the threat of El Niño, and boost the economy. The implementation of these strategies has a positive impact on the sustainability of the marine ecosystem and sustainable economic growth.

Crop booms as regime shifts.

A crop boom is a sudden, nonlinear and intense expansion of a new crop. Despite their large impacts, boom-bust dynamics are not well understood; booms are largely unpredictable and difficult to steer once they unfold. Based on the striking resemblances between land regime shifts and crop booms, we apply complex systems theory, highlighting the potential for regime shifts, to provide new insights about crop boom dynamics. We analyse qualitative and quantitative data of rubber and banana plantation expansion in two forest frontier regions of northern Laos. We show that preconditions, including previous booms, explain the occurrence (why) of booms, and triggers like policy and market changes explain their timing (when). Yet, the most important features of booms, their intensity and nonlinearity (how), strongly depended on internal self-reinforcing feedbacks. We identify built-in feedbacks (neighbourhood effects and imitation) and emergent feedbacks (land rush) and show that they were social in nature, multi-scale from plot to region and subject to thresholds. We suggest that these are regular features of booms and propose a definition and causal-mechanistic explanation of crop booms, examining the overlap between booms and regime shifts and the role of frontiers. We then identify opportunities for management interventions before, during and after booms.

Duality between predictability and reconstructability in complex systems

Predicting the evolution of a large system of units using its structure of interaction is a fundamental problem in complex system theory. And so is the problem of reconstructing the structure of interaction from temporal observations. Here, we find an intricate relationship between predictability and reconstructability using an information-theoretical point of view. We use the mutual information between a random graph and a stochastic process evolving on this random graph to quantify their codependence. Then, we show how the uncertainty coefficients, which are intimately related to that mutual information, quantify our ability to reconstruct a graph from an observed time series, and our ability to predict the evolution of a process from the structure of its interactions. We provide analytical calculations of the uncertainty coefficients for many different systems, including continuous deterministic systems, and describe a numerical procedure when exact calculations are intractable. Interestingly, we find that predictability and reconstructability, even though closely connected by the mutual information, can behave differently, even in a dual manner. We prove how such duality universally emerges when changing the number of steps in the process. Finally, we provide evidence that predictability-reconstruction dualities may exist in dynamical processes on real networks close to criticality.