Abstract SY36-03: Viewing cancer as a complex adaptive system and managing immunotherapy as “homeostatic reset”

ObjetivoO ambiente de negócios global gera diferentes problemas que ameaçam a sobrevivência da organização. Como solução relevante, surge o conceito de resiliência organizacional que oferece uma filosofia holística. O conceito de resiliência oferece uma literatura multidisciplinar eclética e é valioso para estudos organizacionais que ajudam a produzir uma grande variedade de soluções, mas há falta de consenso para medir e aplicar resiliência a nível organizacional. Para colmatar esta lacuna, este trabalho oferece a Abordagem Complex Adaptive Systems (CAS) como uma lente para organizações. O objetivo deste estudo é demonstrar que os Sistemas Adaptativos Complexos (CAS) fornecem um conjunto adequado de ferramentas para abordar o conceito de resiliência organizacional, uma vez que tem o potencial de oferecer orientações mais generalizadas.Design/metodologia/abordagemPara atingir este objetivo, esta investigação segue duas fases de revisão sistemática da literatura. Na primeira fase, o objetivo foi procurar em cinco anos (2015–2020) investigar as tendências atuais nos conceitos de resiliência organizacional. Na segunda fase, verifica-se estudos de resiliência organizacional que incluem a abordagem CAS para analisar os procedimentos de alinhamento de dois conceitos.ConclusõesA literatura mostra que o conceito de resiliência organizacional não está ligado a Sistemas Adaptativos Complexos (CAS). Os sistemas adaptativos complexos são mais resistentes através da adaptação e da aprendizagem, pois dependem de interações locais que moldam e co-evoluem juntamente com o seu ambiente dinâmico que ajuda a emergir como auto-organização num futuro imprevisível. Para alcançar a resiliência organizacional, a lente CAS propõe um quadro generalizável aplicável aos estudos organizacionais.OriginalidadeA originalidade do estudo consiste em propor a obtenção de resiliência organizacional através de Sistemas Adaptativos Complexos (CAS) e oferece um quadro conceptual para alcançar a resiliência organizacional.Palavras-chaveResiliência organizacional, Abordagem de sistemas adaptativos complexos (CAS), Revisão sistemática de literatura, Modelo conceptualTipo de manuscritoPapel conceitual

Complex adaptive systems are a special kind of system with emergent properties and adaptive capacity in response to external environmental conditions. In this chapter, I investigate the proposition that international environmental law, as a set of multilateral environmental agreements, exhibits the characteristics of a complex adaptive system. This proposition is premised on the scientific understanding that the subject matter displays properties of a complex adaptive system. If so, the legal system may benefit from the insights gained and from being modeled in ways more appropriately aligned with the functioning of the Earth system itself. I provide as context a scientific explanation of the Earth system as a complex adaptive system. I then consider if international environmental law can be understood as a system, which is complex and adaptive. From this exploratory review, I found evidence suggesting that international environmental law is a system with interactive elements. I also found indications of self-organization and emergence, suggesting that international environmental law is a complex system. However, it is still questionable whether the legal system has been autonomously adaptive to and co-evolving with global environmental and geopolitical change in ways that lead to net environmental improvement.

