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Showing papers on "Complex adaptive system published in 2003"


Journal ArticleDOI
TL;DR: The study of complex adaptive systems, a subset of nonlinear dynamical systems, has recently become a major focus of interdisciplinary research in the social and natural sciences, suggesting that emergence—the idea that complex global patterns with new properties can emerge from local interactions—could have a comparable impact.
Abstract: ▪ Abstract The study of complex adaptive systems, a subset of nonlinear dynamical systems, has recently become a major focus of interdisciplinary research in the social and natural sciences. Nonlinear systems are ubiquitous; as mathematician Stanislaw Ulam observed, to speak of “nonlinear science” is like calling zoology the study of “nonelephant animals” (quoted in Campbell et al. 1985, p. 374). The initial phase of research on nonlinear systems focused on deterministic chaos, but more recent studies have investigated the properties of self-organizing systems or anti-chaos. For mathematicians and physicists, the biggest surprise is that complexity lurks within extremely simple systems. For biologists, it is the idea that natural selection is not the sole source of order in the biological world. In the social sciences, it is suggested that emergence—the idea that complex global patterns with new properties can emerge from local interactions—could have a comparable impact.

597 citations


10 Feb 2003
TL;DR: This chapter identifies a series of key differences between the complexity science and established theoretical approaches to studying health organizations, based on the ways in which time, space, and constructs are framed.
Abstract: From its roots in physics, mathematics, and biology, the study of complexity science, or complex adaptive systems, has expanded into the domain of organizations and systems of organizations. Complexity science is useful for studying the evolution of complex organizations -entities with multiple, diverse, interconnected elements. Evolution of complex organizations often is accompanied by feedback effects, nonlinearity, and other conditions that add to the complexity of existing organizations and the unpredictability of the emergence of new entities. Health care organizations are an ideal setting for the application of complexity science due to the diversity of organizational forms and interactions among organizations that are evolving. Too, complexity science can benefit from attention to the world’s most complex human organizations. Organizations within and across the health care sector are increasingly interdependent. Not only are new, highly powerful and diverse organizational forms being created, but also the restructuring has occurred within very short periods of time. In this chapter, we review the basic tenets of complexity science. We identify a series of key differences between the complexity science and established theoretical approaches to studying health organizations, based on the ways in which time, space, and constructs are framed. The contrasting perspectives are demonstrated using two case examples drawn from healthcare innovation and healthcare integrated systems research. Complexity science broadens and deepens the scope of inquiry into health care organizations, expands corresponding methods of research, and increases the ability of theory to generate valid research on complex organizational forms. Formatted

330 citations


Journal ArticleDOI
TL;DR: In this paper, the authors consider three cases of water policy making in California, including the San Francisco Estuary Project, the CALFED Bay-Delta Program and the Sacramento Area Water Forum.
Abstract: Collaborative policy making has become increasingly significant in environmental management, but it is often evaluated by whether or not agreement is reached and implemented. The most important outcomes of such policy dialogues are often invisible or undervalued when seen through the lens of a traditional, modernist paradigm of government and accountability. These dialogues represent a new paradigm of governance that can be best understood in the light of a complex adaptive system model of society. From this perspective collaborative policy making is a way of making a system more flexible, adaptive and intelligent. The authors document such outcomes in three cases of water policy making in California, including the San Francisco Estuary Project, the CALFED Bay-Delta Program and the Sacramento Area Water Forum. The outcomes include social and political capital, agreed-on information, the end of stalemates, high-quality agreements, learning and change, innovation and new practices involving networks and fle...

330 citations


Journal ArticleDOI
TL;DR: Agent-based computational economics (ACE) as mentioned in this paper is the computational study of economies modeled as evolving systems of autonomous interacting agents, which is a specialization to economics of the basic complex adaptive systems paradigm.

