Complex Adaptive Systems Framework Research Articles

Co-evolution, organizational capacity building, logistics capabilities and operational agility during health emergencies

PurposeGlobally, health care facilities often fail to respond quickly to health emergencies, resulting in significant deaths. Thus, the study examines the mediating effect of logistics capabilities in the relationship between co-evolution, organization capacity building and operational agility.Design/methodology/approachSmartPLS 4.0.8.3, variance-based structural equation models (VB-SEM) were used to test direct and indirect hypotheses across 45 health facilities. The respondents were 140 nurses and doctors. Disproportionate stratified simple random sampling was used based on government and private health facilities in Kampala City, Uganda.FindingsA significant and positive relationship exists between logistics capability and operational agility. However, co-evolution and organizational capacity are intimately linked to operational agility via logistics capability.Research limitations/implicationsA quantitative cross-sectional survey was conducted using self-administered questionnaires. The results did not show any variation in the operational agility of health facilities during emergency management; thus, qualitative approaches are needed.Practical implicationsDuring emergency management, health facilities need to cooperate by sharing information, building capacity to increase their responsiveness and flexibility. This should be done by sharing equipment, tools, drugs and medical supplies, and we hope to encourage joint medical research.Originality/valueA complex adaptive systems (CAS) framework is used in this study to examine how co-evolution, organizational capacity building and logistical capability relate to the operational agility of healthcare during times of emergency.

Societies with fission-fusion dynamics as complex adaptive systems: the importance of scale.

In this article, we argue that social systems with fission-fusion (FF) dynamics are best characterized within a complex adaptive systems (CAS) framework. We discuss how different endogenous and exogenous factors drive scale-dependent network properties across temporal, spatial and social domains. Importantly, this view treats the dynamics themselves as objects of study, rather than variously defined notions of static 'social groups' that have hitherto dominated thinking in behavioural ecology. CAS approaches allow us to interrogate FF dynamics in taxa that do not conform to more traditional conceptualizations of sociality and encourage us to pose new types of questions regarding the sources of stability and change in social systems, distinguishing regular variations from those that would lead to system-level reorganization. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Territorial control by non-state armed groups and gendered access to healthcare in conflict using a new complex adaptive systems framework

The growing prevalence and influence of non-state armed groups (NSAGs) in armed conflict requires a deeper understanding of how their territorial control and contestation affect sub-national healthcare availability and outcomes, particularly with regards to gender dynamics. However, there are gaps in existing research in understanding how NSAG governance influences health care outcomes. Furthermore, a disconnect exists between the literature on international humanitarian interventions, NSAG governance and national health systems. Each perspective tends to prioritise a specific health service provider—whether NSAGs, State actors, or international humanitarian organisations—and often treats populations as homogenous. To address these gaps, we propose a complex adaptive system framework centred on the influence of territorial control for health services in intra-state conflict settings, with a gender lens. This systems framework accounts for the influence of all parties significant for health service delivery. It recognises that those governing the localised health system may differ from those physically delivering health services. Our framework enables empirical examination of health service delivery and outcomes. We apply the framework to three conflict-affected settings (Colombia, Iraq, Mali) using publicly available data and offer recommendations for policymakers and practitioners targeting health service provision in intra-state conflict contexts.

Long-term resilience in wastewater management: Optimizing treated wastewater allocation with a dynamic multi-agent approach