Concepts from complexity science are familiar experiences for those working in primary health care. We work with people, each one different from every other. We have the privilege of knowing our patients over long periods of time, and this helps us understand them better. We are not surprised by how differently patients respond to a particular treatment. We witness the influence of family and community on our patient’s experience of health and illness and the opportunities and constraints of health care provision within our organizational and policy context.1 As clinicians, we may work within organizations comprised of many individuals and experience the effect of the quality of communication on the organization.2 When we visit a different primary care practice, even though they may have similar objectives and resources and work in a similar way to our own, the difference in the character of the practice is often striking.3 Complexity sciences seek to understand complex systems. People and primary care organizations are examples of complex systems. They have emergent properties that are not explainable using linear models of interaction or causality. Seemingly similar complex systems such as people or organizations become diverse as small differences become amplified through interaction and feedback. The history of a complex system influences its current properties and these constantly evolve. The system is engaged within its context, changing it and being changed.4 Despite the apparent fit between complexity sciences and primary health care, what complexity sciences have to offer primary care research is still an open question. As a novel approach to research, complexity science challenges us to think clearly about the nature of reality and how we come to understand it, questions of ontology and epistemology, and challenges our understanding of causation and how we detect it. Where we are stuck on a particular problem, complexity sciences may offer an innovative way of thinking about it without necessarily needing new research methods. Studying interaction and its dynamics, and studying emergence may be of particular importance for primary care research and require learning or developing new research methods. Arguably the most robust current research in complexity sciences looks inside complex inanimate or cellular systems. Examples include energy networks, computer networks, moving fluids, and cellular enzyme systems. Large volume longitudinal data is collected and analyzed using data mining techniques. Computer simulation of the system can be compared with real life. Mathematics succinctly describes the structure and dynamics of the system. These research approaches require data that capture interaction. We have data about information exchange within our primary care organizations that can be analyzed in terms of network structure and dynamics. Similarly, patient interaction with health care may be explored through case by case longitudinal analysis of our patient data. However, our patients interact with their social and environmental context, and this influences their health.5 This dynamic interaction is poorly documented within available health care data. Linkage of large data sets from social surveys, census, and health care may provide future opportunities for analysis of this dynamic interaction; however, smaller scale mixed-method longitudinal research is likely to be more productive in the short term. Although medical science can claim many successes, there are health problems, for example low back pain and depression, where it can be argued traditional research approaches seem to be stuck. A complexity sciences approach may consider such health problems emergent phenomenon arising from the interaction of many different factors, biological, psychological, technological, social, and environmental. Emergence cannot be tracked back to a particular cause. Similarly, interactions between patients and physicians have emergent properties that are not determined by the patient or the doctor, but develop through their interchange. The function of a primary care practice emerges from the interaction of those who work there, the patients and context. Understanding emergence is a challenge for complexity science, not just for primary care, and is receiving attention from many research disciplines. NAPCRG will continue to serve as a forum for complexity science researchers to learn from one another and to create new, practical insights that will improve the design and delivery of primary health care.

Despite increased prominence and funding of global health initiatives, efforts to scale up health services in developing countries are falling short of the expectations of the Millennium Development Goals. Arguing that the dominant assumptions for scaling up are inadequate, we propose that interpreting change in health systems through the lens of complex adaptive systems (CAS) provides better models of pathways for scaling up. Based on an understanding of CAS behaviours, we describe how phenomena such as path dependence, feedback loops, scale-free networks, emergent behaviour and phase transitions can uncover relevant lessons for the design and implementation of health policy and programmes in the context of scaling up health services. The implications include paying more attention to local context, incentives and institutions, as well as anticipating certain types of unintended consequences that can undermine scaling up efforts, and developing and implementing programmes that engage key actors through transparent use of data for ongoing problem-solving and adaptation. We propose that future efforts to scale up should adapt and apply the models and methodologies which have been used in other fields that study CAS, yet are underused in public health. This can help policy makers, planners, implementers and researchers to explore different and innovative approaches for reaching populations in need with effective, equitable and efficient health services. The old assumptions have led to disappointed expectations about how to scale up health services, and offer little insight on how to scale up effective interventions in the future. The alternative perspectives offered by CAS may better reflect the complex and changing nature of health systems, and create new opportunities for understanding and scaling up health services.

This conceptual paper offers a novel conceptual analysis of emerging work paradigms that have the potential to reshape the tourism professions, underscoring the need for a comprehensive perspective. It emphasises the necessity of examining the increasingly complex tourism professions through the lens of Complex Adaptive Systems (CAS). Additionally, we examine how a CAS analysis of tourism work paradigms can be effectively conducted. First, five fundamental tenets that guide a CAS exploration of work dynamics were identified. Subsequently, the conceptual foundation of the CAS approach was established through the development of a framework synthesised from existing literature on work paradigms and tourism work. This framework elucidates the mechanisms and implications of evolving tourism work dynamics, with the primary objective of addressing their inherent complexities while offering a precise structure for CAS-based simulation modelling. Moreover, we provide guidance on constructing a simulation model based on this framework to address 12 identified gaps in the current tourism work literature. The theoretical contributions, methodological advancements, and potential research directions arising from the CAS approach benefit in predicting and advancing the future of tourism professions. Finally, practical implications for organisational strategy development and policy-making toward fostering a sustainable tourism work ecosystem are discussed.

