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.

From Animals to Animats: Proceedings of The First International Conference on Simulation of Adaptive Behavior (Complex Adaptive Systems)

Abstract: These sixty contributions from researchers in ethology, ecology, cybernetics, artificial intelligence, robotics, and related fields delve into the behaviors and underlying mechanisms that allow animals and, potentially, robots to adapt and survive in uncertain environments. They focus in particular on simulation models in order to help characterize and compare various organizational principles or architectures capable of inducing adaptive behavior in real or artificial animals. Jean-Arcady Meyer is Director of Research at CNRS, Paris. Stewart W. Wilson is a Scientist at The Rowland Institute for Science, Cambridge, Massachusetts.

Dynamics from open innovation to evolutionary change

Abstract: We created conceptual models that people may use to analyze and forecast the dynamic effects of open innovation, which we applied to the smartphone sector using a model-based analysis approach. In addition, we built an open innovation simulation model for the smartphone sector. The dynamic model of open innovation linked logic and concepts relating to open innovation, complex adaptive systems, and evolutionary change. The model can be used to analyze the dynamic effects of open innovation strategies and open innovation simulation for the selection of future strategies.

Entrepreneurship as connecting: some implications for theorising and practice

Abstract: Purpose – The purpose of this paper is to consider why entrepreneurship theorising has become fragmented and how the research problem might be resolved.Design/methodology/approach – The authors first examine how entrepreneurial constructs reflect only part of what we “mean” by the construct to argue that we use different social constructions. This explains why theories are fragmented. But the authors then ask how we might use and reconcile this diversity, pointing to the utility of the constructs as part of a complex whole. The authors discuss entrepreneurship as a complex adaptive system showing how connections and relatedness help explain the power of entrepreneurship to use and adapt to change.Research implications – The authors' proposition of entrepreneurial endeavours as a complex adaptive system provides a fresh theoretical platform to examine aspects of entrepreneurship and improve theorising.Practical implications – The authors argue that this idea of connecting can also be used at the level of p...

Organizational Learning, Innovation and Internationalization: A Complex System Model

Abstract: Research on organizational learning, innovation and internationalization has traditionally linked these concepts through linear causality, by considering any one of them as the cause of another, an approach that might be considered contradictory and static. This paper aims to clarify these relationships and proposes a dynamic theoretical model that has mutual causality at its core and is based on ideas originating in complexity theory. The final model results from case studies of two clothing sector firms. The authors consider that the three concepts constitute a complex system and can adapt and transcend, as any alteration can take the system to the edge of chaos. Adaptability is fostered by concentration, improvement and discussion. Transcendence is fostered by attention, dialogue and inquiry. The different paces of the two case study companies led their systems to two different models: the incremental complex adaptive system model and the global complex generative system model. The incremental model is characterized by adaptive learning, incremental innovation and low internationalization; and the global system is characterized by generative learning, radical innovation and global internationalization. The paper ends with an exploration of the academic and management implications of the model.

Signals and Boundaries: Building Blocks for Complex Adaptive Systems

Abstract: Complex adaptive systems (cas), including ecosystems, governments, biological cells, and markets, are characterized by intricate hierarchical arrangements of boundaries and signals. In ecosystems, for example, niches act as semi-permeable boundaries, and smells and visual patterns serve as signals; governments have departmental hierarchies with memoranda acting as signals; and so it is with other cas. Despite a wealth of data and descriptions concerning different cas, there remain many unanswered questions about "steering" these systems. In Signals and Boundaries, John Holland argues that understanding the origin of the intricate signal/border hierarchies of these systems is the key to answering such questions. He develops an overarching framework for comparing and steering cas through the mechanisms that generate their signal/boundary hierarchies. Holland lays out a path for developing the framework that emphasizes agents, niches, theory, and mathematical models. He discusses, among other topics, theory construction; signal-processing agents; networks as representations of signal/boundary interaction; adaptation; recombination and reproduction; the use of tagged urn models (adapted from elementary probability theory) to represent boundary hierarchies; finitely generated systems as a way to tie the models examined into a single framework; the framework itself, illustrated by a simple finitely generated version of the development of a multi-celled organism; and Markov processes.