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Complex adaptive system

About: Complex adaptive system is a research topic. Over the lifetime, 3190 publications have been published within this topic receiving 111947 citations. The topic is also known as: Complex adaptive system, CAS.


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Journal ArticleDOI
TL;DR: It is proposed that the self-organizing process of adaptive comanagement development, facilitated by rules and incentives of higher levels, has the potential to expand desirable stability domains of a region and make social–ecological systems more robust to change.
Abstract: Ecosystems are complex adaptive systems that require flexible governance with the ability to respond to environmental feedback. We present, through examples from Sweden and Canada, the development of adaptive comanagement systems, showing how local groups self-organize, learn, and actively adapt to and shape change with social networks that connect institutions and organizations across levels and scales and that facilitate information flows. The development took place through a sequence of responses to environmental events that widened the scope of local management from a particular issue or resource to a broad set of issues related to ecosystem processes across scales and from individual actors, to group of actors to multiple-actor processes. The results suggest that the institutional and organizational landscapes should be approached as carefully as the ecological in order to clarify features that contribute to the resilience of social-ecological systems. These include the following: vision, leadership, and trust; enabling legislation that creates social space for ecosystem management; funds for responding to environmental change and for remedial action; capacity for monitoring and responding to environmental feedback; information flow through social networks; the combination of various sources of information and knowledge; and sense-making and arenas of collaborative learning for ecosystem management. We propose that the self-organizing process of adaptive comanagement development, facilitated by rules and incentives of higher levels, has the potential to expand desirable stability domains of a region and make social-ecological systems more robust to change.

1,705 citations

Journal ArticleDOI
TL;DR: A brief introduction to ABMS is provided, the main concepts and foundations are illustrated, some recent applications across a variety of disciplines are discussed, and methods and toolkits for developing agent models are identified.
Abstract: Agent-based modelling and simulation (ABMS) is a relatively new approach to modelling systems composed of autonomous, interacting agents. Agent-based modelling is a way to model the dynamics of complex systems and complex adaptive systems. Such systems often self-organize themselves and create emergent order. Agent-based models also include models of behaviour (human or otherwise) and are used to observe the collective effects of agent behaviours and interactions. The development of agent modelling tools, the availability of micro-data, and advances in computation have made possible a growing number of agent-based applications across a variety of domains and disciplines. This article provides a brief introduction to ABMS, illustrates the main concepts and foundations, discusses some recent applications across a variety of disciplines, and identifies methods and toolkits for developing agent models.

1,597 citations

Journal ArticleDOI
TL;DR: An evolving approach to analyzing resilience in SESs, as a basis for managing resilience, with a framework with four steps, involving close involvement of SES stakeholders is proposed.
Abstract: Approaches to natural resource management are often based on a presumed ability to predict probabilistic responses to management and external drivers such as climate. They also tend to assume that the manager is outside the system being managed. However, where the objectives include long-term sustainability, linked social-ecological systems (SESs) behave as complex adaptive systems, with the managers as integral components of the system. Moreover, uncertainties are large and it may be difficult to reduce them as fast as the system changes. Sustainability involves maintaining the functionality of a system when it is perturbed, or maintaining the elements needed to renew or reorganize if a large perturbation radically alters structure and function. The ability to do this is termed "resilience." This paper presents an evolving approach to analyzing resilience in SESs, as a basis for managing resilience. We propose a framework with four steps, involving close involvement of SES stakeholders. It begins with a stakeholder-led development of a conceptual model of the system, including its historical profile (how it got to be what it is) and preliminary assessments of the drivers of the supply of key ecosystem goods and services. Step 2 deals with identifying the range of unpredictable and uncontrollable drivers, stakeholder visions for the future, and contrasting possible future policies, weaving these three factors into a limited set of future scenarios. Step 3 uses the outputs from steps 1 and 2 to explore the SES for resilience in an iterative way. It generally includes the development of simple models of the system's dynamics for exploring attributes that affect resilience. Step 4 is a stakeholder evaluation of the process and outcomes in terms of policy and management implications. This approach to resilience analysis is illustrated using two stylized examples.

1,533 citations

Journal ArticleDOI
TL;DR: Given the multiple levels at which dynamics become apparent and at which selection can act, central issues relate to how evolution shapes ecosystems properties, and whether ecosystems become buffered to changes over their ecological and evolutionary development or proceed to critical states and the edge of chaos.
Abstract: Ecosystems are prototypical examples of complex adaptive systems, in which patterns at higher levels emerge from localized interactions and selection processes acting at lower levels. An essential aspect of such systems is nonlinearity, leading to historical dependency and multiple possible outcomes of dynamics. Given this, it is essential to determine the degree to which system features are determined by environmental conditions, and the degree to which they are the result of self-organization. Furthermore, given the multiple levels at which dynamics become apparent and at which selection can act, central issues relate to how evolution shapes ecosystems properties, and whether ecosystems become buffered to changes (more resilient) over their ecological and evolutionary development or proceed to critical states and the edge of chaos.

1,487 citations

Journal ArticleDOI
TL;DR: In this article, a series of case studies is used to test an emerging theory of complex adaptive systems that forms the basis for explaining the interrelated dynamics of ecosystems, institutions and society.
Abstract: This volume uses a series of case studies to test an emerging theory of complex adaptive systems that forms the basis for explaining the interrelated dynamics of ecosystems, institutions and society. It deals equally with institutional organization and ecosystem structure.

1,434 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202336
202269
2021120
2020132
2019152
2018191