scispace - formally typeset
Search or ask a question

Showing papers on "Complex adaptive system published in 2009"


Journal ArticleDOI
TL;DR: The Language as a Complex Adaptive System (LAS) approach as discussed by the authors is a model for language acquisition that is based on a complex adaptive system consisting of multiple agents (the speakers in the speech community) interacting with one another.
Abstract: Language has a fundamentally social function. Processes of human interaction along with domain-general cognitive processes shape the structure and knowledge of language. Recent research in the cognitive sciences has demonstrated that patterns of use strongly affect how language is acquired, is used, and changes. These processes are not independent of one another but are facets of the same complex adaptive system (CAS). Language as a CAS involves the following key features: The system consists of multiple agents (the speakers in the speech community) interacting with one another. The system is adaptive; that is, speakers’ behavior is based on their past interactions, and current and past interactions together feed forward into future behavior. A speaker's behavior is the consequence of competing factors ranging from perceptual constraints to social motivations. The structures of language emerge from interrelated patterns of experience, social interaction, and cognitive mechanisms. The CAS approach reveals commonalities in many areas of language research, including first and second language acquisition, historical linguistics, psycholinguistics, language evolution, and computational modeling.

723 citations


Journal ArticleDOI
TL;DR: This article presents a framework, transition management, for managing complex societal systems, based on key notions of complex systems theory, such as variation and selection, emergence, coevolution, and self-organization.
Abstract: textThis article presents a framework, transition management, for managing complex societal systems. The principal contribution of this article is to articulate the relationship between transition management and complex systems theory. A better understanding of the dynamics of complex, adaptive systems provides insight into the opportunities, limitations, and conditions under which it is possible to influence such systems. Transition management is based on key notions of complex systems theory, such as variation and selection, emergence, coevolution, and self-organization. It involves a cyclical process of phases at various scale levels: stimulating niche development at the micro level, finding new attractors at the macro level by developing a sustainability vision, creating diversity by setting out experiments, and selecting successful experiments that can be scaled up.

499 citations


Journal ArticleDOI
TL;DR: In this paper, the interplay among language, agent, and environment in the language acquisition process is discussed. But, despite the importance of individual-level variation in the characteristics and contextual circumstances of the learner/speaker, little is known about the interaction between language, environment, and agent.
Abstract: The notion of language as a complex adaptive system has been conceived within an agent-based framework, which highlights the significance of individual-level variation in the characteristics and contextual circumstances of the learner/speaker. Yet, in spite of this emphasis, currently we know relatively little about the interplay among language, agent, and environment in the language acquisition process, which highlights the need for further research in this area. This article is intended to pursue this agenda by discussing four key issues in this respect: (a) conceptualizing the agent, (b) conceptualizing the environment and its relationship to the agent, (c) operationalizing the dynamic relationship among language, agent, and environment, and (d) researching dynamic systems.

285 citations


Journal ArticleDOI
TL;DR: The credentials of the evidence-based policy movement appear to be increasingly subject to challenge based on research that has highlighted the limits on the use of evidence in policy making, and moves towards a more realistic position of evidence-informed policy making risk conflating prescription with description and undermining a normative vision of better policy making as discussed by the authors.
Abstract: The credentials of the evidence-based policy movement appear to be increasingly subject to challenge based on research that has highlighted the limits on the use of evidence in policy making However, moves towards a more ‘realistic’ position of evidence-informed policy making risk conflating prescription with description and undermining a normative vision of better policy making This article argues that we need to review the ideas that underpin our thinking about evidence-based policy making, and move beyond the territory of instrumental rationality to a position founded upon two intellectual pillars: our developing knowledge about complex adaptive systems; and ideas from a pragmatist philosophical position – especially those of John Dewey – about social scientific knowledge and its role in guiding action to address social problems This leads us to a conception of ‘intelligent policy making’ in which the notion of policy learning is central

238 citations



Journal ArticleDOI
TL;DR: Recent theoretical developments in the philosophy of complexity theory provide a basis for gaining insight into the proposition that multi-project management is neither an extension nor a scaled up version of single project management.

