Order from chaos: How to apply complexity theory at work
All organizations operate at what is known as the edge of chaos. It is a transition space between order and disorder, a region of bounded instability, where forces both progressive and conservative battle for control. A manager may have a three-year strategy, and the protocol, teams and departments in place to deliver it, but realizing that strategy means contending with countless disruptive events; a new competitor entering the market, a shift in consumer behavior – even, perhaps, the manager’s own desire to retire to a quiet village on the countryside.
The term itself suggests something has gone wrong, but operating at the edge of chaos is actually healthy; scientists have shown that all large and complex systems tend to adapt this way. Whether in nature, society or economics, systems must find the right balance between order and flexibility. This is because their survival and success depend on being able to constantly sense and adapt to changes in the environment they operate within. In a business context, operating at the edge of chaos opens up avenues for disruptive innovation, cultural overhaul and process evolution, all of which help organizations adapt to changing market environments.
Complexity theory is founded on researchers' attempts to rationalize the behavior of large and complex systems that operate at the edge of chaos. Traditionally associated with the fields of computer science, mathematics and economics, complexity science is now gaining traction in the world of business too. By studying complex and chaotic systems, we can begin to see how order, pattern and structure arise from them. We can see how the many disparate elements of a system work with each other to shape the whole and its outcomes, as well as how each element evolves over time.
Enterprise has always been challenged by unintended system-level consequences arising from well-intentioned individual-level actions. And, now that rapid technological advancements are driving our world to higher and higher magnitudes of interconnectivity, organizations are encountering complex systems with even greater frequency and consequence. So it is more important than ever for organizations to consider ways in which complexity theory can help.
When we first look at a large and man-made system like that of a multinational organization, we see, more than anything else, structure.
Strategy, protocol, teams, departments, hierarchies. All meticulously organized for optimal performance.
Or at least, that’s how it’s supposed to be. But when we apply a complexity theorist’s lens to the business we do, we see matters are rather more complex. We no longer view organizations as organizations, or departments as departments, but as complex adaptive systems, most helpfully understood in the three parts:
Firstly, employees (in complexity speak: heterogeneous agents). Each employee has different and evolving decision rules that both reflect the environment and attempt to anticipate change in it. Secondly, employees interacting with one another, and the structures that these interactions create – scientists call this emergence. Lastly, the overarching structure that emerges, behaving like a higher-level system with properties and characteristics distinct from those of its underlying agents. This last part is the reason we often say ‘the whole is greater than the sum of its parts’.
Given managers’ desire for control, complexity is far from a convenient reality. Rather than face the brutal reality of the system they are working to sustain, managers often work in silos, creating models and mechanisms that impose a veneer of certainty. In so doing, they help themselves and their colleagues to make decisions with fewer variables. Meeting the goals set out by these models generates evidence of success – but it is a simplified success that may not be in the best interests of the system as a whole.
For instance, placing a rigid priority on maximizing shareholder returns makes things clear for workers: in the case of a difficult tradeoff, the option that lends itself to immediate profitability is the preferable option. But, of course, we are all aware that cutting down on expenses and investments to boost short-term margins can be detrimental to the long-term health of a company. Only by embracing complexity can we effectively balance competing values and priorities (and the effects of decisions on all of them).
It should not surprise us that complexity theory is unavoidably complex. But leveraging such a big idea is far from beyond our grasp. For those seeking to understand how best to grapple with complex adaptive systems, try to remember these three golden rules:
1. Avoid extrapolation
A common error in thinking is to extrapolate the behavior of individual agents to that of a system. This is a logical fallacy.
Instead, try to consider the system at the correct level. If you want to understand why footballer’s wages inflated so rapidly in the past twenty years, consider the multiplicity of revenue streams now available to a professional club’s owner, rather than the bargaining prowess of an individual footballer. Similarly, if you want to understand the success of a star individual in your organization, consider what it is about the system they operate within (whether it’s a department or a team) that enables them to succeed.
2. Beware tightly coupled systems
Tightly coupled systems happen when multiple components become dependent upon one another. Pistols without bullets cannot fire, aircraft without runway cannot land safely. There is usually no slack between items, meaning processes go from one step to the next without opportunity for intervention. Space missions and nuclear power plants are classic examples. And though engineers may build in safety features and failsafes, they often don’t anticipate all possible contingencies, as was catastrophically illustrated by the 2011 Fukushima Daiichi nuclear disaster.
Professionals should strive to design loosely coupled projects and systems, where removing or repositioning one or a few components has little impact on overall performance. For instance, by commissioning IT programs that can be switched off for maintenance without affecting the wider IT infrastructure their organization relies on.
3. Use simulations to look into the future
Advances in technology mean we can predict the evolutions of complex systems with greater precision than ever before. Accurate weather prediction, once considered an impossibility, is now the norm. Forecasting models of staggering power and elegance can divide the atmosphere into millions of cubes, fill each up with temperature, humidity, wind speed and other variables, then set loose the laws of physics to predict what changes in the atmosphere will occur – as far as eight days in advance.
As the cost of simulation comes down, business leaders will do well to consider the game-changing system-level feedback these models might provide.
By recognizing complexity we are not only facing up to reality, but pushing ourselves to be more humble and open. We are understanding that our actions and interventions, in many cases, will not always have the desired effect – that we may sometimes be wrong, and that’s okay.
As Dr. Richard Straub says: “Embracing complexity will not make [our] jobs easier, but it is a recognition of reality, and an idea whose time has come.”
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