309 citations


Journal ArticleDOI
TL;DR: It will be argued that most learning theories rest on a sender‐receiver model of knowledge transmission and this affects how people learn within innovation projects and a complex adaptive approach offers an alternative perspective from which one can evaluate and analyze learning and innovation processes.
Abstract: Innovation is the lifeblood of companies, while simultaneously being one of the most difficult and elusive processes to manage. Failure rates are high – varying between six out of ten to nine out of ten – while the need to innovate is high. Departing from a real‐life case of a company, Sara Lee/Douwe Egberts, that has set learning within and from innovation projects high on the agenda, the main ideas about learning and innovation will be unfolded in the course of this article. It will be argued that most learning theories rest on a sender‐receiver model of knowledge transmission and this affects how people learn within innovation projects. A complex adaptive approach offers an alternative perspective from which one can evaluate and analyze learning and innovation processes. The most important characteristics of complex adaptive systems are non‐linearity, dynamic behavior, emergence and self‐organization. The implications of these phenomena for learning in innovation projects will be explained. The article finalizes with the preliminary findings of a multi‐agent simulation model, which explores what the underlying forces are beneath learning in innovation projects.

208 citations


Journal ArticleDOI
TL;DR: This work presents an Iterated Learning Model of the emergence of compositionality, a fundamental structural property of language, and shows that the poverty of the stimulus available to language learners leads to a pressure for linguistic structure.
Abstract: Language arises from the interaction of three complex adaptive systems — biological evolution, learning, and culture. We focus here on cultural evolution, and present an Iterated Learning Model of the emergence of compositionality, a fundamental structural property of language. Our main result is to show that the poverty of the stimulus available to language learners leads to a pressure for linguistic structure. When there is a bottleneck on cultural transmission, only a language which is generalizable from sparse input data is stable. Language itself evolves on a cultural time-scale, and compositionality is language's adaptation to stimulus poverty.

150 citations


Proceedings ArticleDOI
09 Jun 2003
TL;DR: An approach for the design of complex adaptive systems, based on adaptive multi-agent systems and emergence, which gives local agent design criteria so as to enable the emergence of an organization within the system and thus, of the global function of the system.
Abstract: In this paper, we present an approach for the design of complex adaptive systems, based on adaptive multi-agent systems and emergence. We expound the AMAS theory (Adaptive Multi-Agent Systems) and its technical working. This theory gives local agent design criteria so as to enable the emergence of an organization within the system and thus, of the global function of the system. We also present the theorem of functional adequacy witch ensures that a cooperative self organizing system performs a suitable work. Applications of this theory in the multi-agent system framework led us to define the architecture and a general algorithm for cooperative agents. The originality of our approach lies in the very generic manner our re-organization rules work and that they are completely independent from the function the system has to compute.

138 citations


Journal ArticleDOI
TL;DR: The authors expand the traditional view of surprise with a complexity perspective that makes it possible to ask new questions and to consider new ways of understanding the world around us.
Abstract: Surprise can emanate from two sources: lack of sufficient information or knowledge and the basic dynamics of complex adaptive systems. The authors expand the traditional view of surprise with a complexity perspective that makes it possible to ask new questions and to consider new ways of understanding the world around us. They discuss creativity and learning as two strategies for capitalizing on the surprises that confront organizations.

125 citations


Journal ArticleDOI
TL;DR: A specific model based on the evolutionary processes of variation, selection, retention and struggle, coupled with fitness landscape theory is presented, and a model called the strategy configuration chain is presented to illustrate this strategic process.
Abstract: There are systems methods and evolutionary processes that can help organisations understand the innovative patterns and competitive mechanisms that influence the creation, management and exploitation of technology. This paper presents a specific model based on the evolutionary processes of variation, selection, retention and struggle, coupled with fitness landscape theory. This latter concept is a complex adaptive systems theory that has attained recognition as an approach for visually mapping the strategic options an evolving system could pursue. The relevance and utility of fitness landscape theory to the strategic management of technology is explored, and a definition and model of technological fitness provided. The complex adaptive systems perspective adopted by this paper, views organisations as evolving systems that formulate strategies by classifying, selecting, adopting and exploiting various combinations of technological capabilities. A model called the strategy configuration chain is presented to illustrate this strategic process.

89 citations


Book ChapterDOI
TL;DR: It is argued that scaling issues are not only crucial from the standpoint of basic science, but also in many applied issues, and tools for detecting and dealing with multiple scales, both spatial and temporal are discussed.
Abstract: We review various aspects of the notion of scale applied to natural systems, in particular complex adaptive systems. We argue that scaling issues are not only crucial from the standpoint of basic science, but also in many applied issues, and discuss tools for detecting and dealing with multiple scales, both spatial and temporal. We also suggest that the techniques of statistical mechanics, which have been successful in describing many emergent patterns in physical systems, can also prove useful in the study of complex adaptive systems.

