Complex Adaptive Systems Research Articles

A new method for enhancing collective intelligence using expert’s knowledge

ABSTRACT Collective intelligence refers to the ability of groups to problem-solving, and decision-making. Such ability is not often exhibited when group members act individually. In addition, collective intelligence is interdisciplinary research involving biology, psychology, computer science, complex sciences, cognitive studies, etc. In recent years, the rapid development of social media has significantly changed how humans interact and communicate with each other. Accordingly, many research results have shed light on how social media fosters collective intelligence. This paper presents an approach to enhancing the collective intelligence of crowds based on providing a small expert’s knowledge related to the given problem. In addition, collective members are inspired by individuals on social networks. For such a task, graph theory is used to represent the relationship between collective members. Computational experiments have been carried out showing the positive role of the proposed method in enhancing collective intelligence. Finally, we discuss some further issues on improving the collective intelligence of crowds.

Underexplored Dimensions of Uncertainty and Risk in Human Development Policy Call for a New Paradigm

The human development paradigm like many other development policy domains, old and new, is being challenged by a fundamentally new context that has been described by many as a VUCA world meaning Volatile, Uncertain, Complex, and Ambiguous. These factors are of course not independent of each other, but interactive, thus making for a very difficult environment in which public policy must be made. The Human Development Report (HDR) made a wise and topical choice therefore in selecting uncertain unsettled lives as the theme for the 2021–2022 report. This paper first identifies some of the strengths of the current human development policy analysis but then proceeds to outline multiple concrete, though novel, ways of addressing the challenges of uncertain times and unsettled lives. These include the need for unpackaging the concept of uncertainty, highlighting both differences and linkages between risk and uncertainty, and further implications for policymaking. In addition, we discuss new and promising ways of thinking about uncertainty in policy issues around human development including complexity theory and complex adaptive systems thinking as well as quantum-like frameworks. Finally, we discuss applied spirituality which, in our opinion, can be of great value in engaging with unsettled lives. Ours is an attempt to call for a future paradigm in policy thinking, and social and human sciences as a whole.

THE INFLUENCE OF TEACHING COMPETENCIES ON TEACHERS’ PERFORMANCE AND STUDENTS’ ACADEMIC ACHIEVEMENT IN PRIMARY SCIENCE EDUCATION

The teaching competencies of science teachers significantly influence students' science academic achievement. It shapes the students' understanding of complex science concepts and their ability to excel in scientific exploration, ultimately nurturing their scientific literacy. This study assessed the teaching competencies influencing the teaching performance of 58 primary science teachers relative to the academic achievement of their students in science at the Tinoc District, Tinoc, Ifugao, Philippines. The significant differences by teacher demographics were also determined. This is relevant to the district's observed low scientific literacy and achievement of students in science education as assessed in recent years. Using a quantitative approach, the study employed a modified questionnaire that was validated and pilot-tested. Results disclosed that the science teachers strongly agreed that professionalism is the most common teaching competence influencing their performance, indicating their adherence to local and national standards and code of conduct. They also strongly agreed with teaching effectiveness, personal skills, educational planning, and management skills. Generally, there were no significant differences among the teaching competencies regardless of the teachers' gender, age, highest educational attainment, and years in service. Meanwhile, the students’ science academic achievement was determined to be lower average, suggesting low scientific literacy. Correspondingly, the teaching competencies and students' academic achievement were not correlated. Nonetheless, the teacher's capacity to impact the learners' scientific exploration and achievement remains intact. Further studies are recommended to be undertaken. Keywords: primary science education, primary science teachers, students’ science academic achievement, teaching competency, teacher performance

Bridging the gap: Implementation science, knowledge translation, and innovation for advancing maternal health sustainable development goals

