Analysis Of Complex Adaptive Systems Research Articles

Resilience and active inference.

In this article, we aim to conceptualize and formalize the construct of resilience using the tools of active inference, a new physics-based modeling approach apt for the description and analysis of complex adaptive systems. We intend this as a first step toward a computational model of resilient systems. We begin by offering a conceptual analysis of resilience, to clarify its meaning, as established in the literature. We examine an orthogonal, threefold distinction between meanings of the word "resilience": (i) inertia, or the ability to resist change (ii) elasticity, or the ability to bounce back from a perturbation, and (iii) plasticity, or the ability to flexibly expand the repertoire of adaptive states. We then situate all three senses of resilience within active inference. We map resilience as inertia onto high precision beliefs, resilience as elasticity onto relaxation back to characteristic (i.e., attracting) states, and resilience as plasticity onto functional redundancy and structural degeneracy.

„Mindent eláraszt, ami az útjába kerül” – a 2004-es indiai-óceáni földrengés és szökőár katasztrófadiplomáciai elemzése

The 2004 Indian Ocean earthquake and tsunami was the result of a highly complex process that involved the release of enormous amounts of seismic energy. Although the magnitude of neither the earthquake nor the tsunami was unprecedented, due to the size of the affected area, the height above sea level, the size and density of the local population, as well as the unpreparedness of the risk management, the disaster can be identified as the most destructive catastrophe of humanity. The present research examines the events of the 2004 tsunami in the context of Southeast Asian disaster diplomacy. The methodological framework of the study is the complex adaptive system (CAS) method, which is applied in the analysis of complex adaptive systems through four properties and three mechanisms. Among the variables of the CAS method, the property of diversity as well as the mechanism of building blocks determined regional cooperation. The natural disaster of 2004 demonstrates well that the member states of the Association of Southeast Nations are capable of disaster cooperation despite the unsuccessful protection measures.

The ‘why’ and ‘how’ of flexible learning spaces: A complex adaptive systems analysis

This article presents the perceptions and experiences of 12 school principals, 35 teachers and 85 students on the influences and processes used by eight Australian government primary and secondary schools to transform traditionally arranged classrooms into flexible learning spaces. Characterised by a variety of furniture and layout options, these spaces are designed to enable a range of learning styles and activities and facilitate student-centred pedagogy. These changes to school learning environments are discussed in light of some central constructs of complexity theory, including inertial momentum, emergence, agent interaction, information flow, feedback loops and lock-in. The findings highlight the role of consultation, participation and ownership as central elements of sustainable change processes. Further effective design and transformation of learning environments requires a reflexive school community, pedagogical shift, professional development, and ongoing support to teachers and students. The discussion emphasizes the sociomaterial interplay between the pedagogical and physical classroom environment.

Failure to Rescue Event Mitigation System Assessment: A Mixed-methods Approach to Analysis of Complex Adaptive Systems.

Although it is widely acknowledged that health care delivery systems are complex adaptive systems, there are gaps in understanding the application of systems engineering approaches to systems analysis and redesign in the health care domain. Commonly employed methods, such as statistical analysis of risk factors and outcomes, are simply not adequate to robustly characterize all system requirements and facilitate reliable design of complex care delivery systems. This is especially apparent in institutional-level systems, such as patient safety programs that must mitigate the risk of infections and other complications that can occur in virtually any setting providing direct and indirect patient care. The case example presented here illustrates the application of various system engineering methods to identify requirements and intervention candidates for a critical patient safety problem known as failure to rescue. Detailed descriptions of the analysis methods and their application are presented along with specific analysis artifacts related to the failure to rescue case study. Given the prevalence of complex systems in health care, this practical and effective approach provides an important example of how systems engineering methods can effectively address the shortcomings in current health care analysis and design, where complex systems are increasingly prevalent.

P112: FLO on flow: front line ownership of emergency department, hospital, and health system patient flow a novel approach to ED overcrowding (Part 1)

Introduction: Hospital access block, often called Emergency Department (ED) overcrowding when it manifests there, is an important public health issue and seemingly intractable problem in our evolving Health Care system. The multiple, dynamic, and inter-dependent factors influencing its cause (and potential solutions) may best fit a complex adaptive systems analysis and approach. One technique described in similar contexts is Front Line Ownership (FLO) based on the theoretical framework of positive deviance. The aim of this study is to discover where pragmatic bottom-up insights and adaptive work-arounds can be elicited, described, iterated, and potentially implemented at a broader scale to catalyze systems change, in service of improving patient flow. Methods: This is a qualitative study which identified, convened, and surveyed stakeholders representing three components of the system. Purposive sampling was used to gather a full range of perspectives from three groups: 1) patients and or families, 2) front-line providers, and 3) management/leaders. Interviews were recorded and transcribed by a third party, then each transcription was coded independently by two investigators (at least one of which was the PI). Informed consent was obtained from all participants and each was offered the opportunity to review the transcription to ensure accuracy. A framework analysis was used to synthesize, reflect upon, and interpret the data from multiple perspectives using a structured, iterative approach. Results: In part 1 of this study, three broad over-lapping themes emerged from the analysis as being areas of opportunity for reducing hospital access block. They are: 1. Boundary Conditions (the historical, organizational cultural, psychological, economic, and other contexts influencing system performance), 2. Systems Integration (how well the parts interface with each other relate to the whole), and 3. Operations management (the more technical aspects of patient flow). When these three broad themes are cross-analyzed with a more conventional input-throughput-output approach, previously under-emphasized avenues for improvement may become apparent. Conclusion: A front-line ownership analysis of ED overcrowding is feasible. There are adaptive behaviors by some front-line individuals at each “level” of perspective that have been identified and could be modified and implemented locally to improve patient flow in the ED (and the rest of the health system).