We offer a complex adaptive systems (CAS) lens as a potentially fruitful cross-disciplinary perspective for mentoring. Understanding mentoring relationships as CAS will invigorate the field and assist it to be responsive and relevant given our increasingly complex and turbulent environment. We begin by exploring the key properties of CAS. We then review the current mentoring literature’s connection to the systems and complexity theories that underpin CAS, demonstrating CAS’s relevance. We further examine four specific benefits that such a lens could bring, including attention to process, the reconceptualization of context, the adoption of new methodologies, and the fostering of interdisciplinary conversation. Research questions the CAS lens might stimulate for mentoring are then considered and difficulties with the CAS approach explored. We conclude by suggesting how CAS-informed research might shape mentoring knowledge and practice.

Many researchers throughout the world have been struggling to better understand and describe spatial data infrastructures (SDIs). Our knowledge of the real forces and mechanisms behind SDIs is still very limited. The reason for this difficulty might lie in the complex, dynamic and multifaceted nature of SDIs. To evaluate the functioning and effects of SDIs we must have a proper theory and understanding of their nature. This article describes a new approach to understanding SDIs by looking at them through the lens of complex adaptive systems (CASs). CASs are frequently described by the following features and behaviours: complexity, components, self-organization, openness, unpredictability, nonlinearity and adaptability, scale-independence, existence of feedback loop mechanism and sensitivity to initial conditions. In this article both CAS and SDI features are presented, examined and compared using three National SDI case studies from the Netherlands, Australia and Poland. These three National SDIs were carefully analysed to identify CAS features and behaviours. In addition, an Internet survey of SDI experts was carried out to gauge the degree to which they consider SDIs and CASs to be similar. This explorative study provides evidence that to a certain extent SDIs can be viewed as CASs because they have many features in common and behave in a similar way. Studying SDIs as CASs has significant implications for our understanding of SDIs. It will help us to identify and better understand the key factors and conditions for SDI functioning. Assuming that SDIs behave much like CASs, this also has implications for their assessment: assessment techniques typical for linear and predictable systems may not be valid for complex and adaptive systems. This implies that future studies on the development of an SDI assessment framework must consider the complex and adaptive nature of SDIs.

In complex systems theory, two meanings of a complex adaptive system (CAS) need to be distinguished. The first, CAS1, refers to a complex system that is adaptive as a system; the second, CAS2, refers to a complex system of agents which follow adaptive strategies. Examples of CAS1 include the brain, the immune system, and social insect colonies. Examples of CAS2 include multispecies ecosystems and the biosphere. This chapter uses multilevel selection theory to clarify the relationships between CAS1 and CAS2. The general rule is that for a complex system to qualify as CAS1, selection must occur at the level of the complex system (e.g., individual-level selection for brains and the immune system, colony-level selection for social insect colonies). Selection below the level of the system tends to undermine system-level functional organization. This general rule applies to human social systems as well as biological systems and has profound consequences for economics and public policy.

The debate on PBF is misdirected. As is too often the case in international aid financing, agencies try to prove the effectiveness of their contribution by isolating it as the main reason for success.1 In reaction, opponents will often use the same approach in an attempt to prove that another factor is actually the cause of an observed change. We argue that this endless and futile debate, often present among experts in health systems strengthening, will not contribute to improving public health in low-income countries. Rather than searching for the impossible proof of whether PBF works or not, we should instead try to learn useful lessons from experiences. We agree with Ireland et al. that the focus of PBF assessment should be on “why” and “how” the intervention works.2 Comprehensive evaluation of PBF is needed as part of complete health system reform. We think that, to respond to some of these key questions, health systems should be analysed using a complex adaptive systems lens, as others have advocated in the past.3,4 A complex adaptive system is a collection of interacting components, each of which has its own rules and responsibilities. The behaviour of this kind of system is different to the sum of the behaviour of each of its components. Examples of complex adaptive systems include the human brain, ecosystems and manufacturing businesses. Health system “behaviour” and particularly counterintuitive behaviour (unexpected changes or lack of change) can be analysed using a complex adaptive systems lens when PBF is introduced, often with a mix of other interventions such as in a context of system reform. The purpose of this analysis is not to isolate causal factors but rather to identify “macro” characteristics of the system that may explain behaviour change. Although it has often been ignored in health system evaluation, social simulation can be useful for this approach. The most frequently used technique, agent-based modelling, uses computer simulation centred on a collection of autonomous agents whose interactions are based on a set of rules. These simulations can integrate empirical data or existing knowledge or opinions.5 One of the powerful features of agent-based modelling lies in its capacity to study complex phenomena in a simple and flexible way. Indeed, this approach does not require a high level of mathematical or programming skills, making it accessible to many researchers. Furthermore, it allows for an iterative learning process that is easy to set up compared to long and costly data collection processes. While this methodological approach may not “prove” the effectiveness of an intervention, it could provide insight into the reason a health system behaves in a given way (whether it changes or remains in a steady-state) when PBF is introduced. We believe that this type of information, although maybe less appealing to the usual stakeholders in development aid debates, is much more useful in evaluating PBF.