Background: Maternal health remains a critical global concern, with sustainable development goal 3 aiming to reduce the maternal mortality ratio and ensure universal access to reproductive healthcare by 2030. According to a 2023 report, a woman dies from preventable causes related to pregnancy and childbirth every 2 minutes, and almost 70% of maternal deaths occur in sub-Saharan Africa. The reduction of maternal mortality and promotion of maternal health and well-being are complex tasks. Objective: To understand how a synergistic approach integrating complex interventions’ research, implementation science principles, knowledge translation, and innovative solutions can be leveraged to achieve sustainable improvements in maternal health outcomes in different contexts. Methods: This study employed sequential and iterative critical interpretive synthesis (CIS), integrative literature (ILR) review with multiscale analysis of extant literature on maternal health starting with the landmark Lancet series on “Maternal Survival” 2006 (5 papers); 2016 “Maternal Health” series (6 papers); the 2023 Series – “Maternal health in Perinatal period and Beyond” (4 papers); and health sector strategic plans. Results: Despite knowledge that healthcare is a complex adaptive system (CAS), whereas interventions for maternal health are considered complex interventions, the predominant implementation approaches assume a linear cause-effect approach, leading to the observed stagnation in maternal health. Conclusion: The effectiveness of complex interventions and their success in reaching relevant populations is critically influenced by their implementation in each context. A linear understanding of evidence translation, from ‘bench to bedside’, is naïve. This is because those who deliver, manage, and receive healthcare, negotiate multiple forms and sources of evidence, which complement and compete within a complex system of institutional logics.

Optimising peak energy reduction in networks of buildings

Buildings are amongst the world’s largest energy consumers and simultaneous peaks in demand from networks of buildings can decrease electricity system stability. Current mitigation measures either entail wasteful supply-side over-specification or complex centralised demand-side control. Hence, a simple schema is developed for decentralised, self-organising building-to-building load coordination that requires very little information exchange and no top-down management—analogous to other complex systems with short range interactions, such as coordination between flocks of birds or synchronisation in fireflies. Numerical and experimental results reveal that a high degree of peak flattening can be achieved using surprisingly small load-coordination networks. The optimum reductions achieved by the simple schema can outperform existing techniques, giving substantial peak-reductions as well as being remarkably robust to changes in other system parameters such as the interaction network topology. This not only demonstrates that significant reductions in network peaks are achievable using remarkably simple control systems but also reveals interesting theoretical results and new insights which will be of great interest to the complexity and network science communities.

Conceptual Model for Intentional Change in Violent Extremist Jihadist Organisations

This paper explores a new approach to violent extremism through complex adaptive systems and system of systems analysis. Within both disciplines, the paper will show the simplified model developed about how violent extremist jihadist organisations learn, adapt, innovate or change in an intentional way. Because of its weakness, terrorists always look for the relative advantage and they reach it, among others, via learning, adaptation, innovation, ultimately, through intentional change. Understanding their dynamics intentional change will facilitate disrupting terrorist organisations.

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.

Deep quantum graph dreaming: deciphering neural network insights into quantum experiments

Despite their promise to facilitate new scientific discoveries, the opaqueness of neural networks presents a challenge in interpreting the logic behind their findings. Here, we use a eXplainable-AI technique called inception or deep dreaming, which has been invented in machine learning for computer vision. We use this technique to explore what neural networks learn about quantum optics experiments. Our story begins by training deep neural networks on the properties of quantum systems. Once trained, we ‘invert’ the neural network—effectively asking how it imagines a quantum system with a specific property, and how it would continuously modify the quantum system to change a property. We find that the network can shift the initial distribution of properties of the quantum system, and we can conceptualize the learned strategies of the neural network. Interestingly, we find that, in the first layers, the neural network identifies simple properties, while in the deeper ones, it can identify complex quantum structures and even quantum entanglement. This is in reminiscence of long-understood properties known in computer vision, which we now identify in a complex natural science task. Our approach could be useful in a more interpretable way to develop new advanced AI-based scientific discovery techniques in quantum physics.

Conceptualizing Forest Operations Planning and Management Using Principles of Functional Complex Systems Science to Increase the Forest’s Ability to Withstand Climate Change

The sustainable management of forest resources is greatly influenced by forest operations (FO). Interactions between humans and nature describe how people engage with and are impacted by the natural world. As we enter the Anthropocene epoch, we are being compelled to reevaluate our past and present methods of managing and planning our forest operations in order to find new ones that are more adaptable and successful at addressing the growing unpredictability resulting from accelerating global change. We briefly discuss the goals and constraints of the prior and current management and planning principles for forest operations in this study, focusing on how these principles have evolved on a worldwide scale. We then propose a promising idea, such as managing forest operations as complex adaptive systems and approaches based on resilience and sustainable use of forest resources, in order to achieve the necessary economic, social, and ecological goals. An in-depth understanding of the ecological, economic, and social factors that influence forest resilience is necessary for planning and managing forest operations efficiently. The proposed strategy combines the effectiveness of forest operations with a functional, complex network approach in order to manage forests for the Anthropocene.