83 citations


Journal ArticleDOI
TL;DR: It is determined that the five attributes suggested by CAS to facilitate organizational learning were present in the innovative companies, which had the most organizational learning facilitating factors, and three of them were onlypresent in the company with the highest performance and the most innovative approach.
Abstract: The importance of the factors that facilitate organizational learning have traditionally been outlined in the literature. However, there is no agreement about what the essential facilitating factors are, as each author emphasizes different features. Complexity science is increasingly being used by researchers and practitioners to improve their understanding of organizations. This exploratory study tries to determine the essential facilitating factors for organizational learning, and demonstrate the importance of the ideas from complex adaptive systems (CAS) to it. In order to do this, we put forward a comparative case study of four heterogeneous companies from the Spanish ceramic tile sector in which we analyzed the facilitating factors for organizational learning, by relating them with ideas from CAS. As a result, we determined that the five attributes suggested by CAS to facilitate organizational learning were present in the innovative companies, which had the most organizational learning facilitating factors, and three of them were only present in the company with the highest performance and the most innovative approach: individuals’ relationship with the environment; cultural diversity; and state of equilibrium between formal and informal structures. Copyright # 2003 John Wiley & Sons, Ltd.

Journal ArticleDOI
TL;DR: In this article, the authors pay attention to organized complexity, especially to complex adaptive systems and their emergence from double interacts between individuals, and elaborates on the idea of reflexive actors in social systems, faces of knowledge, and the interplay between design-in-the-large and design in the small.
Abstract: The science of design deals with synthesis. It is both the world of engineering and the world of social construction of reality,as for example in education, policy making,or ganization, and management. Science is concerned with analysis and decomposition. Engineering and technology pay attention to natural objects: objects that refer to matter. Their claims about material artifacts are universal and therefore context independent. Constructionism deals with objects that are established by our practices. Both branches of the science of design emphasize the building metaphor of assembling parts,the systematic arrangements of elements,whic h become part of a whole. In this article,the author pays attention to organized complexity, especially to complex adaptive systems and their emergence from double interacts between individuals. The author elaborates on the idea of reflexive actors in social systems,faces of knowledge, and the interplay between design-in-the-large and design-in-the-small.

Journal Article
TL;DR: The paper provides an introduction to agent-based modelling and simulation of social processes and what one can and what cannot expect from such models, particularly when they are applied to social-scientific investigation.
Abstract: The paper provides an introduction to agent-based modelling and simulation of social processes. Reader is introduced to the worldview underlying agent-based models, some basic terminology, basic properties of agent-based models, as well as to what one can and what cannot expect from such models, particularly when they are applied to social-scientific investigation. Special attention is given to the issues of validation.

Journal ArticleDOI
TL;DR: In this article, it is argued that, because of the characteristics of economic systems, an ex-post analysis is more appropriate, which describes the emergence of such systems' properties, and which sees policy as a social steering mechanism.

Journal ArticleDOI
TL;DR: In this article, the authors discuss the role of complex adaptive systems in public service systems and propose a role for complex adaptive system theory in the context of public service system understanding and management.
Abstract: (2003). Understanding Public Service Systems: Is There a Role for Complex Adaptive Systems Theory? Emergence: Vol. 5, No. 4, pp. 57-85.

Journal ArticleDOI
TL;DR: A model of how close analysis of discursive processes between individuals (high-resolution), which occur simultaneously across a human system (broadband), dynamically evolve is proposed and the tentative suggestion that discourse may evolve to the “edge of chaos” is made.
Abstract: Numerous researchers and practitioners have turned to complexity science to better understand human systems. Simulation can be used to observe how the microlevel actions of many human agents create emergent structures and novel behavior in complex adaptive systems. In such simulations, communication between human agents is often modeled simply as message passing, where a message or text may transfer data, trigger action, or inform context. Human communication involves more than the transmission of texts and messages, however. Such a perspective is likely to limit the effectiveness and insight that we can gain from simulations, and complexity science itself. In this paper, we propose a model of how close analysis of discursive processes between individuals (high-resolution), which occur simultaneously across a human system (broadband), dynamically evolve. We propose six different processes that describe how evolutionary variation can occur in texts-recontextualization, pruning, chunking, merging, appropriation, and mutation. These process models can facilitate the simulation of high-resolution, broadband discourse processes, and can aid in the analysis of data from such processes. Examples are used to illustrate each process. We make the tentative suggestion that discourse may evolve to the "edge of chaos." We conclude with a discussion concerning how high-resolution, broadband discourse data could actually be collected.

