Showing papers on "Complex adaptive system published in 2005"

Supply-chain networks: a complex adaptive systems perspective

Abstract: In this era, information technology is revolutionizing almost every domain of technology and society, whereas the ‘complexity revolution’ is occurring in science at a silent pace. In this paper, we look at the impact of the two, in the context of supply-chain networks. With the advent of information technology, supply chains have acquired a complexity almost equivalent to that of biological systems. However, one of the major challenges that we are facing in supply-chain management is the deployment of coordination strategies that lead to adaptive, flexible and coherent collective behaviour in supply chains. The main hurdle has been the lack of the principles that govern how supply chains with complex organizational structure and function arise and develop, and what organizations and functionality are attainable, given specific kinds of lower-level constituent entities. The study of Complex Adaptive Systems (CAS), has been a research effort attempting to find common characteristics and/or formal distinctio...

Emergence versus self-organisation: different concepts but promising when combined

Abstract: A clear terminology is essential in every research discipline. In the context of ESOA, a lot of confusion exists about the meaning of the terms emergence and self-organisation. One of the sources of the confusion comes from the fact that a combination of both phenomena often occurs in dynamical systems. In this paper a historic overview of the use of each concept as well as a working definition, that is compatible with the historic and current meaning of the concepts, is given. Each definition is explained by supporting it with important characteristics found in the literature. We show that emergence and self-organisation each emphasise different properties of a system. Both phenomena can exist in isolation. The paper also outlines some examples of such systems and considers the combination of emergence and self-organisation as a promising approach in complex multi-agent systems.

From simplistic to complex systems in economics

Abstract: The applicability of complex systems theory in economics is evaluated and compared with standard approaches to economic theorising based upon constrained optimisation A complex system is defined in the economic context and differentiated from complex systems in physio-chemical and biological settings It is explained why it is necessary to approach economic analysis from a network, rather than a production and utility function perspective, when we are dealing with complex systems It is argued that much of heterodox thought, particularly in neo-Schumpeterian and neo-Austrian evolutionary economics, can be placed within a complex systems perspective upon the economy The challenge is to replace prevailing 'simplistic' theories, based in constrained optimisation, with 'simple' theories, derived from network representations in which value is created through the establishment of new connections between elements

Complex adaptive systems and the diffusion of innovations

Abstract: The diffusion of innovations model (DIM) and complex adaptive systems theory (CAS) can be employed together in the construction of predictive or applied hybrid models of induced change in population behavior. In such interventions, differentiated heterogeneous zones may act as catalysts for the adoption of innovation. The present study explores the actual and potential hybridization of these two systems theories, relying on illustrations from historical practical applications of DIM, particularly the STOP AIDS communication campaign in San Francisco. The resulting co-theoretical model provides an analytical tool for students of innovation, particularly in the public sector, and especially in applications of network analysis predicated on a crucially defining feature of social networks, namely “the strength of weak ties” among their members. In cultivating network ties among heterogeneous groups connected by common aims, it is here argued, the innovator may prompt and, to an extent, guide the complex emergence of innovation adoption in social systems. Commonalities in the concept of heterogeneity in CAS and in DIM is explored in depth, along its many dimensions, including membership and role heterogeneity, with a view to preliminary operationalization of diffusion-management principles.

Self-organization and the Emergence of Complexity in Ecological Systems

Abstract: What explains the remarkable regularities in distribution and abundance of species, in size distributions of organisms, or in patterns of nutrient use? How does the biosphere maintain exactly the right conditions necessary for life as we know it? Gaia theory postulates that the biota regulates conditions at levels it needs for survival, but evolutionary biologists reject this explanation because it lacks a mechanistic basis. Similarly, the notion of self-organized criticality fails to recognize the importance of the heterogeneity and modularity of ecological systems. Ecosystems and the biosphere are complex adaptive systems, in which pattern emerges from, and feeds back to affect, the actions of adaptive individual agents, and in which cooperation and multicellularity can develop and provide the regulation of local environments, and indeed impose regularity at higher levels. The history of the biosphere is a history of coevolution between organisms and their environments, across multiple scales o...