Mapping the Dynamic Complexity of Sexual and Gender Minority Healthcare Disparities: A Systems Thinking Approach.

Sexual and gender minority (SGM) populations experience extensive health disparities compared to their straight and cisgender counterparts. The importance of addressing these disparities is paramount, as SGM groups often encounter significant barriers to accessing comprehensive healthcare, including societal stigma, provider bias, and financial constraints. This study utilizes a community-based system dynamics approach to understand and visualize the barriers to and facilitators of healthcare engagement for SGM groups across their life course. It aims to identify core constructs, relationships, and dynamic feedback mechanisms related to the experiences of connection/disconnection with physical, mental, and dental healthcare of SGM individuals. Barriers to access, such as discriminatory practices and the limited availability of SGM-informed healthcare professionals, exacerbate these disparities, underscoring the urgency of developing targeted interventions. System dynamics, a complex systems science (CSS) methodology, was used for this research. Group model building sessions were conducted with diverse SGM groups, including youth, older adults, and trans and gender-expansive community members. Causal loop diagrams were developed according to an iterative process, and a meta-model of their collective experiences was created. The study revealed extensive, dynamic, and shifting structural barriers for SGM community members accessing healthcare. Societal and structural stigma, provider bias, and pathologization were identified as significant barriers throughout their life course. Community-led interventions and SGM-focused holistic healthcare were identified as critical facilitators of SGM healthcare connection. The findings highlight the need for SGM-affirming and culturally responsive healthcare settings. This paper calls for a concerted effort from SGM health researchers to use CSS in developing interventions to reduce SGM health disparities.

Practicing acroyoga in the space of possibilities: Complexity profile as a heuristic device in sport and exercise research

ABSTRACT This article explores the application of complexity science in sports and exercise research, introducing Ashby’s law of requisite variety, Boisot’s and McKelvey’s law of requisite complexity, and Bar-Yam’s complexity profile. These frameworks emphasize the role of complexity in system functionality and shed light on the delicate balance between complexity and scale. Utilising acroyoga as a novel research context, an interpretivist study was conducted to delve into the activity’s complexity profile, revealing the intricate interplay between fostering creativity and maintaining essential regulations. Examining acroyoga practitioners’ and influencers’ perspectives through the complexity profile underscores the sport’s complexity over scale, offering diverse physical, cognitive, affective, and social benefits. However, the limited emphasis on scaling might confine acroyoga to the fringes, hindering widespread acceptance. This study demonstrates the complexity profile as a heuristic tool, challenging assumptions and encouraging innovative perspectives. Acroyoga serves as a compelling case study, highlighting the complexity-scale balance and paving the way for further exploration and sports-related policy formulation.

A New Paradigm for Nurse Leader Decision-Making Within Complex Adaptive Systems.

Health care is a complex and ever-changing environment for nurse leaders and other health care industry decision-makers. The prevailing leadership and decision-making models, rooted in Industrial Age principles, often struggle to adapt to the complexities of modern health care. This article explores the foundations of complexity science and its application to health care decision-making, highlighting the importance of understanding systems dynamics and embracing complexity. Drawing from systems knowledge, the Cynefin Sensemaking Framework, and understanding how to develop enabling constraints, nurse leaders can navigate the complexities of health care by identifying the nature of the problem and applying appropriate decision-making strategies, fostering agility and innovation. By embracing complexity and adopting adaptive leadership approaches, nurse leaders can pragmatically navigate the complexities of modern health care and drive transformative change. This manuscript provides methods for nurse leaders to enhance decision-making within the dynamic landscape of health care as a complex adaptive system.

Refining the causal loop diagram: A tutorial for maximizing the contribution of domain expertise in computational system dynamics modeling.

