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p.7 The general message from the popular complexity science literature seems to be that, where we once focused on the parts of a system and how they functioned, we must now focus on the interactions between these parts, and how these relationships determine the identity not only of the parts but of the whole system... A complex (adaptive) system can be simply described as a system comprised of a large number of entities that display a high level of inter-activity. The nature of this interactivity is mostly nonlinear... It is interesting to note that a result of this is that sometimes it can be very difficult to associate effect with cause
p.7-8 There are a number of basic observations that have been made through the examination of such systems, primarily through the use of computer simulation and the mathematics of nonlinearity. The following sections will discuss the nature and implication of these observations in turn. For a more complete list refer to Cilliers (1998).
- System memory/history (Cilliers, 1998: 4). A complex system has memory/history captured at both the micro- (e.g., personal experiences, personal opinions, worldview) and macroscopic (e.g., culture, ritual, value system) levels. Therefore, system history plays an important role in defining the state of the system as well as affecting system evolution.
- A diversity of behaviors (Allen, 2001). A rich diversity of qualitatively different operating regimes exists that the system might adopt. This is a result of the nonlinear nature of the relationships that describe the interactivity between the different system constituents.
- Chaos and self-organization (Auyang, 1999). The system evolution is potentially incredibly sensitive to small disturbances (a phenomenon popularly referred to as deterministic chaos), as well as being potentially incredibly insensitive to large disturbances (as a result of self-organization or, alternatively, antichaos). All possibilities in between also exist. Complex systems are often quite robust.
- The incompressibility of complex systems (Cilliers, 1998: 4). Complex systems are incompressible, that is, it is impossible to have an account of a complex system that is less complex than the system itself without losing some of its aspects. Incompressibility (resulting from non-linearity) is probably the single most important aspect of complex systems when considering the development of any analytical methodology, or epistemology, for coping with such systems.
p.8 At a fundamental level, boundaries are inferred in order to allow us to begin to make sense of our surroundings. Hard, enduring boundaries do not exist in nature; all perceived boundaries are transient given a sufficiently broad timeframe.
p.9 An important aspect of sensemaking... is how both implicit and explicit assumptions create, or force, the boundary for analysis (see Richardson et al., 2000).
p.9 Maybe we should rename complexity science the “science of partial complex systems.” This usage would make explicit the fact that when considering any problem we are in fact investigating a part of a complex system. As such, all the hypotheses and concerns raised by a “science of partial complex systems” would be appropriate for all analyses, rather than just special cases.
p.10 we should be strongly aware of, and blatantly open about, the provisionality of any perspective that might be utilized in underpinning an analysis of any problem - we must demonstrate considerable humility. Without this scientific “humility,” we will continue to believe that our current understanding is true and defines all that is possible (and desirable).
p.10-11 The question arises as to whether we can now take this model and make predictions about the future operation of the system. The answer, from a complex systems perspective, is that if the qualitative nature of the assumptions that describe the new context remain valid, then the model will be useful, that is, if we remain in the same attractor basin within the “assumption space,” then the knowledge derived from such a model can be straightforwardly translated into the new context.
p.11 there are an infinite number of ways to talk about complexity
p.12 In many ways, complexity science provides insights concerning analysis that might be seen as nothing more than common sense... The aim of this article is not to question the basic observations made concerning “the complex system,” but to understand how the implications of these observations affect analysts’ abilities to discover “truths” (with a small t) concerning such systems.
p.13 Given that no one perspective can capture the inherent intricacies of complex systems, the analysis of complex systems requires us to consider a number of perspectives. The underlying premise for this is that by exploring a number of perspectives, a richer appreciation of the "state of affairs" or "problematic situation" of interest will be developed, resulting in more informed decision making... In short, a principal requirement of a complexity-based epistemology is the exploration of perspectives.
p.14 From a pragmatic point of view, however, we must accept that frameworks are essential in providing at least a focus or starting point for analysis. What we must be strongly aware of is that the theoretical insights offered by any framework should not be used to determine our explorations, but considered as an offering of direction, or simply as a source of creativity to fuel the exploration process.
p.15 By assuming the universe to be a complex system, complexity science offers an alternative perspective that supports the need for criticism, creativity, and pluralism through the notion of strong and weak exploration.
p.16-17 We know that all our choices to some extent incorporate a step in the dark, and therefore we cannot but be responsible for them... An awareness of the contingency and provisionality of things is far better than a false sense of security.
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