Complex adaptive systems: concept analysis

Abstract: Aim. The aim of this paper is to explicate the concept of complex adaptive systems through an analysis that provides a description, antecedents, consequences, and a model case from the nursing and health care literature. Background. Life is more than atoms and molecules – it is patterns of organization. Complexity science is the latest generation of systems thinking that investigates patterns and has emerged from the exploration of the subatomic world and quantum physics. A key component of complexity science is the concept of complex adaptive systems, and active research is found in many disciplines – from biology to economics to health care. However, the research and literature related to these appealing topics have generated confusion. A thorough explication of complex adaptive systems is needed. Methods. A modified application of the methods recommended by Walker and Avant for concept analysis was used. Findings. A complex adaptive system is a collection of individual agents with freedom to act in ways that are not always totally predictable and whose actions are interconnected. Examples include a colony of termites, the financial market, and a surgical team. It is often referred to as chaos theory, but the two are not the same. Chaos theory is actually a subset of complexity science. Complexity science offers a powerful new approach – beyond merely looking at clinical processes and the skills of healthcare professionals. Conclusion. The use of complex adaptive systems as a framework is increasing for a wide range of scientific applications, including nursing and healthcare management research. When nursing and other healthcare managers focus on increasing connections, diversity, and interactions they increase information flow and promote creative adaptation referred to as self-organization. Complexity science builds on the rich tradition in nursing that views patients and nursing care from a systems perspective.

Computability and Evolutionary Complexity: Markets as Complex Adaptive Systems (CAS)

Abstract: Few will argue that the epi-phenomena of biological systems and socio-economic systems are anything but complex. The purpose of this Feature is to examine critically and contribute to the burgeoning multi-disciplinary literature on markets as complex adaptive systems (CAS). The new sciences of complexity, the principles of self-organisation and emergence along with the methods of evolutionary computation and artificially intelligent agent models have been developed in a multi-disciplinary fashion. The cognoscenti here consider that complex systems whether natural or artificial, physical, biological or socio-economic can be characterised by a unifying set of principles. Further, it is held that these principles mark a paradigm shift from earlier ways of viewing such phenomenon.

Complexity Theory and Strategic Change: An Empirically Informed Critique

Abstract: We present a four-year ethnographic study of a public-sector organization and use narrative to describe its development in terms of four complexity theory concepts: sensitivity to initial conditions, negative and positive feedback processes, disequilibrium and emergent order. Our study indicates that order emerges at the boundary between the organization's legitimate and shadow systems. We suggest that the underlying dynamic leading to the emergent order is the need to reduce anxiety. Our findings cause us to question the assertion that organizations are naturally complex adaptive systems producing novel forms of order. We propose an alternate view that in social systems, equilibrium-seeking behaviour is the norm; such systems can self-organize into hierarchy. We draw attention to some of the difficulties we found in applying complexity-theory concepts to a social system and conclude by advocating the development of complexity theory through the incorporation of insights from psychology and social theory.

Computability and Evolutionary Complexity: Markets as Complex Adaptive Systems (CAS)*

Abstract: Few will argue that the epi-phenomena of biological systems and socio-economic systems are anything but complex. The purpose of this Feature is to examine critically and contribute to the burgeoning multi-disciplinary literature on markets as complex adaptive systems (CAS). The new sciences of complexity, the principles of self-organisation and emergence along with the methods of evolutionary computation and artificially intelligent agent models have been developed in a multi-disciplinary fashion. The cognoscenti here consider that complex systems whether natural or artificial, physical, biological or socio-economic can be characterised by a unifying set of principles. Further, it is held that these principles mark a paradigm shift from earlier ways of viewing such phenomenon.