166 citations


Journal ArticleDOI
TL;DR: The idea of "learning from samples of one or fewer" of March, Sproull, and Tamuz provides strategies for research design that enables learning from meager evidence and research designs are likely to be more effective when they anticipate change, include tension, and capitalize on serendipity.
Abstract: Background Because health care organizations (HCOs) are complex adaptive systems (CASs), phenomena of interest often are dynamic and unfold in unpredictable ways, and unfolding events are often unique. Researchers of HCOs may recognize that the subject of their research is dynamic; however, their research designs may not take this into account. Researchers may also know that unfolding events are often unique, but their design may not have the capacity to obtain information from meager evidence. Purpose These two concerns led us to examine two ideas from organizational theory: (a) the ideas of K. E. Weick (1993) on organizational design as a verb and (b) the ideas of J. G. March, L. S. Sproull, and M. Tamuz (1991) on learning from samples of one or fewer. In this article, we applied these ideas to develop an enriched perspective of research design for studying CASs. Methodology/approach We conducted a theoretical analysis of organizations as CASs, identifying relevant characteristics for research designs. We then explored two ideas from organizational theory and discussed the implications for research designs. Findings Weick's idea of "design as a verb" helps in understanding dynamic and process-oriented research design. The idea of "learning from samples of one or fewer" of March, Sproull, and Tamuz provides strategies for research design that enables learning from meager evidence. When studying HCOs, research designs are likely to be more effective when they (a) anticipate change, (b) include tension, (c) capitalize on serendipity, and (d) use an "act-then-look" mind set. Implications for practice are discussed. Practice implications Practitioners who understand HCOs as CASs will be cautious in accepting findings from studies that treat HCOs mechanistically. They will consider the characteristics of CAS when evaluating the evidence base for practice. Practitioners can use the strategies proposed in this article to stimulate discussion with researchers seeking to conduct research in their HCO.

153 citations


Journal ArticleDOI
TL;DR: By observing the evolution of decision making by cooperating and defecting agents, this article offers testable propositions regarding relationship development and distributed nature of governance mechanisms for managing supply networks.
Abstract: In this article, we examine how the firms embedded in supply networks engage in decision making over time. The supply networks as a complex adaptive system are simulated using cellular automata (CA) through a dynamic evolution of cooperation (i.e., “voice” decision) and defection (i.e., “exit” decision) among supply network agents (i.e., firms). Simple local rules of interaction among firms generate complex patterns of cooperation and defection decisions in the supply network. The incentive schemes underlying decision making are derived through different configurations of the payoff-matrix based on the game theory argument. The prisoner's dilemma game allows capturing the localized decision-making process by rational agents, and the CA model allows the self-organizing outcome to emerge. By observing the evolution of decision making by cooperating and defecting agents, we offer testable propositions regarding relationship development and distributed nature of governance mechanisms for managing supply networks.

110 citations


Proceedings ArticleDOI
26 Jul 2009
TL;DR: In this article, the authors present the potentials and promises of Computational Intelligence (CI) to realize an intelligent smart grid, which is the successor of artificial intelligence and is the way of the future computing.
Abstract: The electric power grid is a complex adaptive system under semi-autonomous distributed control. It is spatially and temporally complex, non-convex, nonlinear and non-stationary with a lot of uncertainties. The integration of renewable energy such as wind farms, and plug-in hybrid and electric vehicles further adds complexity and challenges to the various controllers at all levels of the power grid. A lot of efforts have gone into the development of a smart grid to align the interests of the electric utilities, consumers and environmentalists. Advanced computational methods are required for planning and optimization, fast control of power system elements, processing of field data and coordination across the grid. Distributed and coordinated intelligence at all levels and across levels of the electric power grid — generation, transmission and distribution is inevitable if a true smart grid is to be reality. Computational intelligence (CI) is the study of adaptive mechanisms to enable or facilitate intelligent behavior in complex, uncertain and changing environments. These adaptive mechanisms include artificial and bio-inspired intelligence paradigms that exhibit an ability to learn or adapt to new situations, to generalize, abstract, discover and associate. The paradigms of CI mimic nature for solving complex problems. CI is successor of artificial intelligence and is the way of the future computing. This paper presents the potentials and promises of CI to realize an intelligent smart grid.

