Understanding the difference between complex and complicated systems is becoming important for many aspects of management and policy. Each system is better managed with different leadership, tools and approaches. This post explains the differences, and provides an introduction to management tools and leadership tasks best suited for complexity.
A major breakthrough in understanding how to manage complex multistakeholder situations and programs has come through the field of systems theory. Systems thinking is a way of helping people to see the overall structures, patterns and cycles in systems, rather than seeing only specific events or elements. It allows the identification of solutions that simultaneously address different problem areas and leverage improvement throughout the wider system. It is useful, however, to distinguish between different types of systems.
Simple, complicated or complex
According to a classic report in healthcare by Sholom Glouberman and Brenda Zimmerman – Complicated and Complex systems: What would successful reform of Medicare look like? – systems can be usefully seen as lying along a broad continuum from ‘simple’ to ‘complicated’ to ‘complex’.
Simple problems (such as following a recipe or protocol), may encompass some basic issues of technique and terminology, but once these are mastered, following the ‘recipe’ carries with it a very high assurance of success. Complicated problems (like sending a rocket to the moon), are different. Their complicated nature is often related not only to the scale of the problem, but also to their increased requirements around coordination or specialized expertise. However, rockets are similar to each other and because of this following one success there can be a relatively high degree of certainty of outcome repetition. In contrast complex systems are based on relationships, and their properties of self-organisation, interconnections and evolution. Research into complex systems demonstrates that they cannot be understood solely by simple or complicated approaches to evidence, policy, planning and management. The metaphor that Glouberman and Zimmerman use for complex systems is like raising a child. Formula have limited application. Raising one child provides experience but no assurance of success with the next. Expertise can contribute but is neither necessary nor sufficient to assure success. Every child is unique and must be understood as an individual. A number of interventions can be expected to fail as a matter of course. Uncertainty of the outcome remains. The most useful solutions usually emerge from discussions within the wider family and involve values.
These differences have important implications for management. Complicated systems are all fully predictable. These systems are often engineered. We can understand these systems by taking them apart and analyzing the details. From a management point of view we can create these systems by first designing the parts, and then putting them together. However, we cannot build a complex adaptive system (CAS) from scratch and expect it to turn out exactly in the way that we intended. CAS are made up of multiple interconnected elements, and are adaptive in that they have the capacity to change and learn from experience – their history is important. Examples of CAS include ourselves (human beings), a flock of birds (e.g. the picture above), the stock market, ecosystems, immune systems, and any human social-group-based endeavor in a cultural and social system. CAS defy attempts to be created in an engineering effort, and the components in the system co-evolve through their relationships with other components. But we can achieve some understanding by studying how the whole system operates, and we can influence the system by implementing a range of well-thought-out and constructive interventions.
Getting people to work collectively in a coordinated fashion in areas such as poverty alleviation or catchment management is therefore better seen by agencies as a complex, rather than a complicated problem – a fact many managers are happy to acknowledge …. but somehow this acknowledgement often does not translate into different management and leadership practice.
Indicators of progress in managing a complicated system are directly linked through cause and effect. However, indicators of progress in a complex system are better seen as providing a focus around which different stakeholders can come together and discuss, with a view to potentially changing their practices to improve the way the wider system is trending. Understanding this difference has important implications for management action as the table below highlights. In many cases people continue to refer to the system they are trying to influence as if it were complicated rather than complex, perhaps because this is a familiar approach, and there is a sense of security in having a blueprint, and fixed milestones. Furthermore, it is easier to spend time refining a blueprint than it is to accept that there is much uncertainty about what action is required and what outcomes will be achieved. When dealing with a complex system, it is better to conduct a range of smaller innovations and find ways to constantly evaluate and learn from the results and adjust the next steps rather than to work to a set plan.
The art of management and leadership lies in having an array of approaches, and being aware of when to use which approach. Most situations will have simple, complicated and complex system types present, and there may well be multiple systems involved. What is important is distinguishing between system types, and managing each in the appropriate way. Table 1 looks at different leadership roles that can be employed depending on whether one is dealing with a complicated or complex system.
Table 1: Different leadership roles for different systems
- Role defining – setting job and task descriptions
- Decision making – find the ‘best’ choice
- Tight structuring – use chain of command and prioritise or limit simple actions
- Knowing – decide and tell others what to do
- Staying the course – align and maintain focus
Complex adaptive systems
- Relationship building – working with patterns of interaction
- Sense making – collective interpretation
- Loose coupling – support communities of practice and add more degrees of freedom
- Learning – act/learn/plan at the same time
- Notice emergent directions – building on what works
As Irene Ng points out in her Complicated vs Complex Outcomes post we have spent the last 100 years doing complicated rather well. “We can pat our backs on putting the man on the moon, doing brain surgeries etc.” We are now moving to a world where learning and innovation are becoming key outcomes, and delivering these requires new skills and capacities. As Irene Ng so eloquently puts it, “We can determine complicated outcomes. We can only enable complex outcomes. We can specify complicated systems. We can only intervene in complex systems.”
Look for leverage points
In complex situations it is useful to move beyond thinking of “a change” that will fix the system, and instead look for a number of “leverage points” that may be adjusted to improve the system. Encouraging the development and implementation of new work practices, for example, may require changes in rules (e.g. laws, protocols and tacit norms), changes in relationships, networks and patterns of behavior (e.g. how conflict is handled, how mistakes are managed, how power is used), and the use of a range of tools (e.g. databases, checklists, guidelines). One-size fits all approaches are unlikely to work in complex adaptive systems. The way solutions are visioned and delivered locally must reflect the values, contexts and cultures of each different community of stakeholders.
The key for those working with these complex adaptive systems is to support ongoing reflection and collaboration among the different people and groups involved. Future visions and common goals need to be openly discussed and negotiated, and tentative pathways forward charted. Over time good practice in these areas will lead to creative collaborative and partnering arrangements that support ongoing innovation and sustainable development.
This post complements the Learning for Sustainability portal Managing complex adaptive systems page, which provides annotated links to a number of key on-line resources in this area. Theories of Change and associated outcomes models are useful tools which help managers to go beyond linear paths of cause and effect, to explore how change happens more broadly and then analyze what that means for the part that their particular agency or program can play.
[An earlier version of this post was originally posted on the Learning for Sustainability sparks for change blog – 3 March 2013]
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