User-Centred Design of Flexible Hypermedia for a Mobile Guide: Reflections on the HyperAudio Experience

Abstract: A user-centred design approach involves end-users from the very beginning. Considering users at the early stages compels designers to think in terms of utility and usability and helps develop a system based on what is actually needed. This paper discusses the case of HyperAudio, a context-sensitive adaptive and mobile museum guide developed in the late 1990s. User requirements were collected via a survey to understand visitors' profiles and visit styles in natural science museums. The knowledge acquired supported the specification of system requirements, helping define the user model, data structure and adaptive behaviour of the system. User requirements guided the design decisions on what could be implemented by using simple adaptable triggers, and what instead needed more sophisticated adaptive techniques. This is a fundamental choice when all the computation must be done on a PDA. Graphical and interactive environments for developing and testing complex adaptive systems are discussed as a further step in an iterative design process that considers the user interaction to be the central point. This paper discusses how such an environment allows designers and developers to experiment with different system behaviours and to widely test it under realistic conditions by simulating the actual context evolving over time. The understanding gained in HyperAudio is then considered from the perspective of later developments: our findings still appers to be valid despite the time that had passed.

Health care and services delivery systems as complex adaptive systems

Abstract: Alan Turing and John von Neumann pioneered the study of complex systems. In analyzing feedback processes, they were interested in how complex interacting systems can respond to new information. Among other things, they found that some systems with many interactions among highly differentiated parts can produce surprisingly simple, predictable behavior (such as a programmable mechanical routine or process), while others generate behavior that may be impossible to predict, even though these systems feature simple laws and few actors or agents (such as an evolving living organism).

Governing social-ecological systems

Abstract: Social-ecological systems are complex adaptive systems where social and biophysical agents are interacting at multiple temporal and spatial scales. The main challenge for the study of governance of social-ecological systems is improving our understanding of the conditions under which cooperative solutions are sustained, how social actors can make robust decisions in the face of uncertainty and how the topology of interactions between social and biophysical actors affect governance. We review the contributions of agent-based modeling to these challenges for theoretical studies, studies which combines models with laboratory experiments and applications of practical case studies.Empirical studies from laboratory experiments and field work have challenged the predictions of the conventional model of the selfish rational agent for common pool resources and public-good games. Agent-based models have been used to test alternative models of decision-making which are more in line with the empirical record. Those models include bounded rationality, other regarding preferences and heterogeneity among the attributes of agents. Uncertainty and incomplete knowledge are directly related to the study of governance of social-ecological systems. Agent-based models have been developed to explore the consequences of incomplete knowledge and to identify adaptive responses that limited the undesirable consequences of uncertainties. Finally, the studies on the topology of agent interactions mainly focus on land use change, in which models of decision-making are combined with geographical information systems.Conventional approaches in environmental economics do not explicitly include non-convex dynamics of ecosystems, non-random interactions of agents, incomplete understanding, and empirically based models of behavior in collective action. Although agent-based modeling for social-ecological systems is in its infancy, it addresses the above features explicitly and is therefore potentially useful to address the current challenges in the study of governance of social-ecological systems.

Complexity Theory and Strategic Change: an Empirically Informed Critique*

Abstract: We present a four-year ethnographic study of a public-sector organization and use narrative to describe its development in terms of four complexity theory concepts: sensitivity to initial conditions, negative and positive feedback processes, disequilibrium and emergent order. Our study indicates that order emerges at the boundary between the organization's legitimate and shadow systems. We suggest that the underlying dynamic leading to the emergent order is the need to reduce anxiety. Our findings cause us to question the assertion that organizations are naturally complex adaptive systems producing novel forms of order. We propose an alternate view that in social systems, equilibrium-seeking behaviour is the norm; such systems can self-organize into hierarchy. We draw attention to some of the difficulties we found in applying complexity-theory concepts to a social system and conclude by advocating the development of complexity theory through the incorporation of insights from psychology and social theory.

