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The Bedrock Principles of Complexity

Richard Pascale, in his 1999 Sloan Management Review article, challenged the economics-driven ideas of the positioning and resource-based views of strategy, arguing that their assumptions of ‘equilibrium’ as the normal state of affairs was irrelevant to contemporary business. According to Pascale (1999), the four properties of a complex adaptive system which needed to be present for it to qualify as complex system were:

  • 1) They should be comprised of many agents (large business/technol- ogy ecosystems consist of a large number of firms).
  • 2) They should have multiple layers and levels (the large business/ technology ecosystems cut across multiple industries and market boundaries).
  • 3) If they were not replenished with energy they would be subject to entropy. In other words if complex adaptive systems became too stable they would be vulnerable to extinction (continuous innovation in high technology sectors, particularly business model innovation).
  • 4) They should have the ability to recognise or sense patterns enabling them to anticipate but not predict the future (Big Data analytics).

Pascale pointed out that some systems are complex but not adaptive. To count as complex and adaptive a system must be able to anticipate and learn. Modern Big Data algorithms are now capable of doing this as they search and discover patterns in data leading to predictive and prescriptive analytics. Pascale (1999) also identified four bedrock principles of complexity which included:

  • 1) Equilibrium is a precursor to death for complex adaptive systems. Once an organisation becomes too stable it risks disaster by getting out of step with its changing environment. This often occurs when a firm stops innovating or follows an incremental trajectory devoid of any radical breakthroughs. This is what happened to Apple following the departure of Steve Jobs during 1985-1997.
  • 2) Complex adaptive systems have the capacity to organise themselves and generate more complexity. Online trends such as crowdsourcing for ideas and crowdfunding for the raising of finance are examples of this.
  • 3) Complex adaptive systems tend to move towards the edge of chaos. In order to innovate organisations need to have enough but not too much instability and they need to avoid the stagnation of equilibrium (this is discussed in more depth later in the chapter). In fact, chaos is a state that is experienced when technology firms in the ICT ecosystem experience product failures or when their products are rendered irrelevant or obsolete by new technologies from rival firms and platforms.
  • 4) You ‘can’t direct a living system, only disturb it’. Effective control over an organisation or an industry or any other adaptive system is limited. So trivial things can have strategic effects. The introduction of SMS text messaging appeared to be a trivial innovation but it had a significant strategic effect on the telecoms industry.

We will now analyse each of these four bedrock principles in more depth starting with the principle that stable equilibrium equals death. Pascale noted that ‘bounded instability’ was more conducive to evolution than stable equilibrium or ‘explosive instability’. When organisations are too stable they lose their ability to be responsive and adapt. One of the characteristics of complex adaptive systems is that they are subject to entropy (i.e. the loss of energy and ultimate decline). Without the injection of new energy of some kind (e.g. Google’s need to continue to generate ‘traffic’ and capture user data for monetisation through advertising) they slow down and die. On the other hand, explosive instability leads to chaos as was illustrated by the dot.com boom and bust during 1995-2000.

With self-organisation and emergence, complex adaptive systems are comprised of many agents creating many interactions at multiple levels. These interactions also follow very simple rules. Multiple interactions following such rules are capable of generating complex emergent behaviour at the global level. An example of this would be viral marketing and network effects that are prone to self-organisation and emergence involving many agents and interactions across a global platform.

Complex systems also evolve on the edge of chaos (Pascale 1999; McMillan 2008). The edge of chaos is a systems state on a continuum of possible states ranging from completely random to highly mechanistic and stable (see Table 3.2).

As a system moves towards chaos, its elements become ever more highly connected. The edge of chaos is a space in which new order emerges. It is, therefore, important and relevant to the study of strategic processes such as innovation and emergence. At the mechanistic end of the continuum, a system is highly stable, ordered and resistant to change. At the random end of the continuum, there is no apparent order at all. Using fractal geometry, a relatively new branch of mathematics concerned with shapes and patterns, mathematicians have been able to detect patterns in seemingly random events in chaotic systems (those operating in the chaos zone).

Truly random systems do not exist in the world of organisations - in spite of their unpredictability. In human affairs, the systems that are of interest are those depicted in the centre of Table 3.2, namely hierarchical, complex and chaotic. The ability to detect some regularities (patterns) in chaotic systems is important. As Pascale (1999) pointed out, pattern recognition (rather than prediction, as in Newtonian science) enables complex systems to anticipate and prepare for unpredictable events.

Most human systems, such as organisations and economies, are normally complex rather than chaotic in the highly interconnected and networked world. As complex systems tend to evolve to the edge of chaos on the continuum they become highly adaptive. However, the risk is that these systems can tip over the edge of chaos into actual chaos for a time. A chaotic system is one in which wholly unpredictable behaviour has arisen. Complex systems can and do periodically tip into chaos (Pascale 1999).

Table 3.2 Types of system and degrees of stability, chaos & complexity (Adapted front McMillan & Carlisle: 2007)

Explosive Instability

Bounded Instability

Stability & Equilibrium

Type of System

Random

Chaotic

Edge of Chaos

Complex

Edge of Stability

Hierarchy

Mechanistic

Level & Type of Control

None at all

Difficult to detect

Primarily selforganisation

Command and control

Rigid and tight controls

Type of Agent

Relationships

Independent agents; no detectable relationships

Volatile and random

A highly

connected

network

Formal and dictated by top-down directives

Prescribed and fixed

Type of Interactions

Random and

highly

irregular

Some minor regularity

Interdependent and fluid

Largely

dependent

Completely

dependent

System

Outcomes

Random

outcomes,

possibly

disintegration

Instability with unstable changes and outcomes

Flexible new order involving radical and/or incremental changes

Stable with only

incremental

changes

Highly stable mechanistic systems that are resistant to change

Zone of Complexity

The key to keeping a complex system, like an organisation, productively sub-chaotic is to maintain an appropriate tension between flexibility and control, whereas traditional strategic thinking emphasises control only (usually through negative feedback).

Complexity science suggests that it is unlikely that humans will have control of the world that surrounds them. In fact, small events occurring in a complex system operating at the edge of chaos can have unpredictable effects which can push them over the edge. The butterfly effect is a good example of this (Lorenz 1963) and provides a metaphor for a very small disturbance having a massive effects elsewhere in a system. This sums up the way that complex systems are adaptive. Self-organising agents produce novel emergent structures and behaviours and both incremental and radical change can take place but their behaviour is not random. Complexity is a state of ‘bounded instability’ (Pascale 1999). In the very short-term, some degree of prediction is possible. However, as complex systems lapse into chaos their behaviour becomes wildly unpredictable in the short-term. The implication for this (as discussed in Chapter 1) is to recast managers as facilitators of emergent strategy rather than designers and directors of deliberate strategy (Mintzberg and Waters 1985) and forecasting becomes futile.

 
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