Water scarcity poses a significant challenge to sustainable development, necessitating innovative approaches to manage limited resources efficiently. Effective water resource management involves not just the conservation and distribution of freshwater supplies but also the strategic reuse of treated wastewater (TWW). This study proposes a novel approach for the optimal allocation of treated wastewater among three key sectors (user agents): agriculture, industry, and urban green space. Recognizing the intricate interplays among these sectors, System Dynamics (SD) and Agent-Based Modeling (ABM) were integrated in a Complex Adaptive System (CAS) to capture the interactions and feedback mechanisms inherent within treated wastewater allocation systems. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) serves as the optimization tool, enabling the identification of optimal allocation strategies across various management scenarios over a 25-year simulation period. Our research navigates the complexities of long-term resource management, accounting for each sector's evolving its objectives and guidelines along the whole system objectives and strategies. The outcomes demonstrate how treated wastewater can be effectively distributed to support economic and social equity –as the system objectives-while supporting agricultural and industrial growth and enhancing efficiency and social well-being –reflecting individual agent objectives-within the CAS framework. The research explores four distinct management scenarios, each prioritizing different sectors to address water resource management challenges. Notably, all four scenarios align with the strategies required by the ruler (government), providing strategic guidance to water resource managers for decision-making. The simulation results reveal a scenario where all sectors' demands are met, with Scenario 4 emerging as the most effective. Scenario 4 aligned with the objectives and guidelines of each sector, demonstrating significant improvements in the CY (Agriculture agent index; increased from 0.2 to 0.68), IGI (Industry agent index; increased from 1 to 1.63), and GAI (Urban Green Space agent index; increased from 1 to 1.23) indices over the 25-year simulation period. By providing a strategic blueprint for policymakers and stakeholders, this study contributes significantly to the discourse on sustainable water resource management, presenting a replicable model for similar contexts globally, where judicious allocation of treated wastewater is paramount for achieving harmony between human activity and ecological preservation.

Using complex adaptive systems (CAS) framework to assess success factors that lead to successful organizational change: a new way to understand change implementation for success

Using complex adaptive systems (CAS) framework to assess success factors that lead to successful organizational change: a new way to understand change implementation for success

Dynamics in digital finance and its impact on SME financing

Employing the Complex Adaptive System (CAS) framework, we analyzed the evolution of digitally transformed financial networks, underscoring the pivotal role of adaptation and learning among network participants. Government funds supporting banks and platforms in digital transformation prove more effective than direct subsidies to SMEs. Achieving sustainable development requires an optimal balance between investment and efficiency due to the impact of digitalization costs on constructing digital financial networks. The study advocates for a long-term microcredit financing mode tailored to SMEs, aiding them in maintaining financial stability. Additionally, intermediary nodes within the network, such as credit rating companies, can facilitate information sharing, thereby fostering the growth of digital financial networks. This heightened information sharing enhances the payoff for each subject and reduces financing constraints. Furthermore, the study establishes that digital financial networks can alleviate the repercussions of unforeseen events, such as the COVID-19 pandemic. Finally, empirical analysis of the Beijing University Inclusive Finance Index data and financial records of Chinese SMEs listed on the New Third Board from 2016 to 2018 confirms that digital finance can alleviate the financing constraints of SMEs. In summary, the progression of digital financial networks is intricate, necessitating a thorough comprehension of diverse factors. The government is encouraged to guide the enhancement of relevant systems and regulations, bolstering supervision, and facilitating the high-quality development of digital finance networks.

Challenges and sustainability of Indian mental health: evaluating volunteer-based community mental health clinics with a complex adaptive system framework

PurposeThis study aims to evaluate how a community psychiatry model, referred to as the Mental Health Action Trust (MHAT) in India, provides decentralized care and comprehensive services to people with severe mental illness living in poverty. Using the complex adaptive system (CAS) framework, the authors aim to understand the factors that contribute to the diverse outcomes of the MHAT community mental health programme as observed in four different locations.Design/methodology/approachFour MHAT clinics were purposively chosen from two districts in Kerala. A comparative case study methodology was used to document each clinic’s MHAT services and activities, as found during field visits and interviews with staff members and volunteers.FindingsThe study shows that all four clinics met the basic aim of providing free, quality mental health care to the poorest populations, although not all aspects of the comprehensive model could be equally provided. Alignment with the MHAT vision, appropriate leadership, the relationship with partners and their level of community engagement determined the varied success between clinics.Originality/valueThe current study evaluation stresses that community ownership is crucial. Careful attention must be paid to the characteristics of selected partners, including their leadership styles and ability to garner resources.