Multi-agent power market simulation using EMCAS

Abstract: Countries around the world continue to restructure their electricity markets and open them up to competition and private investors in pursuit of economic efficiency and new capital investment However, the recent volatility exhibited by many restructured power markets, in combination with several prominent market failures, have highlighted the need for a better understanding of the complex interactions between the various market participants and the emerging overall market behavior Advanced modeling approaches are needed that simulate the behavior of electricity markets over time and model how market participants may act and react to changes in the underlying economic, financial, and regulatory environments This is particularly useful for developing sound market rules that will allow these markets to function properly A new and promising approach is to model electricity markets as complex adaptive systems using an agent-based modeling and simulation approach, such as is implemented in the electricity market complex adaptive system (EMCAS) software EMCAS provides an agent-based framework to capture and investigate the complex interactions between the physical infrastructures and the economic behavior of market participants that are a trademark of the newly emerging markets This paper describes the EMCAS agents, their interactions, the unique insights obtained from agent-based models, and discusses current model applications in several US, Asian, and European markets

Complex Acts of Knowing: Paradox and Descriptive Self-Awareness

Abstract: We are reaching the end of the second generation of knowledge management, with its focus on tacit-explicit knowledge conversion. Triggered by the SECI model of Nonaka, it replaced a first generation focus on timely information provision for decision support and in support of BPR initiatives. Like BPR it has substantially failed to deliver on its promised benefits. The third generation requires the clear separation of context, narrative and content management and challenges the orthodoxy of scientific management. Complex adaptive systems theory is used to create a sense-making model that utilises self-organising capabilities of the informal communities and identifies a natural flow model of knowledge creation, disruption and utilisation. However the argument from nature of many complexity thinkers is rejected given the human capability to create order and predictability through collective and individual acts of freewill. Knowledge is seen paradoxically, as both a thing and a flow requiring diverse management approaches. The Cynefin Centre Membership of the Centre, which focuses on action research in organisational complexity is open to individuals and to organisations. It focuses on high-participation action research projects seeking new insights into the nature of organisations and markets using models derived from sciences that recognise the inherent uncertainties of systems comprised of interacting agents. However, the Centre is not about attempting to apply physical or biological models to organisations wholesale without attention to the uniquely human capacities of free will, awareness and social responsibility. It is about engaging human organisational complexity in its many manifestations, including the ancient collective and emergent patterns of narrative, ritual, negotiation of identity and truth, self-representation and knowledge exchange. The Centre is not about consultants or academics conducting multiple interviews or observations and deriving static hypothesises and models based on their outside "expertise". It is about creating focused dynamic interactions between traditional and unexpected sources of knowledge to enable the emergence of new meaning and insight. The Centre is based on a model of networked intelligence, creating a broad and loosely structured coalition of academics, industrial and governmental organisations to create new insight and understanding for its members into the complexity of managing in a new age of uncertainty. The basis of all Centre programmes is to look at any issue from multiple new perspectives and to facilitate problem solving through multiple interactions among programme participants. Programmes run on a national, international and regional basis and range from investigation of seemingly impossible or intractable problems to pragmatic early entry into new methods and tools such as narrative databases, social network stimulation and asymmetric threat response.

Regulation by Adaptive Management - is it Possible?