109 citations


Journal ArticleDOI
TL;DR: In this paper, the authors evaluate the Federal Response Plan (FRP), National Response Plan and National Response Framework (NRF) from the perspectives of interorganizational networks and complex adaptive systems.
Abstract: This article evaluates the Federal Response Plan (FRP), the National Response Plan (NRP), and the National Response Framework (NRF) from the perspectives of interorganizational networks and complex adaptive systems. The article uses the theoretical approach of complexity theory and dynamic network analysis to assess the relationships among organizations using the NRP/NRF as the structure that shapes their functional and organizational relationships. It also examines the applicability of concepts from complexity science for emergency and crisis management, to the evolution of NRP/NRF from the earlier FRP. The article uses the network analysis method in evaluating changes from FRP to NRF. The network analysis results demonstrate increases in complexity in the disaster and crises response and recovery plans over time.

105 citations


Journal ArticleDOI
01 Jan 2009
TL;DR: The results show that in the presence of non-linear and adaptive interaction, unintended and unpredictable outcomes might occur, and that knowledge sharing results from the complex interaction between employee behavior and organizational interventions.
Abstract: This paper explores knowledge sharing using an agent-based simulation model. Built using Repast, our application allows managers to simulate employee knowledge-sharing behaviors by making parametric assumptions on employee decision strategies and organizational interventions that affect identifiability, benefits, and costs. Our results show that in the presence of non-linear and adaptive interaction, unintended and unpredictable outcomes might occur, and that knowledge sharing results from the complex interaction between employee behavior and organizational interventions.

Journal Article
TL;DR: Bayesian network techniques are used to mine and model emergent relationships between local behaviors and global behaviors in social networks over time and show that temporal metrics are an extremely valuable new contribution to link prediction, and should be used in future applications.
Abstract: This article describes research into the discovery and modelling of emergent temporal phenomena in social networks. It summarizes experimental results that bring together two views in contemporary science: Bayesian analysis and link prediction, to enhance the current understanding of emergent temporal patterns in social network analysis (SNA), particularly in value creation through social connectedness-an important, and growing, discipline within management science. Traditional link prediction methods use the values of metrics in a graph to determine where new links are likely to arise, and little work has been done on analyzing long-term graph trends. We have found that existing graph generation models are unrealistic in their prediction, and can be complemented through the use of temporal metrics, in the study of some networks. To date, no temporal information has been used in link prediction research, thereby excluding valuable temporal trends that emerge in sociogram sequences and also lowering the accuracy of the link prediction. We extracted information from the Pussokram online dating network dataset, and 9,939 cases of each class were formed. Logistic regression in the Weka data mining system was used to perform link prediction. Our results show that temporal metrics are an extremely valuable new contribution to link prediction, and should be used in future applications. In addition to using metrics to measure the local behaviors of participants in social networks, we used Bayesian networks to model the interrelationships between the metrics as local behaviors and links forming between individuals as emergent behaviors (social complexity). We also explored how the metrics evolve over time using Dynamic Bayesian Networks (DBN). (ProQuest: ... denotes formulae omitted.) Introduction Social networks are complex systems that are characterized by high numbers of interconnected component entities, and a high degree of interaction between these entities. The interrelationships in such a network are dynamic and evolve over time. Temporal changes in social networks are difficult to understand and anticipate. The interrelationships between the component entities in a social network and its global behavior can be so numerous and mostly hidden, and can affect so many different entities throughout the social network that it becomes extremely difficult to comprehend. Complexity theory is ideally suited to study social networks. Complex adaptive systems theory is a branch of complexity theory that studies systems that consist of agents that are collectively able to evolve in response to environmental changes. The agents in such a system constantly act and react to the actions of other agents and events in the environment. A social network is a complex adaptive system, in which people are agents interacting with each other. In order to understand social complexity, the local behaviors ofthe participants must be understood, as well as how they act together and interact with the environment to form the whole. To model this, we use Bayesian network techniques to mine and model emergent relationships between local behaviors and global behaviors in social networks over time. Social Complexity The complex structure of any social organization can be thought of as a network of individuals/agents (Nohria 8c Eccles, 1992: 288; Lincoln, 1982), sometimes termed network actors, that operates and is operated on in an environment which itself is an environment of other distributed organizations (Van Wijk et al, 2003; Potgieter et al, 2006), and the actions of agents within the network are shaped and constrained because of their position and embeddedness in the network (Nohria, 1992). Complex adaptive systems theory, a branch of complexity theory that is well suited to study social networks, investigates systems that consist of agents that are collectively able to evolve in response to environmental changes (i. …


