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Cited by 2,118

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Perspective: Complexity Theory and Organization Science

Anderson, Philip·1999·Organization Science
DOI 10.1287/orsc.10.3.216
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Abstract

Complex organizations exhibit surprising, nonlinear behavior. Although organization scientists have studied complex organizations for many years, a developing set of conceptual and computational tools makes possible new approaches to modeling nonlinear interactions within and between organizations. Complex adaptive system models represent a genuinely new way of simplifying the complex. They are characterized by four key elements: agents with schemata, self-organizing networks sustained by importing energy, coevolution to the edge of chaos, and system evolution based on recombination. New types of models that incorporate these elements will push organization science forward by merging empirical observation with computational agent-based simulation. Applying complex adaptive systems models to strategic management leads to an emphasis on building systems that can rapidly evolve effective adaptive solutions. Strategic direction of complex organizations consists of establishing and modifying environments within which effective, improvised, self-organized solutions can evolve. Managers influence strategic behavior by altering the fitness landscape for local agents and reconfiguring the organizational architecture within which agents adapt.

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Metadata

Authors
Anderson, P.
Venue
Organization Science
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Published
1999-06
Volume
10
Issue
3
Pages
216-232
ISSN
  • 1047-7039 (ISSN-L)
  • 1526-5455
DOI
10.1287/orsc.10.3.216
OpenAlex
W1979058254
Cited by
2,118
References
7
FWCI
25.72
Type
journal article
Language
en
Open access
closed

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.