This study adopts a systemic view to investigate societal resilience within the whole-of-society framework for crisis preparedness, focusing on best practices, challenges, and solutions. Finland serves as the case study due to its pioneering position in crisis preparedness and its adoption of a comprehensive preparedness model that encompasses relationships and interactions among diverse stakeholders. In this study, the Finnish preparedness system is illustrated and analysed through the lens of complex adaptive systems (CAS). Data are collected through interviews with security actors representing different stakeholder groups, including civil society, businesses, and the public sector. An interpretative approach synthesises insights from literature, reports, and stakeholder interactions to co-create knowledge. The analysis covers the CAS tenets of context, relational constitution, adaptive capacity, emergence, and openness. The study presents an exploratory model anchored in CAS theory, incorporating key practices, processes, and adaptation loops integral to societal resilience from a systemic perspective in the Finnish context. From a theoretical point of view, this study contributes to CAS theory by exploring the role of context as a slow-changing variable, which is often considered a constant in CAS. Furthermore, while emergent behaviour is a critical component of CAS, most studies explore the emergent behaviour of a system within a short time span. However, the findings of this study highlight the importance of long-term emergent behaviour in addition to short-term behaviour. From a practical standpoint, this study not only explores best practices but also identifies the challenges of the Finnish system and provides a benchmark for other countries to develop their own crisis preparedness. However, replicating the system elsewhere may be challenging due to certain unique contextual factors.

The COVID-19 pandemic shed light on the fragility of today's public health systems and failure to sufficiently invest in preparedness. These shortcomings are observed in delays achieving timely, equitable, and sufficient access to life-saving vaccines when faced with erratic demand. This Current Opinion describes vaccine supply networks (VSNs) from a complex adaptive systems (CAS) lens, highlighting interactions between system elements and co-evolution with the environment in which they operate. More specifically, it shows how broadening the boundaries of VSNs reveals the high degree of complexity that leads to unexpected and emergent system behavior, especially when disease threats evolve over time and across geographies. A CAS lens allows for the design of improved management strategies to ensure continued performance of VSNs during both outbreak and inter-epidemic periods, thus contributing to sustained disease management. It points to ample opportunities for more integrated modeling across disciplines to capture inherent feedback loops that influence both VSNs and disease dynamics. Furthermore, it reveals how pandemic preparedness relies on a broader understanding of the mechanisms that drive outbreak prevention and control, beyond vaccines and their direct supply chains. Finally, it highlights the value of adaptive management to navigate inevitable future disruptions and associated uncertainties, overcoming limitations of typical risk-mitigation strategies based on prediction and control.