What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care.

Primary care physicians are likely both excited and apprehensive at the prospects for artificial intelligence (AI) and machine learning (ML). Complexity science may provide insight into which AI/ML applications will most likely affect primary care in the future. AI/ML has successfully diagnosed some diseases from digital images, helped with administrative tasks such as writing notes in the electronic record by converting voice to text, and organized information from multiple sources within a health care system. AI/ML has less successfully recommended treatments for patients with complicated single diseases such as cancer; or improved diagnosing, patient shared decision making, and treating patients with multiple comorbidities and social determinant challenges. AI/ML has magnified disparities in health equity, and almost nothing is known of the effect of AI/ML on primary care physician-patient relationships. An intervention in Victoria, Australia showed promise where an AI/ML tool was used only as an adjunct to complex medical decision making. Putting these findings in a complex adaptive system framework, AI/ML tools will likely work when its tasks are limited in scope, have clean data that are mostly linear and deterministic, and fit well into existing workflows. AI/ML has rarely improved comprehensive care, especially in primary care settings, where data have a significant number of errors and inconsistencies. Primary care should be intimately involved in AI/ML development, and its tools carefully tested before implementation; and unlike electronic health records, not just assumed that AI/ML tools will improve primary care work life, quality, safety, and person-centered clinical decision making.

Complex adaptive systems-based framework for modeling the health impacts of climate change

IntroductionClimate change is a global phenomenon with far-reaching consequences, and its impact on human health is a growing concern. The intricate interplay of various factors makes it challenging to accurately predict and understand the implications of climate change on human well-being. Conventional methodologies have limitations in comprehensively addressing the complexity and nonlinearity inherent in the relationships between climate change and health outcomes. ObjectivesThe primary objective of this paper is to develop a robust theoretical framework that can effectively analyze and interpret the intricate web of variables influencing the human health impacts of climate change. By doing so, we aim to overcome the limitations of conventional approaches and provide a more nuanced understanding of the complex relationships involved. Furthermore, we seek to explore practical applications of this theoretical framework to enhance our ability to predict, mitigate, and adapt to the diverse health challenges posed by a changing climate. MethodsAddressing the challenges outlined in the objectives, this study introduces the Complex Adaptive Systems (CAS) framework, acknowledging its significance in capturing the nuanced dynamics of health effects linked to climate change. The research utilizes a blend of field observations, expert interviews, key informant interviews, and an extensive literature review to shape the development of the CAS framework. Results and discussionThe proposed CAS framework categorizes findings into six key sub-systems: ecological services, extreme weather, infectious diseases, food security, disaster risk management, and clinical public health. The study employs agent-based modeling, using causal loop diagrams (CLDs) tailored for each CAS sub-system. A set of identified variables is incorporated into predictive modeling to enhance the understanding of health outcomes within the CAS framework. Through a combination of theoretical development and practical application, this paper aspires to contribute valuable insights to the interdisciplinary field of climate change and health. Integrating agent-based modeling and CLDs enhances the predictive capabilities required for effective health outcome analysis in the context of climate change. ConclusionThis paper serves as a valuable resource for policymakers, researchers, and public health professionals by employing a CAS framework to understand and assess the complex network of health impacts associated with climate change. It offers insights into effective strategies for safeguarding human health amidst current and future climate challenges.

Protected area tourism and management as a social-ecological complex adaptive system

This article presents a mini review of systems and resilience approaches to tourism analysis and to protected area management, and of how the Social-Ecological Complex Adaptive Systems (SECAS) framework can help link them together. SECAS is a unique framework that integrates social theories (structuration) and ecological theories (hierarchical patch dynamics) and examines inputs, outputs, and feedback across a variety of hierarchically nested social and ecological systems. After an introduction to the need for continued theoretical development, this article continues with a review of the origins and previous applications of the SECAS framework. I subsequently highlight how complex adaptive systems and resilience have been presented in the literature as a way to separately study (1) protected area management, (2) protected area tourism/ecotourism, and (3) land-use change in adjacent forest and agricultural landscapes. The purpose of this article is to build on the frameworks described in this literature and link them through the SECAS framework. I populate the SECAS framework with components identified in the literature on protected area management, ecotourism, and land-use change to present an example of a full systems perspective. Each component also represents a hierarchically nested system, such as a governance system, health system, or transportation system. I conclude with a three-step (5-part) multi-scale and temporal method for SECAS research derived from hierarchy and structuration theories.

