Society as a Complex Adaptive System

Complex Adaptive Systems, for our purposes, are social systems that that evolve and display new, emergent properties, and self-organizing behavior of their components; they are based on a reasonably stable infrastructure, on the satisfaction of the most basic needs, and flexible, frequent, and open communication and interaction. Complex Adaptive Systems may be based on a few, simple rules, but can yield complex and unpredictable outcomes. The ‘Hole in the Wall’ project is an interesting case in point in the design of spaces for complex adaptive systems, or complex adaptive networks. In this project, touch screen computers were literally put in ‘holes in walls’ in places where unschooled children congregated. The children were given no instructions on how to use the computers, or what to do with them, but with startling results: the children soon taught themselves how to use the computers and the Internet, and much more (Mitra, 2003).

Complex Adaptive Systems, for our purposes, are social systems that that evolve and display new, emergent properties, and self-organizing behavior of their components; they are based on a reasonably stable infrastructure, on the satisfaction of the most basic needs, and flexible, frequent, and open communication and interaction. Complex Adaptive Systems may be based on a few, simple rules, but can yield complex and unpredictable outcomes. The ‘Hole in the Wall’ project is an interesting case in point in the design of spaces for complex adaptive systems, or complex adaptive networks. In this project, touch screen computers were literally put in ‘holes in walls’ in places where unschooled children congregated. The children were given no instructions on how to use the computers, or what to do with them, but with startling results: the children soon taught themselves how to use the computers and the Internet, and much more (Mitra, 2003).

Panarchy theory focuses on improving theories of change in natural and social systems to improve the design of policy responses. Its central thesis is that successfully working with the dynamic forces of complex adaptive natural and social systems demands an active adaptive management regime that eschews optimization approaches that seek stability. This is a new approach to resources management, and yet no new theory of how to do things in environmental and natural resources management, particularly one challenging entrenched ways of doing things and the interests aligned around them, is likely to gain traction in practice if it cannot gain traction in the form of endorsement and implementation through specific laws and regulations. At some point, that bridge must be crossed or the enterprise of putting panarchy theory into panarchy practice will stall. Any effort to operationalize panarchy theory through law thus comes up against the mission of law to provide social stability and the nature of law itself as a complex adaptive system. To state the problem in another way, putting panarchy theory into practice will require adaptively managing the complex adaptive legal system to adaptively manage other complex adaptive natural and social systems, all in a way that maintains some level of social order. Panarchy theorists have yet to develop an agenda for doing so. It is time for lawyers to join the team.

Keynes’ ‘fallacy of composition’ and, later, the ‘Santa Fe perspective’ have shown that the economy may be defined as a “complex adaptive system”, since it is not something given but results from constantly developing interactions among heterogeneous individuals. Endorsing the definition of ‘dynamic complexity’, the paper analyses the extent to which Keynes’ notion of complexity is coterminous with the one developed at the Santa Fe Institute. An analysis of the role of expectations in the General Theory, along with the dimension of time, history and path-dependence, makes it possible to conceive Keynes’ work as an open, complex evolving system and to establish a direct connection with the ‘Santa Fe perspective’. However, Keynes’ “two-stage methodology” which allows the analyst to deal with complex organic material without resorting to reductionism by means of ordinary language involves the inherent non-measurability of complex magnitudes which does not depend on the individual’s limited ability to measure the complex object. This feature may cast some doubts on the inheritance of Keynes’s methodology by the ‘Santa Fe perspective’, which attempts to handle complexity through simulation techniques implicitly based on a sophisticated mathematical formalism.

The Use of Complex Adaptive Theory and Information Technologies to Inform Development Strategies in English Speaking Black Community, Montreal

Linking Social and Ecological Systems

Ecosystems and social systems as complex adaptive systems

Financial markets are complex adaptive systems, in which investors' behaviors and the dynamic diversity have much impacts on the quality of market, and investors adapt to the environment by improving their strategies. Based on the research achievements ot the Santa Fe Institute, we build up an artificial stock market model, with which we analysis the behavior of investors who are featured by social learning mechanism, and study the effects of dynamic changes on the stock market at macro and micro levels. Based on the analysis of investors' behavior, we model artifitial stock market according to herd behavior, and conduct simulation experiments combined with the interaction mechanism of agents. The results of simulation show that communicator is a key factor in the process of new shares' issuance, so maintain a certain number of communicators is crucial to the success of IPO.