Complex adaptive systems (CAS), to reiterate, are systems composed of many individual parts or agents in which patterns can emerges as a result of agents deploying "simple rules" from the "bottom-up" without external control—CAS are "self-organizing" systems. "Simple rules" in health care would include seeking to optimize both patient well-being and the functioning of professionals. If elements of a CAS system are altered, the system adapts or reacts. The behaviour of a complex adaptive system can be inherently unpredictable and non-linear as elements of the system, the internal (eg, professionals and managers) and external agents (eg, patients, families, and society), have multiple perturbations, changes, and interdependencies. Despite the flurry of interest in complex systems and non-linear dynamics in recent decades, application of knowledge and innovation about complexity and adaptation in systems for health care has been slow. Critics typically state that there is no "evidence" that applying CAS and complexity science is needed or "works" in the real world of health care systems.1 It is almost a decade since the issues of practicability were first raised in this Forum in 2009.2 Has progress been made? A PubMed scan (Figure 1) provides some comfort in the growth of applications of CAS thinking in health research. In this Forum, Wietmarschen, Wortelboer, and van der Greef3 provide a highly accessible vision for the future of complex adaptive systems and why they are needed. They re-articulate why a shift is needed from static silos of diagnoses and linear structures toward a more integrated biopsychosocial way of thinking about health, using systems thinking approaches. Moreover, in their far-sighted appraisal of Western lifestyle problems of obesity and sedentary behaviours, they demonstrate practical modelling techniques integrating molecular with cognitive and psychological metrics, and variables from different layers of human functioning. A systems dynamics software tool called Method to Analyse Relations between Variables using Enriched Loops was used to create the model during the group sessions. The resulting model contained various positive and negative feedback loops connecting multiple health domains, indicating non-linear mechanisms affecting processes that cross multiple health domains. These techniques have been applied to the analyses of individual trajectories in a clinical approach to obesity in Vogellanden-Centre for Rehabilitation, Zwolle, the Netherlands. System dynamics modelling (SD), is an interdisciplinary modelling method used for representing and understanding the behaviour of complex systems. An SD model consists of a series of stocks, which represent the total people receiving a type of service at a given time, interconnected through flows, which represent the movement of people from one stock to another over time. Participatory approaches align stakeholder understanding of the underlying causes of a problem and can achieve consensus for action. Advances in software are allowing the participatory model building approach to be extended to more sophisticated multimethod modelling that provides policy makers with more powerful tools to support the design of targeted, effective, and equitable policy responses for complex health problems.4 Cepoiu-Martin and Bischak5 utilized a system dynamics model of the Alberta Continuing Care System (ACCS), Canada, using stylized data to assist service planning. They explored policies of introducing staff/resident benchmarks in both supportive living and long-term care (LTC) in the background of predicted increases in the population of people with dementia and the provision of staffing benchmarks, The ACCS model developed, by going beyond linear cause-effect considerations, and allowed the exploration of the entire network of causal relationships between various components of the system. It provided evidence of applicability of SD simulation to analysis of the impact of adopting benchmarks related to the staff/resident ratios in the continuing care system in Alberta. The model provides a basis for future evaluations of interventions in the workforce development area, capturing all feedbacks that modify balance between staff supply and demand in the age care sector. The following three papers highlight practical applications at the clinical coal-face, albeit all are early stage studies. Bandini et al6 have successfully piloted a clinical tool for episode complexity in inpatient care on internal medical wards. Episode complexity represents the need for greater time and effort (compared with other patients and episodes) with respect to clinical assessment and treatment; relationships with the patients, caregivers, other specialists, and actors in the health care network; and information gathering and processing. A very interesting emergent finding from their study is that multimorbidity as measured by the Charlson comorbidity index was not a good predictor of episode complexity, as patients with multiple comorbidities often had simple hospital episodes while those without little comorbidity (low Charlson comorbidity score) had much more complex episodes with much less certain outcomes. The dynamics of those individual illness trajectories were not predicted by standard static disease based metrics nor supported by guidelines. Individual trajectories or journeys is a recurring theme in the developing CAS approaches in health care, representing the opportunity for responding to health status dynamics in a timely manner.7 This notion of intellectual work and time as markers of clinical complexity was also raised by Katerndahl et al in a previous analysis of medical work across clinical specialities.8 In complex systems, as the information in the input increases linearly, the complexity of the system increases exponentially. Thus, a simple rule is suggested, that clinical work complexity reflects the amount of care provided weighted by its diversity and variability. Primary care, because of its diversity and variability, scores highly on the amount of work demanded of its practitioners. In this theme, Fink et al9 describe the application of a clinical tool—Diagnostic Protocols (DP)—in a single handed practice over a 14-year period. Based on several decades of work by Braun and colleagues, DP represents a series of simple rules to reduce uncertainty in primary