Book
21 May 2009
TL;DR: The Interactional Instinct argues that language is a cultural artifact that evolves as from a complex adaptive system through human interaction, and how nonlinear Complex Adaptive Systems (CAS) emerge from pattern formations out of chaos, analogising what happens with an emerging language.
Abstract: The Interactional Instinct: The Evolution and Acquisition of Language by Namhee Lee, Lisa Mikesell, Anna Dina L. Joaquin, Andrea Mates, & John Schumann. Oxford University Press, 2009. Reviewed by Bahiyyih L. Hardacre University of California, Los Angeles The currently more widely accepted idea that human language could have emerged and developed without the need for a specific gene for Universal Gram- mar (UG) is the major contribution of this book. Offering an evolutionary view on the neurobiology of language acquisition, it argues that language is a cultural artifact that evolves as from a complex adaptive system through human interaction. It claims that through the process of communicative exchanges, individuals organ- ize lexical items into structures, and if these are efficient, they are incorporated as part of this language’s “grammar”. This seems to be a highly efficient practice as it ensures that such newly created forms fit the cognitive and motor capacities of the human brain before being incorporated. In this perspective, language has evolved to fit the brain and not the other way around, as advocated by supporters of the UG perspective. This book is divided into chapters written by professors and PhD students in the Applied Linguistics department at the University of California, Los Angeles, who are all members of the Neurobiology of Language Research Group (NLRG) at the same institution. The book has in total 7 chapters and delivers the content in a very straightforward manner: chapter 1 describes what complex adaptive systems are in order to show how language is one; chapter 2 lists evidence for language emergence, describing the creation of pidgins and creoles; chapter 3 anticipates the implications of interaction and the use of language in real life settings, as op- posed to what traditional linguists have done so far in terms of describing language through prefabricated structures; chapter 4 describes interactional readiness in infant-caregiver interactions, associating apparently innate behavior in neonates and chemical rewards in a neurobiological level; chapter 5 reinforces the interactional instinct concept showing how the need for affiliation coincides with the ability to affiliate; and finally, chapters 6 and 7 describe the role of affect in first language acquisition and in cognition, respectively. The first chapter, “Grammar as a Complex Adaptive System”, begins by showing how Innatism can no longer be sustained by current findings in neuro- science, genetics and linguistics. The authors explain how nonlinear Complex Adaptive Systems (CAS) emerge from pattern formations out of chaos, analogi- cally describing what happens with an emerging language. In order to show how languages can be seen as CAS’s, the authors apply all the principles of CAS, i.e., aggregation, multi-strata of building blocks, local and random interactions, tagging, Issues in Applied Linguistics © 2009 Bahiyyih L. Hardacre ISSN 1050-4273 Vol. 17 No. 2, 163-165

Journal ArticleDOI
TL;DR: A tentative model for managing organizations as CAS – system management is presented and an inductive and interactive approach is used to identify the management principles in the case study.
Abstract: Purpose – The purpose of this paper is to explore the concept of complex adaptive systems (CAS) from the perspective of managing organizations, to describe and explore the management principles in a case study of an organization with unconventional ways of management and to present a tentative model for managing organizations as CAS – system management. There is a need for the development of knowledge, metaphors and language for management of the new forms of organizing, for example, value networks, which are evolving as a response to the increased demand for efficiency, flexibility and innovation.Design/methodology/approach – The frame of reference is based on a literature review of the area of CAS and an inductive and interactive approach is used to identify the management principles in the case study.Findings – A classification of the components of a CAS is suggested and described as properties of, and approaches for, managing CAS. The identified management principles in the case study are: a clearly f...