Quantifying the relationship of resilience and eco-efficiency in complex adaptive energy systems

Background IE as a field studies systems that consist of a myriad of interconnected environmental, social, economic and technical systems. People, machines, organization, the bio-geo-chemical environment, infrastructures, markets, laws and habits form a complex adaptive socio-technical system, evolving through constant parallel and distributed interaction of heterogeneous social and technical elements. Understanding and shaping this evolution is essential if we are to rise to the challenges of climate change, resource scarcity, population growth and world peace. These system can be characterized as Complex Adaptive Systems, which John H. Holland [8] defines as:

Managing Systemic Risk in Legal Systems

Introduction Traditionally, numerous studies that analysed the impact of human activities on ecosystems conceptualised humans either as external drivers of ecosystems (albeit not influenced by them) or as users of the environment (but not influencing it). However, in recent years, studies have changed their focus on the interdependency between social systems and ecosystems (Gunderson & Holling, 2002; Holling, 2001). In this sense, Farina (2000) highlights cultural landscapes as human land use systems shaped by the intricate relationship between social and ecological systems. They are the result of a slow co-evolution of both systems and they have taken centuries to reach their current configuration, reflecting a mutual adaptation of abiotic, biotic and cultural factors. Here, we argue that complexity theory offers new opportunities for conceptual and theoretical development when cultural landscapes are considered as complex adaptive social–ecological systems (Liu et al ., 2007; Rescia et al ., 2010; Walker et al ., 2004). Complex adaptive systems are characterised by cross-scale non-linear interactions and feedback loops between the ecological and socioeconomic components and they present stable dynamics characterised by three complementary attributes: resilience, transformability and adaptability (Walker et al ., 2004). In many studies, the concept of resilience has been seen as the capacity to withstand or recover from disturbance, while still essentially retaining the same function, structure, identity and interconnections (Walker et al ., 2004). In cultural landscapes, resilience is considered as the capacity for transformation and adaptation of the population and their activities (i.e. land use system) and ecosystems in the face of gradual changes in socioeconomic conditions and biophysical characteristics, until these cross certain thresholds (Berkes & Folke, 1998; Holling, 2001). In a normative sense, resilience of cultural landscapes ensures the flows of services they supply, such as cultural identity, heritage value, recreation and tourism, land use diversity, biodiversity and carbon sequestration (Oteros-Rozas et al ., Chapter 14).

The recent analytic essay by Greenberg et al. rightfully emphasized the role of the public health academia in preventing global chronic disease.1 The authors focused on key areas such as resource allocation and overcoming barriers to prevention, but paid less attention to academia's crucial role in advancing fresh thinking or innovative models to tackle the epidemic. Two such emerging concepts are Complexity Science (CS) and Allostatic Load (AL) in the causation of chronic diseases.2–5 Our worldview is shifting from mechanistic models to the new CS paradigm. CS perceives social systems (e.g., financial markets) and natural systems (e.g., population health) as emergent properties of open, dynamic, nonlinear, and adaptive systems (i.e., Complex Adaptive Systems [CASs]). Patterns of health and chronic diseases are conceptualized as emergent properties of populations.3 These patterns arise from networks of interactions among dynamic sets of interconnected nonlinear subsystems (e.g., political systems, physical and social environments) that predominantly reside in the latter.6,7 To facilitate changes in a CAS (i.e., health of a population) multiple interventions are required in several subsystems and sectors rather than one magic bullet such as vaccines. Interventions have to be flexible, constantly monitored, and evaluated, and information must be fed back to change the course of intervention. There are few documented reports of CS in health interventions, and a recent report describes its application in an attempt to reduce cardiovascular diseases morbidity and mortality among Canadian Asians.8 Exploring CASs in health will deepen our understanding of how social systems, climate change, human behaviors, and physical environments promote development of chronic diseases in humans and will help us tackle this issue. AL denotes the cumulative wear and tear or the physiological toll experienced by the body over a life course to adapt to biological, psychological, and environmental demands to maintain homeostasis.4 It postulates that chronic diseases are mediated through stress pathways and suggests that stressors of daily living play a role in promoting chronic diseases rather than stemming from a limited set of risk factors.9 Thus, an ordinary social life (as we understand it) may promote chronic diseases over and above what can be explained by the extended life span and traditional risk factors. Because chronic disease affects a majority of the global population, the burden can be substantial. These are just two examples of how fresh thinking and innovative approaches may help us to unravel, understand and contain the epidemic of chronic diseases.