Book ChapterDOI
01 Jan 2003
TL;DR: The construction and performance of a novel immune-based learning algorithm is explored whose distributed, dynamic and adaptive nature offers many potential advantages over more traditional models.
Abstract: The human immune system is a complex adaptive system which has provided inspiration for a range of innovative problem solving techniques in areas such as computer security, knowledge management and information retrieval. In this paper the construction and performance of a novel immune-based learning algorithm is explored whose distributed, dynamic and adaptive nature offers many potential advantages over more traditional models. Through a process of cooperative coevolution a classifier is generated which consists of a set of detectors whose local dynamics enable the system as a whole to group positive and negative examples of a concept. The immune-based learning algorithm is first validated on a standard dataset. Then, combined with an HTML feature extractor, it is tested on a web-based document classification task and found to outperform traditional classification paradigms. Further applications in content filtering, recommendation systems and user profile generation are also directly relevant to the work presented.

Journal ArticleDOI
TL;DR: A number of simulation techniques may be applied to strategy and decision-making, ranging from static equilibrium models through to agent-based approaches which can incorporate evolution and learning.
Abstract: A number of simulation techniques may be applied to strategy and decision-making. In using such techniques it is important to understand the role of models, in the context of the wider decision-making process which is largely a communal and political process; the choice of a particular approach or technique depends not only on the type of decision being considered, but also on the stage of the decision-making process (which in turn affects the type of problem under consideration). Models which take into account the dynamics of a system can give insight into possible outcomes and indications of unintended consequences. Such models may not necessarily reproduce all aspects of a complex adaptive system; there is a continuum of modelling techniques, ranging from static equilibrium models through to agent-based approaches which can incorporate evolution and learning. The trade-offs between alternative approaches is discussed.

Book ChapterDOI
01 Jan 2003
TL;DR: The Intelligent Complex Adaptive System (ICAS) as discussed by the authors is a model for an organization that will enter into a symbiotic relationship with its cooperative enterprise, virtual alliances and external environment, while simultaneously retaining unity of purpose and effective identification and selection of incoming threats and opportunities.
Abstract: In response to an environment of rapid change, increasing complexity and great uncertainty, the organization of the future must become an adaptive organic business. The intelligent complex adaptive system (ICAS) serves as a model for this organization that will enter into a symbiotic relationship with its cooperative enterprise, virtual alliances and external environment, while simultaneously retaining unity of purpose and effective identification and selection of incoming threats and opportunities. Eight characteristics, emergent in nature, are needed to succeed in the forthcoming, highly competitive, complex world where perceiving, interpreting and responding effectively become complicated and challenging in and of themselves. The characteristics needed to meet this challenge are presented and discussed. They are: organizational intelligence, unity and shared purpose, optimum complexity, selectivity, knowledge centricity, flow, permeable boundaries and multi-dimensionality. How these are brought into reality is the challenge of every manager and leader of the future.

Journal ArticleDOI
TL;DR: This article argued that the prevailing empiricist approach to the management sciences should be abandoned and consumer markets would be better regarded as complex adaptive systems and provided suggestions for developing future thinking on this topic.
Abstract: This paper advocates a change in how the research industry understands the consumer. The prevailing empiricist approach to the management sciences should be abandoned and consumer markets would be better regarded as complex adaptive systems. The paper concludes by providing suggestions for developing future thinking on this topic. This paper was joint winner of the Best New Thinking award at the 2003 Market Research Society conference.