Abstract: The history of environmental law provides as good an example as any other field in regulatory law of how successful prescriptive regulation has been at meeting public policy objectives, but how difficult it will be to extend that experience much farther into the future. For decades so-called "command and control" regulation has picked the low-hanging fruit - in environmental law, for example, it has gone after emissions from smokestacks and discharge pipes, disposal of wastes in landfills, transportation of hazardous chemicals, and similar discrete, easily-identified sources of environmental harm.The future that lies ahead for most fields of regulation, however, is filled with problems of unwieldy dimensions and intractable causes. In environmental law, for example, the problems that are foremost to many observers include the invasion of non-native species into ecosystems, the depletion of estuarine resources by fertilizer runoff from countless agricultural operations hundreds to thousands of miles inland, the degradation of habitat from suburban "sprawl," and the evidence of climate change, which itself is irrefutable even if its causes are not. In this brand of environmental policy challenge, there are no discrete sources or clearly traced lines of causation. Rather, problems such as these exhibit the hallmark characteristics of complex adaptive systems. Their behavior emanates from a multitude of diverse, dispersed sources responding to co-evolving interactions, feedback loops, and nonlinear cause-and-effect properties. They are, to put it simply, excruciatingly hard for researchers to understand, and thus even harder for law to wrestle under control.This kind of policy problem thus confounds the prescriptive regulation model, because there are no readily available targets for the prescriptions and, even worse, we have no idea what response the system would exhibit to any particular command. Even if legislatures armed them with unlimited powers, administrative agencies could not simply command away invasive species, or farm runoff, or new rooftops, or global climate change. There is almost universal agreement that problems of this sort demand new approaches to regulation. Agencies thus have experimented with many alternatives to prescriptive regulation, including market-based programs, information-based programs, negotiated project-specific licensing, ecosystem-scaled land management programs, multi-party collaborative planning efforts, and government-private quasi-partnerships.To take advantage of their inherently adaptive qualities, however, these regulatory instruments must themselves be managed adaptively. It will do no good, in other words, to hand an agency a market-based program only to have the agency administer the program through centralized decision making. Nor is likely that the now dominant public land use theme of ecosystem management, which focuses on landscapes and ecosystem dynamics rather than discrete media or species, can successfully be implemented through decision making that relies on reductionist, linear models of how "parts" of ecosystems function. Not only must the instruments of regulation be transformed, therefore, but so too must the methods of regulation. Hence it is almost universally the case that advocates of regulatory innovations also advance the method of implementation known generally as adaptive management.The voluminous literature that exists today to describe what adaptive management means traces its roots to Professor C.S. Holling's seminal work in the field, "Adaptive Environmental Assessment and Management." Although almost 30 years have passed since he and his colleagues first described the adaptive management methodology, no work on the topic since then has improved on their core theory, and far be it from me to try where so many others have failed. Its essence is an iterative, incremental decision-making process built around a continuous process of monitoring the effects of decisions and adjusting decisions accordingly. It is, in other words, far more suited to the needs of future regulatory challenges than is prescriptive regulation.On the one hand, nothing about this is startlingly new or unusual as a general means of decision making - businesses implement adaptive management all the time, or they perish. Ironically, however, the puzzle is whether administrative agencies can behave adaptively and survive. As a leading proponent of adaptive management once observed, agencies "have not often been rewarded for flexibility, openness, and their willingness to experiment, monitor, and adapt." The deterrents to these core attributes of adaptive management come from three fronts: legislatures, the public, and the courts. In short, in order for adaptive management to flourish in administrative agencies, legislatures must empower them to do it, interest groups must let them do it, and the courts must resist the temptation to second-guess when they do in fact do it. The track record of administrative law from the era of prescriptive regulation suggests that none of these three institutional constraints will yield easily. Quite simply, there is good reason to doubt whether regulation by adaptive management is possible without substantial change in the administrative law system.In this Article I explore the concern just raised using the example of the Endangered Species Act's (ESA) Habitat Conservation Plan (HCP) program. Part I of this Article briefly provides the general background of interest - the potential for collision between adaptive management theory and administrative law institutions - to more firmly illustrate the nature of the problem. Part II then grounds the topic in a real-world context through the story of the HCP program. Although Congress appears to have hoped that the HCP program would promote adaptive management of imperiled species, its delegation of authority to FWS was an imprint of prescriptive regulation. Nevertheless, during the 1990s, while Congress was functionally inert on reform of the ESA despite much reform rhetoric, FWS essentially reinvented the program through administrative reform in the mold of adaptive management. Soon, however, citizen groups representing environmental protection interests responded with vociferous and litigious opposition to reform, ultimately bearing down on the agency's injection of "flexibility" in the program through repeated lawsuits challenging HCP permits. With few (but notable) exceptions, the courts were all too quick to pounce as well, stifling the agency's willingness to experiment. The result could be one of the tragedies of environmental and administrative law - today, the HCP program increasingly resembles a plain vanilla regulatory program, functional on that level but increasingly stripped of its once promising adaptive qualities. One can only hope this is not a harbinger for the future of adaptive management in general, for if it is, regulation by adaptive management will not be possible.