BackgroundThe concept of complexity is used in palliative care (PC) to describe the nature of patients’ situations and the extent of resulting needs and care demands. However, the term or concept is not clearly defined and operationalised with respect to its particular application in PC. As a complex problem, a care situation in PC is characterized by reciprocal, nonlinear relations and uncertainties. Dealing with complex problems necessitates problem-solving methods tailored to specific situations. The theory of complex adaptive systems (CAS) provides a framework for locating problems and solutions.This study aims to describe criteria contributing to complexity of PC situations from the professionals’ view and to develop a conceptual framework to improve understanding of the concept of “complexity” and related elements of a PC situation by locating the complex problem “PC situation” in a CAS.MethodsQualitative interview study with 42 semi-structured expert (clinical/economical/political) interviews. Data was analysed using the framework method. The thematic framework was developed inductively. Categories were reviewed, subsumed and connected considering CAS theory.ResultsThe CAS of a PC situation consists of three subsystems: patient, social system, and team. Agents in the "system patient" are allocated to further subsystems on patient level: physical, psycho-spiritual, and socio-cultural. The "social system" and the "system team" are composed of social agents, who affect the CAS as carriers of characteristics, roles, and relationships. Environmental factors interact with the care situation from outside the system. Agents within subsystems and subsystems themselves interact on all hierarchical system levels and shape the system behaviour of a PC situation.ConclusionsThis paper provides a conceptual framework and comprehensive understanding of complexity in PC. The systemic view can help to understand and shape situations and dynamics of individual care situations; on higher hierarchical level, it can support an understanding and framework for the development of care structures and concepts. The framework provides a foundation for the development of a model to differentiate PC situations by complexity of patients and care needs. To enable an operationalisation and classification of complexity, relevant outcome measures mirroring the identified system elements should be identified and implemented in clinical practice.

“The study of cas is a difficult, exciting task. The returns are likely to be proportionate to the difficulty.” Holland (2006) On August 9, 2015, cancer took Prof. John Henry Holland away from us. Prof. Holland was a pioneer of Complex Adaptive Systems (CAS) research and a true inspiration. He is known not only for his work on CAS, Holland (1962, 1992)—which he would fondly write as “cas”—but also for his seminal work on adaptation in natural and artificial systems leading to the creation of genetic algorithms and eventually the fields of evolutionary computation, Holland (1995) and Learning Classifier Systems, Holland and Holyoak (1989). Holland was a truly interdisciplinary academic. He had an undergraduate degree in Physics from MIT (1950), an M.A. in Mathematics (1954) and possibly the first ever PhD in Computer Science (1959), both from the University of Michigan—a place where he also subsequently served as a Professor of Psychology, Electrical Engineering and Computer Science. Holland leaves behind his legacy in the form of a large number of thought-provoking articles, video lectures, books, and inspired people—ranging from colleagues, fellows and students to budding complexity enthusiasts. Two of his recent books summarize his views on CAS in both a longer, Holland (2012) as well as a shorter form, Holland (2014). It is easy to foresee that these works will serve not only as a guide to CAS but also guidance for future generations. Holland will indeed be greatly missed. Links to some of his online obituaries are as follows: Melanie Mitchell http://tinyurl.com/qcj22tv National Center for Science Education http://tinyurl.com/pm7ga8y New York Times http://tinyurl.com/p5cd22u Santa Fe Institute http://tinyurl.com/qhkgtxd The Scientist http://tinyurl.com/pns6t64 University of Michigan http://tinyurl.com/obbdpx5 Washington Post http://tinyurl.com/oalp27x