New Directions for Evidence Science, Complex Adaptive Systems, and a Possibly Unprovable Hypothesis About Human Flourishing

This paper will explore the concept of resilience from its roots in ecology to the application of this ecological concept of resilience to social and community resilience in the context of climate change. In this context, resilience is seen as a property of complex adaptive communities rather than of individuals. This paper will explore how this ecological concept of resilience has been taken up both by climate adaptation research and by the Transition Town movement. This ecological concept of resilience is at odds with the individualism of both psychological and economic approaches to resilience in relation to climate change.

In popular dialogues, describing a system as "complex" is often the point of resignation, inferring that the system cannot be sufficiently described, predicted nor managed. Transport networks, management infrastructure and supply chain logistics are all often described in this way. Academic dialogues have begun to explore the collective behaviors of complex systems to define a complex system specifically as an adaptive one; i.e. a system that demonstrates 'self organising' principles and 'emergent' properties. Based upon the key principles of interaction and emergence in relation to adaptive and self organising systems in cultural artifacts and processes, this paper will argue that complex systems are cultural systems. By introducing generic principles of complex systems, and looking at the exploration of such principles in art, design and media research, this paper argues that a science of cultural systems as part of complex systems theory is the post modern science for the digital age. Furthermore, that such a science was predicated by post structuralism and has been manifest in art, design and media practice since the late 1960s.

*Correspondence: cgg@unam.mx 1Universidad Nacional Autonoma de Mexico, A.P. 20-726, 01000 Mexico city, D.F, Mexico Full list of author information is available at the end of the article Complex systems and networks Complex Adaptive Systems (CAS) or complex systems are characterized by the interactions between their numerous elements. The word ‘complex’ comes from the Latin plexus which means entwined. In other words, it is difficult to correlate global properties of complex systems with the properties of the individual constituent components. This is primarily because the interactions between these individual elements partly determine the future states of the system (Gershenson 2013). If these interactions are not included in the developed models, the models would not be an accurate reflection of the modelled phenomenon. While numerous techniques and frameworks for modeling complex systems have previously been devised (Niazi 2011), clearly one of the most explicit and intuitive methodology is the modeling of interactions using networks (Niazi and Hussain 2012). Networks consist of nodes or vertices, which can be used to represent elements, and links or edges, which usually represent interactions or relations between the elements. In this context, networks represent the structure of complex systems; how elements interact. However, networks can also be used to represent the dynamics or function of complex systems, e.g. considering nodes as states and links as transitions. Thus, the same analysis can be applied to the structure and the function of networks. Understanding the relationship between structure and function is one of the major open questions across sciences, which can also be posed using networks: how do changes in the structural network affect the state network? (Boccaletti et al. 2006; Gershenson 2012). For example, what will be the effect of knocking out a gene in the behavior of a cell? Several systems change their structure over time, and their properties can be modelled with temporal networks (Holme and Saramaki 2012). Likewise, there are several instances when the structural changes are triggered by the state of the network, as has been studied in adaptive networks (Gross and Sayama 2009). With not much more than a decade of network research, there are already numerous applications of networks in diverse areas, such as epidemiology (Colizza et al. 2007; Christakis and Fowler 2007; Pastor-Satorras and Vespignani 2001), human mobility (Gonzalez et al. 2008), social networks (Huberman et al. 2009; Niazi and Hussain 2011), artificial life (Gershenson and Prokopenko 2011), life sciences (Bullmore and Sporns 2009; Gershenson 2004; Guimera and Nunes Amaral 2005; Montoya et al. 2006), theory of

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.