Use of complex adaptive systems metaphor to achieve professional and organizational change

Abstract: Aim. This paper uses the experiences of a programme designed to bring about change in performance of public health nurses (health visitors and school nurses) in an inner city primary care trust, to explore the issues of professional and organizational change in health care organizations. Background. The United Kingdom government has given increasing emphasis to programmes of modernization within the National Health Service. A central facet of this policy shift has been an expectation of behaviour and practice change by health care professionals. Methods. Change was brought about through use of a Complex Adaptive Systems approach. This enabled change to be seen as an inclusive, evolving and unpredictable process rather one which is linear and mechanistic. The paper examines in detail how the use of concepts and metaphors associated with Complex Adaptive Systems influenced the development of the programme, its implementation and outcomes. Findings. The programme resulted in extensive change in professional behaviour, service delivery and transformational change in the organizational structures and processes of the employing organization. This gave greater opportunities for experimentation and innovation, leading to new developments in service delivery, but also meant higher levels of uncertainty, responsibility, decision-making and risk management for practitioners. Conclusion. Using a Complex Adaptive Systems approach was helpful for developing alternative views of change and for understanding why and how some aspects of change were more successful than others. Its use encouraged the confrontation of some long-standing assumptions about change and service delivery patterns in the National Health Service, and the process exposed challenging tensions within the Service. The consequent destabilising of organizational and professional norms resulted in considerable emotional impacts for practitioners, an area which was found to be underplayed within the Complex Adaptive Systems literature. A Complex Adaptive Systems approach can support change, in particular a recognition and understanding of the emergence of unexpected structures, patterns and processes. The approach can support nurses to change their behaviour and innovate, but requires high levels of accountability, individual and professional creativity.

Toward a normative theory of normative marketing theory

Abstract: We show how different approaches to developing marketing strategies depend on the type of environment a firm faces, where environments are distinguished in terms of their systems properties rather than their content. Particular emphasis is given to turbulent environments in which outcomes are not a priori predictable and are not traceable to individual firm actions and we show that, in these conditions, the relevant unit of competitive response and understanding is no longer the individual firm but the network of relations comprising interdependent, interacting firms. Networks of relations are complex adaptive systems that are more ‘intelligent’ than the individual firms that comprise them and are capable of comprehending and responding to more complex and turbulent environments. Yet they are co-produced by the patterns of actions and interactions of the firms involved. The creation and accessing of such distributed intelligence cannot be centrally directed, as this necessarily limits it. Instead managers...

Convening Stakeholder Networks A New Way of Thinking, Being and Engaging

Abstract: A growing number of companies are convening stakeholder networks to address complex sustainability and corporate responsibility issues. The role of network convenor is new for most companies, and it involves different ways of thinking, being and engaging beyond the more traditional approaches to managing bilateral stakeholder relationships. In this paper we describe how three companies established successful networks and then explore the mind-set, skill sets and engagement processes that are required to build and sustain multi-stakeholder networks. The paper draws on theory and research related to complex adaptive systems, collective learning and whole-system change. l Stakeholder l Engagement l Networks l Collective learning l Whole system change