Simplifying complexity science principles: developing healthcare faculty for using simulation as an educational method

Professional development in simulation-based education (SBE) is a complex process. Individual components have overlapping boundaries and relationships, rendering them suitable for Complex Adaptive Systems (CAS). This complexity is multifold in low-resource settings, unfamiliar with simulation, and augmented by hierarchical culture and linguistic diversity. This study aimed to conduct a professional development course through SBE using principles of CAS for faculty in a Pakistani medical college. A simulation educator team (six on-site, three online) from Pakistan and North America constructed and facilitated a 6-hour-long hybrid course following CAS in three phases. Planning phase consisted of needs assessment and inclusion of stakeholders in decision-making. Implementation phase involved remote facilitators joining the in-person team using Zoom and demonstrating evidence-based practices in case design, pre-briefing, facilitation and debriefing. In the evaluation phase, participants completed an immediate Twenty-three faculty members from basic medical ( We suggest using CAS as a framework for professional development programs to train faculty in simulation. A CAS framework can help the organizers to integrate systems thinking into educational interventions.

The complexity of entrepreneurial ecosystem evolution and new venture policy: The case of the U.S. commercial space ecosystem

Recent scrutiny on policy interventions to promote start-ups has increased attention toward stimulating more innovative, high-growth ventures. Responding to the shortcomings in such interventions, scholars call for more attention to contexts in which new ventures emerge and how policies can shape the environment to be more conducive to venturing. The concept of an entrepreneurial ecosystem is a valuable framework for understanding such a context as it highlights the evolutionary and interdependent complexity of such a system. How this complexity affects the effectiveness of policy interventions, however, remains unclear. Applying a complex adaptive systems framework, we study the U.S. commercial space ecosystem to examine its evolution and the role policy interventions play. Our analysis suggests that by understanding an emerging ecosystem as a complex adaptive system, policy makers can be more effective by appropriately selecting, targeting, and timing interventions to match the unique nature of individual, targeted components of the system.

Managing organizational transformation (OT) using complex adaptive system (CAS) framework: future lines of inquiry

PurposeA slew of conventional change models and theories appear in the extant change literature. Despite being theoretically sound, these a priori structured approaches to organizational change management have questionable application given the rapidly changing business environments. Novel approaches, offering greater flexibility to fast changing external conditions, may offer superior models to organizational change and organizational transformation (OT) in particular. In this paper, the application of a complex adaptive system (CAS) framework, from complexity theory (CT), for managing OT is assessed theoretically.Design/methodology/approachA conceptual paper.FindingsA review of the extant change literature suggests that current approaches and models for organizational change are limited in their ability to reflect OT responses to today's highly dynamic external environments. New models are required to inform and guide organizations. A new model, i.e. CAS framework, is deemed suitable to guide the OT implementation.Originality/valueThis paper critically analyses different approaches to change management, consolidates CAS framework, reviews its applications in the field of management and presents a case for CAS's application for the management of OT.

Agent-based modelling for the study of shipwreck site formation processes: A theoretical framework and conceptual model.