The Adoption of Computers by Australian General Practice – A Complex Adaptive Systems Analysis

The world is slowly moving to an E-Health environment. Computers are becoming an essential part of how health is delivered, just as they are a part of all other aspects of life. Yet the adoption of computers is patchy. In some jurisdictions it is the hospital sector that is highly computerized, and in others it is primary care. This paper examines the adoption of computers in general practice in Australia, and provides a theoretical explanation for the reasons general practices adopted computers, while hospitals did not. The application of Complex Adaptive Systems not only explains the computerization, but provides lessons for others in promoting computers in the health sector.

Knowledge Exchange and integrative research approach

This chapter discusses the process of Knowledge Exchange and the challenges associated with Knowledge Transfer both within a multi-disciplinary academic research team and across the research team to the customer stakeholders. Specifically addressing socio-technical complex adaptive systems, Knowledge Exchange provides a vital cornerstone to both successful integration of research findings and communication to the customer in a continuous process. This takes place on three primary levels: 1 among the members of the multidisciplinary research team, 2 between researchers and identified user counterparts with the customer, and 3 across the research team and the user customer stakeholders. Challenges include creating and sustaining active collaboration at all three of these levels, beginning with engagement within the research team itself. Modeling and analysis of complex adaptive systems, especially those with a high degree of socio-technical complexity, requires various, often highly separate, disciplines within an academic team. These may include change management, operations management, behavioral science, marketing, policy, computer science, systems engineering, and complexity analysis, as well as involving researchers across multiple academic institutions. Active Knowledge Exchange serves both to facilitate researcher-to-researcher interactions and to integrate the research findings into a synthesized whole. It is this whole that must then represent the socio-technical complexity of the system in a manner that illustrates the findings in a clear, tangible, and especially actionable manner to the customer stakeholders. Specifically, this chapter discusses our starting challenges and expectations, the process and continuing, new challenges we discovered, gaps that remained between realities of these challenges and realization of our vision, and offers suggestions for future research.

A concept in adaptive ecosystem management—An engineering perspective

Uncertainty how ecosystems will behave and look like in the future has been increasing. Several concepts, such as sustainable, adaptive, or risk-based management, were proposed to cope with uncertain, incomplete knowledge, and with changing environmental conditions. However, there is a lack of agreement what those concepts mean and how they differ from traditional forest management approaches. The paper aims at (1) exploring general systems theory, control theory, and risk management theory for general principles that may be guiding the future development of ecosystem management, and that (2) developing a concept in adaptive ecosystem management that may act as a shared mental model for both scholars and practitioners. The exploration of systems- and risk-based theories results in decisive foundations of adaptive management: (1) emergence, adaptation, and resilience are fundamental concepts to characterize the dynamic behavior of ecosystems, accounting for their ability for purposeful transformational change. (2) Space–time scale matters. The space–time window of traditional, silviculture-based forestry has been focusing on the patch and stand level, whereas adaptive management has to consider much larger time-space windows, covering a range from the needle to the landscape. (3) Risk-informed management starts with the identification of critical effects, then quantifies the dose–response relationship between exogenous strain and endogenous stress. The paper proposes a concept in adaptive ecosystem management, being founded on three general concepts. First, control theory is the backbone to keep system dynamics within an acceptable range even if external disturbances are acting, the underlying model is based on uncertainties, and if the real process can only be modeled incompletely. Second, adaptive ecosystem management has to focus on processes across different space–time scales instead of looking at the static, “frozen” structures like trees of stands. Third, interaction of processes across space–time scales, characterized by dose–response functions, are the driving forces to shape the long-term development of the systems. The proposed concept challenges different domains. Research has to improve its understanding of process interactions across space–time scales, characterized by dose–response models, which are essential to understand system robustness (resilience). Higher education related to ecosystem management should refocus on scientific competences to the analysis of complex adaptive systems, and the use of control theory to continuously improve policies and practices for ecosystem management. Practitioners on the ground have to become convinced that the goal of ecosystem management is a “moving target”, consisting of a portfolio of ecosystem goods and services. They have to internalize the principle of “rule discovery” as a systematic learning process to improve management practices and by looking for “biodiversity in our minds”. The paper is expected to trigger discussions among scholars and practitioners, leading to a set of principles for resource use that consider both complexity and dynamics of ecosystems.

Complex Adaptive Systems: Use and Analysis of Complex Adaptive Systems in Ecosystem Science: Overview of Special Section

Ecologicalsystemsarecomplexassemblagesofinter-acting organisms embedded in an abiotic environ-ment.Complexityarisesfrominterspecificandintra-specific interactions among individuals or agents,interactions across trophic levels, and the interac-tions of organisms with the abiotic environmentover space and time. In addition, interactions canrange from strong and direct to weak and diffuseand are modified by both positive and negativefeedback with the environment. In our effort tounderstand pattern formation at the community orecosystem level, we are confronted with the daunt-ing array of processes that function across differentspatial and temporal scales. We are thus forced toaddress the question of how these different levels oforganization can be integrated, or how mechanismsand patterns at one level of organization can beunderstood in terms of processes operating at adifferent level of organization.The goal of this