Posted Content
TL;DR: The simulations results reveal symmetrical trust levels between two trading agents, based on which the degree of trust relationship in each pair of trading agents as well as the resulting collaboration patterns in the entire supply network emerge.
Abstract: This paper models a supply network as a complex adaptive system (CAS), in which firms or agents interact with one another and adapt themselves. And it applies agent-based social simulation (ABSS), a research method of simulating social systems under the CAS paradigm, to observe emergent outcomes. The main purposes of this paper are to consider a social factor, trust, in modeling the agents' behavioral decision-makings and, through the simulation studies, to examine the intermediate self-organizing processes and the resulting macro-level system behaviors. The simulations results reveal symmetrical trust levels between two trading agents, based on which the degree of trust relationship in each pair of trading agents as well as the resulting collaboration patterns in the entire supply network emerge. Also, it is shown that agents' decision-making behavior based on the trust relationship can contribute to the reduction in the variability of inventory levels. This result can be explained by the fact that mutual trust relationship based on the past experiences of trading diminishes an agent's uncertainties about the trustworthiness of its trading partners and thereby tends to stabilize its inventory levels.

Journal ArticleDOI
TL;DR: This paper proposes a framework that makes sense of the nature of chronicity and its multiple dimensions beyond disease and argues for a set of building blocks and leverage points that should constitute the starting points for 'redesign'?
Abstract: Background The Chronic Care Model (CCM) is widely taken up as the universal operational framework for redesigning health systems to address the increasing chronic disease burden of an ageing population. Chronic care encompasses health promotion, prevention, self management, disease control, treatment and palliation to address ‘chronicity’ of long journeys through disease, illness and care in the varying contexts of complex health systems. Yet at an operational level, CCM activities are predominantly based on an evidence-base of discreet chronic disease interventions in specific settings; and their demonstrable impact is limited to processes of select disease management such as diabetes in specific disease management programs. Aims This paper proposes a framework that makes sense of the nature of chronicity and its multiple dimensions beyond disease and argues for a set of building blocks and leverage points that should constitute the starting points for ‘redesign’? Findings Complex Adaptive Chronic Care is proposed as an idea for an explanatory and implementation framework for addressing chronicity in existing and future chronic care models. Chronicity is overtly conceptualized to encompass the phenomena of an individual journey, with simple and complicated, complex and chaotic phases, through long term asymptomatic disease to bodily dysfunction and illness, located in family and communities. Chronicity encompasses trajectories of self-care and health care, as health, illness and disease co-exist and co-evolve in the setting of primary care, local care networks and at times institutions. A systems approach to individuals in their multi-layered networks making sense of and optimizing experiences of their chronic illness would build on core values and agency around a local vision of health, empowerment of individuals and adaptive leadership, and it responds in line with the local values inherent in the community's disease-based knowledge and the local service's history and dynamics. Complex Adaptive Chronic Care exceeds the current notions of disease management as an endpoint. Primary care team members are system adaptors in partnership with individuals constructing their care and system leadership in response to chronic illness, and enable healthy resilience as well as personal healing and support. Outcomes of complex adaptive chronic care are the emergence of health in individuals and communities through adaptability, self-organization and empowerment. Discussion Chronic care reform from within a complex adaptive system framework is bottom up and emergent and stands in stark contrast to (but has to co-exist with) the prevailing protocol based disease care rewarding selective surrogate indicators of disease control. Frameworks such as the Chronic Care Model provide guidance, but do not replace individual experience, local adaptive leadership and responsiveness. The awareness of complexity means opening up problems to a different reality demanding different set of questions and approaches to answer them.