Complexity science and systems thinking are increasingly recognized as relevant paradigms for studying systems where biology, psychology, and socioenvironmental factors interact. The application of systems thinking, however, often stops at developing a conceptual model that visualizes the mapping of causal links within a system, e.g., a causal loop diagram (CLD). While this is an important contribution in itself, it is imperative to subsequently formulate a computable version of a CLD in order to interpret the dynamics of the modeled system and simulate "what if" scenarios. We propose to realize this by deriving knowledge from experts' mental models in biopsychosocial domains. This article first describes the steps required for capturing expert knowledge in a CLD such that it may result in a computational system dynamics model (SDM). For this purpose, we introduce several annotations to the CLD that facilitate this intended conversion. This annotated CLD (aCLD) includes sources of evidence, intermediary variables, functional forms of causal links, and the distinction between uncertain and known-to-be-absent causal links. We propose an algorithm for developing an aCLD that includes these annotations. We then describe how to formulate an SDM based on the aCLD. The described steps for this conversion help identify, quantify, and potentially reduce sources of uncertainty and obtain confidence in the results of the SDM's simulations. We utilize a running example that illustrates each step of this conversion process. The systematic approach described in this article facilitates and advances the application of computational science methods to biopsychosocial systems. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Abstract IA020: Microphysiological systems to decipher tumor-stromal interactions in colorectal cancer

Abstract Cancer is a complex adaptive system driven by the intricate interplay between tumor cells and their microenvironment. Among these interactions, cancer-associated fibroblasts (CAFs), the predominant cellular component of the tumor stroma, are frequently linked with an unfavorable prognosis and play an important role in the progression of various cancers. Although extensive literature has underscored the impact of CAFs on cancer cell behaviors such as proliferation and invasion, the stromal influence on phenotypic plasticity in the context of treatment response and metastasis remains largely unexplored. Furthermore, preclinical treatment studies often concentrate on drug-induced changes in tumor cells with little investigation into the impact on surrounding stromal cells. To enhance our comprehension of the intricate and dynamic interactions between cancer cells and their microenvironment, we are utilizing quantitative high-content imaging in conjunction with more physiologically relevant patient-derived model systems, such as organoids and organ-on-chip technologies. Here we will describe our experimental workflows and demonstrate how the incorporation of mathematical modeling enhances our understanding of 3D organoid growth dynamics and facilitates a more detailed examination of drug resistance influenced by tumor plasticity. One illustrative example we will emphasize involves the treatment of CAFs with an anti-epidermal growth factor receptor (EGFR) targeted agent, cetuximab, resulting in an increased secretion of epidermal growth factor (EGF). This heightened EGF secretion was sufficient to render neighboring tumor cells resistant to cetuximab treatment. These efforts aim to increase our understanding of tumor-stromal interactions in cancer evolution, particularly emphasizing the functional and therapeutic potential of CAFs, through the integration of expertise across disciplines. Citation Format: Shannon Mumenthaler. Microphysiological systems to decipher tumor-stromal interactions in colorectal cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr IA020.

Complex Adaptive System Theory, Agent-Based Modeling, and Simulation in Dominant Technology Formation

Complex Adaptive System Theory, Agent-Based Modeling, and Simulation in Dominant Technology Formation

Abstract WMP99: A Novel Network Analysis of County- and Individual-Level Factors Associated With Functional Outcomes After Stroke

Background: Social circumstances may contribute to health disparities in patients with stroke through indirect and interdependent mechanisms that are not well understood. Objective: This study used complexity science methodology to examine the interplay between county- and individual-level social and clinical factors influencing stroke functional outcomes. Methods: As part of the Get With the Guidelines-Stroke (GWTG-S) Data Challenge, the GWTG-S Registry was merged with county-level Institute for Health Metrics and Evaluation data. Patients diagnosed with stroke (ischemic, subarachnoid, or intracerebral hemorrhage) were included. Multilayer networks were constructed by estimating mixed graphical models of 32 nodes across four layers, including social (county- and patient-level) and clinical (comorbidities and encounter) factors. Social determinant networks were estimated for patients with less favorable (ie., functional dependence or death, modified Rankin Score [mRS] 3-6) versus favorable outcomes (ie., functional independence, mRS 0-2). Network structure and node centrality were compared between mRS groups using bootstrap permutation analyses. Hub nodes were defined by betweenness centrality and facilitate effects across the network. Results: From 2013-2019, 990 721 (62.3%) stroke patients had mRS of 3-6 and 597 477 (37.6%) had mRS 0-2 at discharge. Compared to the mRS 0-2 group, the mRS 3-6 group’s social determinants network had greater global connectivity (p<0.001), and homelessness (p<0.001) and Black race (p<0.001) were hub nodes. Unique to the mRS 3-6 group, younger patients were more likely to identify as homeless (p=0.031), uninsured (p=0.001), and live in a county with lower per capita income (p<0.001). Conclusions: The effects of social determinants were significantly greater in patients with less favorable functional outcomes. Homelessness and race play critical roles in mediating the impacts of county- and individual-level social determinants on downstream disparities in stroke outcomes. Particularly among younger patients, housing, insurance status, and income may serve as a critical leverage point for county-level interventions to improve function after stroke.