Evaluating Responses in Complex Adaptive Systems: Insights on Water Management from the Southern African Millennium Ecosystem Assessment (SAfMA)

Abstract: Ecosystem services are embedded in complex adaptive systems. These systems are riddled with nonlinearities, uncertainties, and surprises, and are made increasingly complex by the many human responses to problems or changes arising within them. In this paper we attempt to determine whether there are certain factors that characterize effective responses in complex systems. We construct a framework for response evaluation with three interconnected scopes or spatial and temporal domains: the scope of an impact, the scope of the awareness of the impact, and the scope of the power or influence to respond. Drawing from the experience of the Southern African Millennium Ecosystem Assessment (SAfMA), we explore the applicability of this framework to the example of water management in southern Africa, where an ongoing paradigm shift in some areas has enabled a transition from supply-side to demand-side responses and the creation of new institutions to manage water across scales. We suggest that the most effective responses exhibit congruence between the impact, awareness, and power scopes; distribute impacts across space and time; expand response options; enhance social memory; and depend on power-distributing mechanisms. We conclude by stressing the need for sufficient flexibility to adapt responses to the specific, ever-evolving contexts in which they are implemented. Although our discussion focuses on water in southern Africa, we believe that the framework has broad applicability to a range of complex systems and places.

Agent Based Modeling and Adaptation to Climate Change

Abstract: Summary Agent based modeling is a technique for simulating complex systems that allows the modeler to investigate both the potential for and the sources of emergent properties: behaviors of the system quite different from the behavior of any of the elements within it. Problems well suited to investigation by agent based models are those with many people solving a similar problem where their individual responses to the problem influence the choices that others make, where new technologies may emerge to assist them solve the problem, and where social dilemmas exist. These features are inherent in many problems of adaptation to climate change. Agent based modeling can not predict the future of a complex adaptive system – no method of modeling can – but it can offer insights into the relationship between features of current systems and the range of possible future adaptations that will be likely in response to climate change. Zusammenfassung Die agentenbasierte Modellierung als Methode der Simulation komplexe...

Computational ecology: from the complex to the simple and back.

Abstract: In 1958, when ecology was a young science and mathematical models for ecological systems were in their infancy, Elton [1] wrote of the “neolithic days of animal ecology, that is to say about twenty-five years ago.” Acknowledging the influence of Lotka and Volterra, he noted, “Being mathematicians, they did not attempt to contemplate a whole food-chain with all the complications of five stages. They took two: a predator and its prey.” Today, in the era of computational ecological modeling, deterministic systems for two variables—and even a whole food chain—appear like simple idealizations well removed from the complexity of nature. We now consider predator–prey interactions as “consumer–resource” interactions embedded within the large ecological networks that underlie biodiversity (Figure 1) [2]. Consequently, the scale of the problems we model has grown to reflect the world as we now need to observe it. For example, the interplay between ecosystem dynamics and the physical environment that influences global change occurs over a tremendous range of spatial and organizational scales (e.g., [3]). Similarly, the population dynamics of the transmission of infectious diseases often involve spatial or social networks with large numbers of individuals, but the interactions of each individual involve only a subset of the network and can span from local to global distances (e.g., [4–6]). Figure 1 The Network of Trophic Interactions for Little Rock Lake, Wisconsin These examples illustrate the current view of ecological systems as complex adaptive systems [7,8]. Complex adaptive systems are distinguished not only by the multiplicity of components within them, but also by interactions that can be local or distributed among these components and whose rates vary as nonlinear functions of the state of the system itself. One obvious role of computation in the science of complex systems is simply one of synthesis: to reconstruct the whole from the parts as we learn more and more about the components and their interactions. There are obvious limitations to this approach, evident in the famous image of those imperial cartographers who produced a map of the empire of the same size as the empire itself [9]. I argue here that an alternative and more useful role of computation is to address questions on the relationship between dynamics at different temporal, spatial, and organizational scales, that is, to address the importance of variability at small, local scales to the dynamics of aggregated quantities measured at large, global scales. If small-scale “details” matter, we need to ask how much complexity we need to incorporate into large-scale models if we seek to both understand and predict the dynamics of global quantities. Is it possible to incorporate the effect of small-scale variability without resorting to the “brute force” approach of using higher and higher resolution? I start with examples from theoretical ecology that illustrate problems and approaches related to these scaling questions; I then present more specific examples related to global change and ecosystem dynamics, and end with a series of related problems on the dynamics of large food webs, the ultimate networks of ecological interactions.

