Ameneh Deljoo and Marijn Janssen
2. Theoretical background
2.1 Complex adaptive system
In this section an overview of complex systems is provided and the multi‐disciplinary nature of research on the topic will be explained. The section will firstly address the way of thinking required to grasp complex adaptive systems before moving on to discussing some of the basic concepts of complex systems. Finally, five overlapping concepts of complex adaptive systems will be offered as a starting point, or generative metaphor,( i. e. aggregation, flows, nonlinearity, diversity and evolving). These five concepts can assist in the process of understanding how CAS can assist with organizational understanding. This section will also provide most of the definitions required for understanding complex systems.
Many writers have used a story telling method of explaining CAS as it often requires going back and having a look at what has been said previously before progressing once again( ibid). According to several researchers(( Holmdahi 2005),( Levin 1998, 2002)) the concept of complexity science covers many fields of scientific research such as many natural systems( e. g., brains, immune systems, ecologies, societies) and increasingly, many artificial systems( parallel and distributed computing systems, artificial intelligence systems, artificial neural networks, evolutionary programs) are characterized by apparently complex behaviors that emerge as a result of often nonlinear spatio‐temporal interactions among a large number of component systems at different levels of organization( Eve Mitleton 2003; Jiang et al. 2011). These systems have recently become known as Complex Adaptive Systems( CAS). The complexity framework is originating from work in the natural sciences studying CAS, e. g., physics, chemistry, biology)( see for example( Dooley 1996; Rounsevell et al. 2012)).
( Holland 1992) defines a CAS as a“ collection of autonomous, heterogeneous agents, whose behavior is defined with a limited number of rules. These rules govern the types and number of interactions among agents”( p. 18). The power of the system mainly comes from agents’ interactions, not the agents themselves. Each individual interaction generally has only a small or limited direct effect on the outcome of the system. However, the aggregate product of the thousands of these interactions and the accumulated feedbacks among the agents can have a large effect. Systems behavior is made up by the interacting agents.
CAS is a method developed in physics, mathematics, and computational sciences( Eidelson 1997) to deal with the issue of complexity and complex systems and has been redefined by a growing number of applications in domains as diverse as biology, political science, economics( Holland 1992). Complex dynamical systems are comprised of parts that interact with each other. They are complex because it is impossible to predict their behavior by simply understanding the function of each part, primarily because the function of the overall system depends on the way these parts interact with each other( Wallis 2008).
The diversity of these parts and the richness of their interactions endow a complex system with its capacity to innovate, adapt, and sustain itself( Brown 2004). At the same time, these global, emergent properties cannot be studied or readily understood by only inspecting the parts in isolation. Complex dynamical systems are nonlinear, such that threshold effects allow a small change in the input value to cause disproportionate change in the output variable. They are also adaptive‐the system adjusts to both negative and positive changes caused by external or internal factors. They often exhibit emergent properties, i. e., properties not anticipated by the blueprint of the system( Chan 2001; Damper 2000). One of these properties is the ability of system’ s parts to self‐organize in some manner that is beneficial to this emerging group. CAS has the ability to exist and operate in a state between pure stability and complete instability in a region contains both.
Many interesting systems are difficult to describe or control using traditional system methods( Stowell and Welch 2012). They include natural ecological system, immune system, economic and other social systems( Levin 1998). One source of difficulty arises from nonlinear interactions among system components. Nonlinearities can lead to unanticipated emergent behaviors, a phenomenon that has been documented and studied in different majors. Nonlinear systems with interesting emergent behavior are often referred to as complex systems. Complex system primitive components of the system can change their specification, or evolve, over time. In this( section 2.1) CASs are dynamic systems able to adapt in and involve with a changing environment. It is important to realize there is no separation between a system and its environment in the idea that a system can always adapt to environment changing. Within such a context, change needs to be seen in terms of co‐evolution with all other related systems, rather than as adaptation to a separate and distinct environment
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