Book ChapterDOI
01 Jan 2003
TL;DR: The system proposed can best be described as an intelligent complex adaptive system (ICAS), which builds on the currently anticipated knowledge organization to become a living system composed of living subsystems that combine, interact, and co-evolve to provide the capabilities of an advanced, intelligent technosociological adaptive enterprise.
Abstract: As we begin to understand and hopefully anticipate the behavior of the current and future environment, it becomes clear that neither the classic bureaucratic nor the current popular matrix and flat organizations will provide the unity, complexity, and selectivity necessary for survival. A different approach is needed to create an organizational system that can enter into a symbiotic relationship with other organizations within its enterprise and with the external environment while retaining its own unity of purpose and selectivity of incoming threats and opportunities, i.e., turning the living system metaphor into a reality. This organization builds on the currently anticipated knowledge organization to become a living system composed of living subsystems that combine, interact, and co-evolve to provide the capabilities of an advanced, intelligent technosociological adaptive enterprise. The system we propose can best be described as an intelligent complex adaptive system (ICAS).

Posted Content
TL;DR: In this article, it is argued that, because of the characteristics of economic systems, an ex-post analysis is more appropriate, which describes the emergence of such systems' properties, and which sees policy as a social steering mechanism.
Abstract: Economies are open complex adaptive systems far from thermodynamic equilibrium, and neo-classical environmental economics seems not to be the best way to describe the behaviour of such systems. Standard econometric analysis (i.e. time series) takes a deterministic and predictive approach, which encourages the search for predictive policy to ‘correct’ environmental problems. Rather, it seems that, because of the characteristics of economic systems, an ex-post analysis is more appropriate, which describes the emergence of such systems’ properties, and which sees policy as a social steering mechanism. With this background, some of the recent empirical work published in the field of ecological economics that follows the approach defended here is presented. Finally, the conclusion is reached that a predictive use of econometrics (i.e. time series analysis) in ecological economics should be limited to cases in which uncertainty decreases, which is not the normal situation when analysing the evolution of economic systems. However, that does not mean we should not use empirical analysis. On the contrary, this is to be encouraged, but from a structural and ex-post point of view.

Book ChapterDOI
01 Jan 2003
TL;DR: In this article, a simple technique to model and analyze value networks is demonstrated by examples, which illustrate that successful value networks operate on systems principles and an ethic of high integrity and trust.
Abstract: Organizations and business webs or networks behave as complex adaptive systems. Yet, many business modeling techniques fail to incorporate systems thinking or address the role of knowledge and intangibles in creating value. Intangibles such as knowledge play three important roles in business: as assets, as currencies, and as deliverables. Reframing enterprises as value networks can reveal both tangible and intangible value creating activities. Value networks are webs of relationships that generate tangible and intangible value through complex dynamic exchanges between two or more individuals, groups, or organizations. A simple technique to model and analyze value networks is demonstrated by examples. These examples illustrate that successful value networks operate on systems principles and an ethic of high integrity and trust.


Proceedings ArticleDOI
Pathak1, Dilts1, Biswas1
01 Dec 2003
TL;DR: In this article, a multi-paradigm dynamic system simulator based on discrete time and discrete event formalism for simulating a supply chain as a complex adaptive system is presented.
Abstract: This paper introduces a multi-paradigm dynamic system simulator based on discrete time and discrete event formalism for simulating a supply chain as a complex adaptive system. Little is known about why such a diversity of supply chain structures exist. Simulating dynamic supply chain networks over extended periods using the multi-paradigm dynamic system simulator allows us to observe the emergence of different structures. The simulator is implemented using a software agent technology, where individual agents represent firms in a supply chain network. In this paper, we present an example scenario run on the simulator and the preliminary results that have been observed. This multi-paradigm tool provides a valuable investigation instrument for real life supply chain problems.