A mechanism for pockets of predictability in complex adaptive systems

Abstract: We document a mechanism operating in complex adaptive systems leading to dynamical pockets of predictability ("prediction days"), in which agents collectively take predetermined courses of action, transiently decoupled from past history. We demonstrate and test it out of sample on synthetic minority and majority games as well as on real financial time series. The surprising large frequency of these prediction days implies a collective organization of agents and of their strategies which condense into transitional herding regimes.

The complexity of concept mapping for policy analysis

Abstract: Concept mapping is a participatory mixed methodology that enables diverse participant groups to develop shared conceptual frameworks that can be used in a variety of policy contexts to identify or encourage complexity, and the adaptive emergent properties associated with it. The method is consistent with an evolving paradigm of complex adaptive systems thinking and helps groups address complexity in several ways: it is inductive, allowing shared meaning to emerge; it is based on a simple set of rules (operations) that generate complex patterns and results; it engages diverse agents throughout the process through a range of participation channels (synchronous or asynchronous web, face-to-face, etc.); the visual products the concept maps, pattern matches, action plots provide high-level representations of evolving thinking; the results are generative, encouraging shared meaning and organizational learning while preserving individuality and diversity; the maps themselves provide a framework that enables autonomous agents to align action with broader organizational or systems vision. The concept mapping process involves free listing, unstructured sorting and rating of ideas, and a sequence of statistical analyses (multidimensional scaling, hierarchical cluster analysis) that produce maps and other results that the participants then interpret. An example is provided of a web-based project that mapped the practical challenges that need to be addressed to encourage and support effective systems thinking and modeling in public health work. It is suggested that using concept mapping especially in combination with other types of human simulation provides a valuable addition to our methodological tools for studying complex human systems.

An Overview of Complex Adaptive Systems

Abstract: Almost every biological, economic and social system is a complex adaptive system (CAS). Mathematical and computer models are relevant to CAS. Some approaches to modeling CAS are given. Applications in vaccination and the immune system are studied. Mathematical topics motivated by CAS are discussed.

An Immuno Control Framework for Decentralized Mechatronic Control

Abstract: The Immune System is a complex adaptive system containing many details and many exceptions to established rules. Exceptions such as the suppression effect that causes T-cells to develop reversible aggressive and tolerant behaviors create difficulties for the study of immunology but also give hints to how artificial immune systems may be designed.

CAFISS: a complex adaptive framework for immune system simulation

Abstract: Currently most reported immune system simulations in literature involve the use of differential equations, genetic algorithm-based searching or simple cellular automata models. This limits the diversity in results obtained and thus provides fewer avenues for experimenting with behavioral responses of the immune system entities under exogenous stimulations. Complex adaptive systems (or CAS) by Holland provide a way of modeling natural systems with complex aggregation and nonlinear interactions to exhibit emergent behaviours. The immune system, being a powerful and flexible information processing system is particularly suited to being modeled using CAS. This paper describes a Java-based implementation of a framework for modeling the immune system, particularly Human Immunodeficiency Virus (or HIV) attack, using a CAS model. The credibility of the system is established through comparisons against available viral dynamics data. We show that it is feasible to achieve relatively accurate predictions of viral pathogenesis through agent-based discrete event simulations, the first steps towards improved automation of hypothesis verification.