The term complex adaptive systems (CAS) can be applied to describe entities consisting of several interdependent parts. Such systems are capable of changing the behavior as a unified whole in response to changes in their environment or the experience of one or more parts (Brownlee, 2007). John Holland proposed that adaptive systems share four common properties of aggregation, nonlinearity, flow, and diversity, and three mechanisms: tagging, internal models, and building blocks (Nelson, 2005). This post aims to analyze the properties mentioned earlier, assess their usefulness, and evaluate how they inform the understanding of the structure of organizations.
Any large company can be considered a complex adaptive system. Thus, Amazon is an example of a complex system, as the company possesses the properties of aggregation, nonlinearity, flow, and diversity. Specifically, the organization consists of numerous departments or aggregations with resources flowing through them and the behavior of each aggregation is unique, diverse, and nonlinear (Smith & Bedau, 2000). For example, the behavior and decisions made within the company’s product development department will differ from those made in the legal one. Nevertheless, the choices of one aggregation can affect others within the organization, as the decision to develop a new service, for example, will result in various aggregations working on different aspects of that service. In addition, the company works with numerous self-organized suppliers and does not require control from other aggregates.
Overall, Holland’s description of the complex adaptive system can be viewed as both valuable and problematic. The approach illustrates that every agent (employee or group of employees) within an organization can change their behavior and adapt to the market’s emerging needs (Goldstein, 2007). However, due to the nonlinear behavior of the aggregate agents, complex adaptive systems are highly unpredictable (Preiser et al., 2018). Thus, Holland’s ideas show that the company’s management should be adaptive to respond to the aggregate agents’ unexpected behaviors, the market’s needs, and company goals. Furthermore, the nonlinear property indicates the need for continuous monitoring and intuitive knowledge of which units can be self-organized and require more control.
Brownlee, J. (2007). Complex adaptive systems (Technical report 070302A).
Goldstein, J. (2007). Conceptual foundations of complexity science: Development and main constructs. In M. Uhl-Bien & R. Marion (Eds.), Complexity leadership: Part 1: Conceptual foundations (pp. 17–42). Information Age.
Preiser, R., Biggs, R., De Vos, A., & Folke, C. (2018). Social-ecological systems as complex adaptive systems: Organizing principles for advancing research methods and approaches. Ecology and Society, 23(4), 46–62.
Smith, R. M., & Bedau, M. A. (2000). Is echo a complex adaptive system? Evolutionary Computation, 8(4), 419–442.