Journal ArticleDOI
TL;DR: A new concept, the Internal-flow emergy, is used to indicate the evolution direction of an eco-industrial system and is adopted as a case study to illustrate the effectiveness of the agent-based modeling.

Journal ArticleDOI
TL;DR: This project applies agent-based modeling techniques to better understand the operation, organization, and structure of a local heroin market in Denver, CO during the 1990s, using data from an 18-month ethnographic case study.
Abstract: This project applies agent-based modeling (ABM) techniques to better understand the operation, organization, and structure of a local heroin market. The simulation detailed was developed using data from an 18-month ethnographic case study. The original research, collected in Denver, CO during the 1990s, represents the historic account of users and dealers who operated in the Larimer area heroin market. Working together, the authors studied the behaviors of customers, private dealers, street-sellers, brokers, and the police, reflecting the core elements pertaining to how the market operated. After evaluating the logical consistency between the data and agent behaviors, simulations scaled-up interactions to observe their aggregated outcomes. While the concept and findings from this study remain experimental, these methods represent a novel way in which to understand illicit drug markets and the dynamic adaptations and outcomes they generate. Extensions of this research perspective, as well as its strengths and limitations, are discussed.

Posted Content
J. B. Ruhl1
TL;DR: In this paper, the authors describe the subject matter of environmental law as a complex adaptive system and explain why environmental law thus must "think like an adaptive system" in order to accomplish its objectives.
Abstract: This article is the fourth in my series of articles exploring the application of complex adaptive systems (CAS) theory to legal systems. It applies the model built in the three prior installments (in the Duke, Vanderbilt, and UC-Davis law reviews) to the specific context of environmental law. The work describes the subject matter of environmental law as a CAS and explains why environmental law thus must "think like a complex adaptive system" in order to accomplish its objectives.

18 Nov 2009
TL;DR: This thesis presents a method for creating Agent Based Models that suitably represent complex evolving systems and is demonstrated to yield subsequent generations of richer and ever more useful simulation models.
Abstract: Exactly predicting the future of an evolving large scale socio-technical system is impossible. Yet, if we are to sustainably manage the industrial and infrastructure systems our society depends on, we must understand how the actions we take today will affect the evolution of these systems. Simulating how the social and technical networks co-evolve over time allows us to explore possible system futures. This knowledge can help today’s decision makers to steer the system away from undesirable evolutionary pathways. Creating models that capture the complexity of socio-technical systems co-evolution requires multiple formalisms to be encoded in a modeling framework that itself evolves. This thesis presents a method for creating Agent Based Models that suitably represent complex evolving systems. The method involves a co-evolution between the technical aspects of model development, the social process involving the stakeholders, the collection of relevant domain knowledge and the encoding of facts. Through seven case studies the method is demonstrated to yield subsequent generations of richer and ever more useful simulation models.

Posted Content
J. B. Ruhl1
TL;DR: The first in a series of articles exploring the application of complex adaptive systems (CAS) theory to legal systems is as mentioned in this paper, where the authors build the basic model of CAS and map it onto legal systems, offering some suggestions for what it means in terms of legal institution and instrument design.
Abstract: This article is the first in my series of articles exploring the application of complex adaptive systems (CAS) theory to legal systems. It builds the basic model of CAS and maps it onto legal systems, offering some suggestions for what it means in terms of legal institution and instrument design.