The Ostroms and Hayek as Theorists of Complex Adaptive Systems: Commonality and Complementarity

This chapter uses the theory of complex systems as a conceptual lens through which to compare the work of Friedrich Hayek with that of Vincent and Elinor Ostrom. It is well known that, from the 1950s onwards, Hayek conceptualised the market as a complex adaptive system. It is argued in this chapter that, while the Ostroms began explicitly to describe polycentric systems as a class of complex adaptive system from the mid-to-late 1990s onwards, they had in fact developed an account of polycentricity as displaying most if not all of the hallmarks of organised complexity long before that time. The Ostromian and Hayekian approaches can thus be seen to share a good deal in common, with both portraying important aspects of society – the market economy in the case of Hayek, and public economies, legal and political systems, and environment resources in the case of the Ostroms – as complex rather than simple systems. Aside from helping to bring out this aspect of the Ostroms’ work, using the theory of complex systems as a framework for comparing the Hayekian and Ostromian approaches serves two other purposes. First, it can be used to show how one widely criticised aspect of Hayek’s theory of society as a complex system, namely his account of cultural evolution via group selection, can be strengthened by an appeal to the work of Elinor Ostrom. Second, it also helps to resolve a tension – ultimately acknowledged by the Ostroms themselves – between some of their explicit methodological pronouncements and the actual, substantive approach they adopted in their analysis of polycentric systems.

This chapter introduces Complex Adaptive Systems Thinking (CAST) into the domain of Intellectual Capital (IC). CAST is based on the theories of Complex Adaptive System (CAS) and Systems Thinking (ST). It argues that the CAST, combined with Intelligence Base offers a potentially more holistic approach to managing the Intellectual Capital of an organization. Furthermore, the authors extend this IC management with additional dimensions proper to a social entity such as an organization. New organizational design methods are needed and the capability approach is such a method that supports IC in virtual and real organizations. The characteristics of Intellectual Capital are discussed in the iterative process of inquiry and the Cynefin Framework, guaranteeing a holistic view on the organization and its environment.

Pragmatic Trials and Improving Long-Term Care: Recommendations From a National Institutes of Health Conference.

Emergence in stigmergic and complex adaptive systems: A formal discrete event systems perspective

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

Introduction to COMPASS: navigating complexity in public health research

An inherent affinity exits between the evolutionary interpretation of the invisible hand and the science of complexity. The evolutionary interpretation regards the mar ket economy as an adaptive, self-organizing, and evolving process while the science of complexity studies the endogenous emergence of patterns and structures in com plex systems. The invisible hand provides an intellectual and historical backdrop for the complexity science while certain tools and techniques in complexity have helped economists gain a deeper understanding of the market process and gaze at the in visible hand.

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.

Complexity science is a new field of science that help us to understand complex social phenomenon such as economies, traffics, wars, epidemics, mass actions, etc. On the other hand, multi-agents simulation (MAS) methods are useful and powerful tools for complex system science. They come to be applied to studies and analysis of complex systems along with the development of computers. It can be said that the perspectives of complex systems and the skills for MAS are very useful knowledge for students who should live and work in the complex modern society. Then the author tried to design and conduct an education program dealing with complex system science and MAS as liberal arts targeting at students from all faculties. It's a project-based learning program in which students investigated and discussed What is complex systems? and develop MAS. In general, it is difficult for students who haven't gained the any special knowledge of computing to build MAS. Then the author applied the generic multi-agent simulation platform to this program. With artisoc, students can build MAS easily. In this paper, the practical example of PBL program with artisoc and its result are introduced.

Complex adaptive systems (CAS) have been identified as being hard to comprehend, composed of multiple interacting components acting interdependently with overlapping functions aimed at adapting to external/environmental forces. The current theoretical model utilized the natural functions of teams, viewing teams as a complex adaptive system, to develop the structure of the theory of complex adaptive team systems (CATS). The CATS model was formulated around the components of complexity theory (interactions, nonlinearity, interdependency, heterogeneity, complex systems, emergence, self-organizing, and adaptability) to show its utility across multiple domains (the role of leadership, organizational learning, organizational change, collective cognitive structures, innovation, cross-business-unit collaborations). In theorizing the CATS model, a new level of analysis was implemented, the interactions between agents as a move toward emergence in complex systems. The CATS model ultimately provides a model for organizations/institutions to drive knowledge creation and innovation while operating in today's complexity.