care presentations of serious conditions that may seem at first contact to be routine and non-serious. Here, we have the common theme of simple rules to identify courses of action related to simple and complex dynamics in patient trajectories over time in clinical care. At an organizational level, leadership is a crucial element of success, and its role is recognized as an important factor for achieving better performance and optimizing health improvements for patients. Horvat and Filipovic,10 using complexity leadership theory, identified three types of leadership and matched them to indicators of organizational maturity. Administrative leadership is grounded in traditional, bureaucratic notions of hierarchy, alignment, and control. Enabling leadership structures and enables conditions in which CAS can optimally address creative problem solving, adaptability, and learning. Adaptive leadership exemplifies a generative dynamic that underlies emergent change activities. Organizational maturity promotes organizational learning, enables effective and efficient management performance, reduces errors, and adapts to internal and external dynamics. Sustained success can be achieved by the effective management of the organization, through awareness of the organization's environment, by learning, and by the appropriate application of either improvements, or innovations, or both.11 Their survey of Serbian managers supported the hypothesis that administrative leadership had little influence on any maturity category of health care organizations. Adaptive and enabling leadership had greater association with managerial maturity. However, both adaptive and enabling leadership were also correlated with administrative leadership reflecting the entanglement of traditional structures and cultures of health care organizations with bottom-up informal emergent forces. A question that might be asked is: whether administrative leadership maintain the status quo by constraining emergence and self-organization to the detriment of organizational adaptability and learning? On an optimistic note, de Bock et al12 provide a case study of such bottom-up informal complex adaptive forces that successfully shifted clinical decision-making from professional silos into transdisciplinary inter-professional working. These shifts were driven by the internal and external tensions about caring for a longitudinal patient journey beyond technical rescue. The personal power of the nurses who were by the bedside, and their "bottom-up" understanding of the patient's needs, catalysed interdependent interactions and self-organization within the different professional groups. Care was thus adapted to patient-centred approaches beyond reductionist repair modes of thinking. This Forum highlights this emerging implementation of practical, but early stage CAS approaches to improving the outcomes of clinical care and health care more generally. To progress, a vision and practical goals for the shift needed from a conservative medical hierarchical disease focus, toward a more integrated biopsychosocial dynamic interactive ways of thinking about health.3 Tools to enable such implementation are needed, and four different practical approaches to deploy CAS theory in clinical care are highlighted that demonstrate innovation and adaptive thinking. They demonstrate a transition into enabling and adaptive leadership roles from the bottom-up. Yet the paper by Horvat and Filipovic provides some explanation about the slowness of the such transitions related to the challenges to complexity based leadership, with the ever-present dominant conservative health organizations. Administrative leadership models and cultures seeks to maintain the status quo and, for all intense and purposes, stand in the way of innovation and the emergence of "adapting and innovative" processes of care, system organization, and leadership. The International Organization for Standardization, a worldwide federation of national standards bodies (ISO member bodies), states that achievement of sustained success for any organization in a complex, demanding, and ever-changing environment requires enabling and adaptive leadership in health organizations.11 Health care will have to go through a huge cultural change to improve its organizational maturity with enabling and adaptive leadership. There is a need to successfully shape new ways of working and organizing in the evolution of health care. The role of adaptive leadership, as Ron Heifetz pointed out so eloquently, is not to solve problems, but rather to facilitate the necessary adaptive work of the people directly confronting the problems, often in the front-line in health care.13

After the End of Free Fall: Geriatricizing Primary Care

This chapter introduces complexity and its contribution to objective uncertainty in mental health and addiction. The view of the brain as a complex adaptive system is discussed, and the conceptualization of the mind as emergent from the brain is introduced. Limits that complexity imposes on the accuracy of establishing causation, pattern recognition, and predictions—all three of which are fundamental to clinical practice and the research that informs it—are explained. The pragmatic utility of viewing the brain and mind through a lens of complex adaptive systems is discussed with emphasis on how it makes many sources of clinical uncertainty irreducible.

The present work explores bacterial colonies and their individual and social behaviours under the lens of complex adaptive systems. We initially provide a background on the biology of bacteria to describe important phenomena, such as quorum-sensing, individual and collective behaviours, adaptation, evolution and self-organization over the influence of mechanical effects on bacterial systems and connecting scales. We then explore some associations between bacterial colonies and complex adaptive systems by considering components and ownerships of self-organization. The main contribution of this paper places emphasis on individual decision-making and behaviour as a cause of bacterial colonies’ actions, i.e., how self-organization and collective behaviours impact the ability of a bacterial colony to address an environmental stimulus and maintain itself as an open biological and fault-tolerant system. Finally, we conclude the work and provide some comments regarding future research.