Background Shipwreck site formation processes (SFP), their dynamics and transformation have long interested maritime archaeology from both a historical perspective and heritage management since shipwrecks constitute endangered cultural sites. Major contributions to shipwreck SFP have been made since the 1970s which conceive SFP from a systemic theoretical perspective, notably those of Keith Muckelroy and Martin Gibbs. However, to our view, such a perspective falls short in its capacity to explain and predict the distribution of elements in a shipwreck. In this article, we propose that shipwreck SFP can be understood from the theoretical framework of complex adaptive systems (CAS), where a SFP constitutes a CAS in which nonlinear interactions of natural and cultural factors give rise to the observed seabed distribution of a shipwreck as an emergent phenomenon. Methods From this theoretical framework, we propose agent-based modelling (ABM) as a suitable methodological approach for studying SFP. We show its implementation using the USS Somers, a 19th-century brig of war that sank in 1846 off the port of Veracruz, Mexico, during the Mexican-American War as a case study. The conceptual model was developed from the integration of historical data about the ship’s nautical characteristics and operation, information on the wrecking event from eyewitnesses, as well as modern environmental data. Results We present a conceptual model defining various elements that would constitute the Somers’ ABM. It gives specifics about the characteristics and variables regarding agents, global variables, processes, indicators, degradation and deposition sequences, and user interface. Conclusion The conceptual model served to develop ABM in a simulation platform where historical hypotheses can be tested and various possible scenarios of the SFP can be explored. By contrasting the simulation results with the archaeological record of the shipwreck, ABM would allow maritime archaeologists to postulate more supported and refined interpretations of a shipwreck’s SFP.

Scaling up breastfeeding in England through the Becoming Breastfeeding Friendly initiative (BBF).

Breastfeeding is the most accessible and cost-effective activity available to public health and has been shown to be one of the most effective preventive measures mothers can take to protect their children's health. Despite the well-documented benefits, the UK has one of the lowest breastfeeding rates in the world. The Becoming Breastfeeding Friendly (BBF) toolkit was developed through highly structured technical and academic collaboration, led by Yale University. It provides an evidence-based process to help countries assess their breastfeeding status and readiness to scale up, and identifies concrete measures countries can take to sustainably increase breastfeeding rates, based on data-driven recommendations. BBF is grounded in the Breastfeeding Gear Model complex adaptive systems framework which is made up of eight simultaneous conditions that sustain breastfeeding. In 2018, a committee of multi-agency stakeholders implemented the BBF process in England, collecting evidence to score the 'gear' components of England's breastfeeding environment against 54 benchmarks. The Training and Programme Delivery gear received the highest score, attributable to existing learning outcomes for health professionals and practitioners, peer supporters and specialist services, although there is a need for greater coordination and integration. The lowest scores were given for Promotion and Coordination, Goals and Monitoring due to the lack of a dedicated national strategy for breastfeeding and poor sharing of localised strategies and programmes. The process generated clear recommendations highlighting the need for more robust routine infant feeding data collection and reporting, and the necessity for strengthening leadership, monitoring and oversight to scale up and sustain breastfeeding.

Biological networks and complexity in early-onset motor neuron diseases.

Motor neuron diseases (MNDs) are neuromuscular disorders where the spinal motor neurons-either the cell bodies themselves or their axons-are the primary cells affected. To date, there are 120 different genes that are lost or mutated in pediatric-onset MNDs. Most of these childhood-onset disorders, aside from spinal muscular atrophy (SMA), lack viable therapeutic options. Previous research on MNDs has focused on understanding the pathobiology of a single, specific gene mutation and targeting therapies to that pathobiology. This reductionist approach has yielded therapeutic options for a specific disorder, in this case SMA. Unfortunately, therapies specific for SMA have not been effective against other pediatric-onset MNDs. Pursuing the same approach for the other defined MNDs would require development of at least 120 independent treatments raising feasibility issues. We propose an alternative to this this type of reductionist approach by conceptualizing MNDs in a complex adaptive systems framework that will allow identification of common molecular and cellular pathways which form biological networks that are adversely affected in early-onset MNDs and thus MNDs with similar phenotypes despite diverse genotypes. This systems biology approach highlights the complexity and self-organization of the motor system as well as the ways in which it can be affected by these genetic disorders. Using this integrated approach to understand early-onset MNDs, we would be better poised to expand the therapeutic repertoire for multiple MNDs.