Penggunaan Media Video & Gambar Dalam Pembelajaraan IPAS Pada Siswa Kelas 4 SDN 1 Wonoboyo

This research aims to explore the use of video and image media in learning Natural Sciences (IPAs) for grade 4 students at SDN 1 Wonoboyo. Visual media has become an important component in modern education, and this research investigates how the use of this media can influence students' understanding and motivation in learning sciences.
 The research results show that the use of video and image media in science learning positively influences students' understanding of the concepts being taught. Students also show higher interest and motivation to learn when visual media is used. Even though it requires more intensive preparation for teachers, the use of visual media is considered effective in explaining complex science concepts.
 This research provides insight into the importance of using visual media in improving the quality of science learning at the elementary level and suggests that teachers and schools continue to develop their skills in integrating this media in the curriculum. Video and image media have great potential in motivating students and facilitating better understanding of science material, so they can be effective tools in improving education at the elementary level.

Patient level dataset to study the effect of COVID-19 in people with Multiple Sclerosis

Multiple Sclerosis (MS) is an inflammatory autoimmune disease of the central nervous system, causing increased vulnerability to infections and disability among young adults. Ever since the outbreak of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 infections, there have been concerns among people with MS (PwMS) about the potential interactions between various disease-modifying therapies and COVID-19. The COVID-19 in MS Global Data Sharing Initiative (GDSI) was initiated in 2020 with the aim of addressing these concerns. This paper focuses on the anonymisation and publicly releasing of a GDSI sub-dataset, comprising data entered by PwMS and clinicians using a fast data entry tool. The dataset includes information on demographics, comorbidities and hospital stay and COVID-19 symptoms of PwMS. The dataset can be used to perform different statistical analyses to improve our understanding of COVID-19 in MS. Furthermore, this dataset can also be used within the context of educational activities to educate different stakeholders on the complex data science topics that were used within the GDSI.

Teachers' Views Regarding the Character of Students' Perseverance in Science Learning

Purpose of the study: The primary objective of this research was to describe teachers' views regarding the importance of students' persistent character in learning science when teaching class VII students at Junior high school.
 Methodology: This research used the qualitative Miles and Huberman method with a sample of 1 teacher representing 3 science teachers at Junior high schools in Indonesia, Myanmar, and Vietnam. Researchers conducted interviews with science teachers. The sampling technique used was purposive sampling. Research data was taken using the interview method. The data analysis used is the results of interviews linked to theories from various literature.
 Main Findings: The findings of this research show that the results of teacher interviews at Junior high school, teachers said that persistence plays an important role in creating an effective learning environment. Students who have a high level of perseverance will achieve better results in understanding complex science concepts and prepare students who are tough and never stop learning.
 Novelty/Originality of this study: The benefit of this research is to know the importance of students' persistent character in understanding science learning. Hopefully, future researchers will be better off adding student and teacher research methods as samples and questionnaires or question research instruments. This aims to understand better the character development indicators of student persistence in learning.

Defining Complex Adaptive Systems: An Algorithmic Approach

Despite a profusion of literature on complex adaptive system (CAS) definitions, it is still challenging to definitely answer whether a given system is or is not a CAS. The challenge generally lies in deciding where the boundaries lie between a complex system (CS) and a CAS. In this work, we propose a novel definition for CASs in the form of a concise, robust, and scientific algorithmic framework. The definition allows a two-stage evaluation of a system to first determine whether it meets complexity-related attributes before exploring a series of attributes related to adaptivity, including autonomy, memory, self-organisation, and emergence. We demonstrate the appropriateness of the definition by applying it to two case studies in the medical and supply chain domains. We envision that the proposed algorithmic approach can provide an efficient auditing tool to determine whether a system is a CAS, also providing insights for the relevant communities to optimise their processes and organisational structures.