Journal ArticleDOI
TL;DR: In this article, the authors argue that the foreground is the knowledge or technology tied up with the product or program that prompted the evaluation, while the background is the ongoing dynamics of the context into which the knowledge is inserted.
Abstract: Programs and policies invariably contain new knowledge. Theories about knowledge utilization, diffusion, implementation, transfer, and knowledge translation theories illuminate some mechanisms of change processes. But more often than not, when it comes to understanding patterns about change processes, “the foreground” is privileged more than “the background.” The foreground is the knowledge or technology tied up with the product or program that prompted the evaluation. The background is the ongoing dynamics of the context into which the knowledge is inserted. Complex adaptive system thinking encourages greater attention to this context and the interactions and consequences that result from the intervention, making these the forefront of attention. For the evaluator, there are implications of this shift in thinking. Process evaluations should be designed to capture the fluidity of the change process. Impact and outcome evaluations will require long time frames. Complex adaptive system thinking also encourages multilevel measures, a focus on structures, and capacity to assess the possibility of whole system transformation (whole school, whole organization) as a result of the newly introduced program or policy. For the people involved in the innovation, there is a corresponding shift from a focus on their knowledge (and competence) to assessment of their learning (and system-level capability). New ways to interpret fidelity in these situations should therefore be developed. © Wiley Periodicals, Inc., and the American Evaluation Association.

Journal ArticleDOI
TL;DR: An evolution model of SNs is proposed in order to understand the general principle of SN evolution, and a multi-agent simulation on the evolution model discloses that the SN emerges and evolves from firms' dynamic interaction under the dynamic environment.

Journal ArticleDOI
TL;DR: From the experiments, it is found that efficient allocation can be realized despite a lack of communications among the participants or any instructions to them and in the critical region, the overall system will behave in an efficient, stable, and unpredictable mode in which the market's invisible hand can fully play its role.
Abstract: There has been a belief that with the directing power of the market, the efficient state of a resource-allocating system can eventually be reached even in a case where the resource is distributed in a biased way. To mimic the realistic huge system for the resource allocation, we designed and conducted a series of economic experiments. From the experiments we found that efficient allocation can be realized despite a lack of communications among the participants or any instructions to them. To explain the underlying mechanism, an extended minority game model called the market-directed resource allocation game (MDRAG) is constructed by introducing heterogeneous preferences into the strategy-building procedures. MDRAG can produce results in good agreement with the experiments. We investigated the influence of agents' decision-making capacity on the system behavior and the phase structure of the MDRAG model as well. A number of phase transitions are identified in the system. In the critical region, we found that the overall system will behave in an efficient, stable, and unpredictable mode in which the market's invisible hand can fully play its role.

Journal ArticleDOI
TL;DR: In this paper, the authors focus on the long-term sustainability of current practices of resource management, which no longer appear robust to uncertainty from extreme weather events or trends and highlight the need for profound changes in resource management (Gleick 2000, Pahl-Wostl 2007).
Abstract: In recent years, the prospects of severe climate change have increasingly focused attention on the long-term sustainability of current practices of resource management, which no longer appear robust to uncertainty from extreme weather events or trends. Increased awareness of uncertainties and the complexity of the systems to be managed highlight the need for some profound changes in resource management (Gleick 2000, Pahl-Wostl 2007). Uncertainties and complexity have always characterized water management. Water management traditionally emphasizes the reduction of uncertainties, often by designing systems that can be predicted and controlled. This has resulted in a strong emphasis on technical solutions to rather narrowly defined problems. However, human– technology–environment systems are more appropriately described as complex adaptive systems where unpredictable co-evolution makes uncertainty irreducible. Managing under inevitable uncertainty requires improved learning and adaptation, in addition to control. The goal of management should be to increase the adaptive capacity to learn from and better cope with uncertain developments, rather than to try to find optimum solutions. Watermanagement science must confront the main barriers to learning and adaptation: path dependence emerging from sunk costs in prior paradigms, infrastructure, and existing practices. Developing new paradigms and practices has gained increasing importance with the attempt to implement integrated management approaches.