Brexit and the United Kingdom-European Union relationship from the perspective of a complex adaptive system

The UK-EU relationship has been depicted as awkward, and Brexit represents the pinnacle of this strangeness. This paper uses the Complex Adaptive Systems framework and the Containers, Differences, Exchanges (CDE) model to understand Brexit as a turning point in the system-wide pattern of the UK-EU relationship from differentiated integration to disintegration. Also, in accordance with the post-functionalist theory, this article argues that the ‘constraining dissensus’ that emerged since the 1990s has been pushing both towards more differentiated integration and, ultimately, towards disintegration.

Design for financial sustainability

SummaryThe 1987 United Nations Brundtland Report established the vision of sustainable development as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs.” How might we anticipate the requirements of future custodians of vast, continuously morphing socio-technical-ecological systems while addressing current pressing needs? An abstract, principled approach (such as axiomatic design) might help address such ambiguity. Such systems are composed of large numbers of information-processing agents/agencies that collectively form a complex adaptive system (CAS). The focus here is on financial sustainability: (1) what is a principled approach toward sustainable design? (2) What design insights might we obtain by studying financial crises forensically against sustainability successes in nature? (3) How to design for financial sustainability? This paper adopts the CAS framework alongside axiomatic design to help elicit design patterns and anti-patterns of sustainability. Inspired by nature, a promising inside-out design pattern emerges.

An integrated model for optimal water resources allocation under “3 Redlines” water policy of the upper Hanjiang river basin

Study regionThe upper Hanjiang river basin, which is a crucial water source for the middle route of the South-to-North Water Diversion Project in China. Study focusThis paper attempts to present a new integrated model for optimal water resources allocation in a typical river basin. In this model, the complex adaptive system framework is applied at the macro level, and the system dynamics simulates the inner response of water user agent at the micro level and China’s “3 Redlines” water policy is considered in the optimal module. New hydrological insights for the regionAn integrated model is proposed for optimal water resources allocation of the upper Hanjiang river basin in the planning years 2025 and 2030, and the NSGA-II algorithm is executed to obtain the Pareto optimal solution set for the optimal allocation of water resources. The results show that there is a significant competitive relationship between environmental objective and economic objective. Water resources tend to flow to industrial water user agent driven by the adaptation to economic objective, to agricultural water user agent and municipal water user agent driven by the adaptation to environmental objective, which can be used for decision-making and provide guidance for further development of water resources allocation.

Complex adaptive governance systems: a framework to understand institutions, organizations, and people in socio-ecological systems.

Governance is the reason for and solution to complex problems in socio-ecological systems (SESs). Governance refers to the institutions, organizations, and people involved in and affected by socio-ecological practices (SEPs), such as research, planning, design, construction, restoration, conservation, and management. The complexity of SESs requires the ability to understand and identify how the social world produces differential opportunities, constraints, and resources across multiple levels and scales of governance systems and as a consequence undesirable SEP outcomes for social equity, human well-being, and environmental integrity. This paper presents a complex adaptive governance systems framework (CAGS-F) designed to provide guidance, organization, and basic conceptualizations of social scientific concepts and terms for diagnostic, descriptive, and prescriptive inquiry into SEPs for the purpose of improving justice and sustainability. CAGS-F is unique for synthesizing the panarchy heuristic’s focus on socio-ecological interdependence, cross-scalar, multi-causal, non-linear complexity, and change with compatible social scientific theories of multi-level institutions, organizations, and human practices. The framework works from a critical realist orientation to reveal how power and privilege embedded in institutions, organizations, and human practices produce inequitable and/or undesirable SEP outcomes. The structure of the framework employs analytic dualism to provide a way to identify where, at what level and scale, who is included and/or adversely affected, and at which point in discrete adaptive cycles across institutional, organizational, and human practices opportunities, barriers, and leverage points exist so as to optimize design, planning, programming, and implementation of SEPs or evaluate unintended and unforeseen, less than successful, inequitable, and/or undesirable outcomes.