01 Jan 2003
TL;DR: In this paper, the authors discuss the emergence of complex thinking in organizations and propose a new way of thinking to recover the multidimensional character of the human being within the organization, in contrast to the unidimensional view, established by the Cartesian-Newtonian paradigm, which reduced man to a mere appendix of the machine.
Abstract: This article discusses the emergence of complex thinking in organizations. This new way of thinking recovers the multidimensional character of the human being within the organization, in opposition to the unidimensional view, established by the Cartesian-Newtonian paradigm, which reduced man to a mere appendix of the machine and someone who only takes orders. The emergent mental model is directly linked to an organizational form that can support it — the postindustrial management logic. Through a co-evolutionary perspective, it tries to establish a dynamic and dialectic relation between the micro and macro organizational contexts, which is only possible if companies become complex adaptive systems (CASs). Analyzing and understanding companies as CASs is basic for the renewal process in which the human being, considered as a determinant and privileged agent of change, is the architect and constructor of this future. The article intends to widen the discussion about the emergence of complex thinking, giving insights for the development of studies and researches on the application of CASs to organizations.

Journal ArticleDOI
TL;DR: The Information Revolution and the development of networks have produced phenomena such as the growing connection between elements that are often extremely different from one another (computers, people, even smart objects) that cannot be planned according to a top-down logic, but “emerge” from interactions between elements and therefore “from the bottom.”
Abstract: The concepts of complexity prevalent at various historical times have influenced the frames of mind with which organizations and the models of social planning and organizational design have been analyzed. During the Industrial Revolution, the model of organizational design derived from the conceptual model of the machine. In this model, the concept of the hierarchical control of functions prevailed. The consequent approach was top-down thinking. Much of the organizational theory of the twentieth century was based on determinism, reductionism, and equilibrium as key principles. If an organization is conceived of as a machine, the control of the organization is obtained through a reduction in its complexity; that is, in the states of the machine or its variety. The Information Revolution and the development of networks have produced phenomena such as the growing connection between elements that are often extremely different from one another (computers, people, even smart objects). This has led to phenomena that cannot be planned according to a top-down logic, but, on the contrary, “emerge” from interactions between elements and therefore “from the bottom.” The approach most suitable for analyzing these phenomena is bottom-up thinking. If in the past the world could be represented as a machine, today it is represented as a network and increasingly as an ecosystem. The Internet, for example, can be considered not only as a technological infrastructure and a social practice, but also as a new way of thinking related to the concepts of freedom of access and diffusion of knowledge. Furthermore, the

01 Jan 2003
TL;DR: Methods and case studies of approaching architectural design and fabrication utilizing Complex Adaptive Systems utilizing CASs are presented, which provide frameworks for managing large numbers of elements and their inter-relationships.
Abstract: This paper presents methods and case studies of approaching architectural design and fabrication utilizing Complex Adaptive Systems (CASs). The case studies and observations described here are findings from a continuing body of research investigating applications of computational systems to architectural practice. CASs are computational mechanisms from the computer science field of Artificial Life that provide frameworks for managing large numbers of elements and their inter-relationships. The ability of the CASs to handle complexity at a scale unavailable through non-digital means provides new ways of approaching architectural design, fabrication, and practice.

Journal ArticleDOI
TL;DR: By describing a practical application of the ideas about complex adaptive systems to newborn care, the report aims to help pediatricians prepare to lead in this field and give physicians insights to develop and modify health care systems.
Abstract: Arecent Institute of Medicine (IOM) report describes a chasm in health care quality that we must cross for patients to receive better care in the 21st century. The report calls for a “systems approach,” drawing on the rapid evolution of knowledge about complex adaptive systems.1 Understanding how complex adaptive systems work can give physicians insights to develop and modify health care systems. By describing a practical application of the ideas about complex adaptive systems to newborn care, we aim to help pediatricians prepare to lead in this field. A complex adaptive system is a collection of individual agents who have the freedom to act, but because the agents are interconnected, action by any agent changes the context for other agents in the system. One familiar example is the buyers in a stock market. In the last century, it was usual to see organizations as mechanical systems: in mechanical systems, if we know what each part of a system does, we can predict perfectly how the whole will respond in a given situation. This is obviously not true of the stock market. A complex adaptive system may display sudden unpredictable shifts in behavior caused by interactions among agents. An essential first step in improving the US health care system is to recognize that its member organizations and individuals, with sublevels nested within and interconnected to each other, make up a complex adaptive system. One of the key attributes of a complex adaptive system is that orderly behavior can emerge among many agents who are acting independently but who share a common drive. For instance, ants, driven to survive, create intricate buildings and foraging systems without any planning by a chief executive ant. So do humans. The citizens of New York City share a drive to eat; with no single individual …