Journal ArticleDOI
TL;DR: It is found that industrial ecology and system of systems present complementary frameworks for posing systemic problems in the context of sociotechnical applications and complexity science provides a natural and necessary foundation and set of tools to analyze mechanisms such as evolution, emergence, and regulation.
Abstract: Summary Two objectives are pursued in this article. First, from a methodological perspective, we explore the relationships among the constructs of complex adaptive systems, systems of systems, and industrial ecology. Through examination of central traits of each, we find that industrial ecology and system of systems present complementary frameworks for posing systemic problems in the context of sociotechnical applications. Furthermore, we contend that complexity science (the basis for the study of complex adaptive systems) provides a natural and necessary foundation and set of tools to analyze mechanisms such as evolution, emergence, and regulation in these applications. The second objective of the article is to illustrate the use of two tools from complexity sciences to address a network transition problem in air transportation framed from the system-of-systems viewpoint and shaped by an industrial ecology perspective. A stochastic simulation consisting of network theory analysis combined with agent-based modeling to study the evolution of an air transport network is presented. Patterns in agent behavior that lead to preferred outcomes across two scenarios are observed, and the implications of these results for decision makers are described. Furthermore, we highlight the necessity for future efforts to combine the merits of both system of systems and industrial ecology in tackling the issues of complexity in such large-scale, sociotechnical problems.

01 Jan 2009
TL;DR: In this paper, a stochastic simulation consisting of network theory analysis combined with agent-based modeling is presented to study the evolution of an air transport network, and the implications of these results for decision makers are described.
Abstract: Summary Twoobjectivesarepursuedinthisarticle.First,fromamethodological perspective, we explore the relationships among the constructs of complex adaptive systems, systems of systems, and industrial ecology. Through examination of central traits of each, we find that industrial ecology and system of systems present complementary frameworks for posing systemic problems in the context of sociotechnical applications. Furthermore, we contend that complexity science (the basis for the study of complex adaptive systems) provides a natural and necessary foundation and set of tools to analyze mechanisms such as evolution, emergence, and regulation in these applications. The second objective of the article is to illustrate the use of two tools from complexity sciences to address a network transition problem in air transportation framed from the system-of-systems viewpoint and shaped by an industrial ecology perspective. A stochastic simulation consisting of network theory analysis combined with agent-based modeling to study the evolution of an air transport network is presented. Patterns in agent behavior that lead to preferred outcomes across two scenarios are observed, and the implications of these results for decision makers are described. Furthermore, we highlight the necessity for future efforts to combine the merits of both system of systems and industrial ecology in tackling the issues of complexity in such large-scale, sociotechnical problems.

01 Jan 2009
TL;DR: The authors examine how risk is employed within the United Kingdom's Civil Contingencies Secretariat's policy of resilience, and draw a link between risk and governance within the modern network society.
Abstract: Looking at the way risk is employed within the United Kingdom’s Civil Contingencies Secretariat’s policy of resilience, this article critically examines how contingency is managed within contemporary biopolitical security practices seeking to protect and promote species-life. Underlying these changes, it will be argued, are profound changes in the way species-life is generally understood in terms of a complex adaptive network. Paying particular attention to how contingency is understood within the literature on complex adaptive systems that inform contemporary notions of the ‘network society’, this article will seek to draw a link between risk and governance within the modern ‘network society.’ In doing so, this paper seeks to examine how advances in the protocological control of networks are informing biopolitical security practices and their relation to the governmental rationality of neo-liberalism

Journal ArticleDOI
TL;DR: In this article, the authors address the problem of how change and innovation can create a fuller voice for ecological interests in organizations and public policy, raising issues about change mechanisms at the institutional versus organizational level.
Abstract: This article addresses the problem of how change and innovation can create a fuller voice for ecological interests in organizations and public policy, raising issues about change mechanisms at the institutional versus organizational level. First, it suggests that the newer, systems-based and inclusive approaches to organizational development practice and theory may overcome shortcomings of earlier approaches to planned change. Second, it argues that co-evolutionary approaches that use complex adaptive systems thinking will more effectively structure such third-generation interventions by focusing on issues at the institutional level. Third, the article examines a dialectical model of institutional change which incorporates activist input and channels conflict into innovative outcomes. Finally, it presents a case example of how a dialectical model combined with a co-evolutionary perspective could foster the institutional change required to facilitate the integration of ecological priorities into the human ...