Understanding the difference between complex and complicated systems is becoming important for many aspects of management and policy. With complicated problems or issues one can define the problem and strategically develop actions, time-frames and milestones along a path to success. In contrast, cause and effect are difficult to predict in complex adaptive systems. This post aims to provide more detail around these concepts as an introduction. It complements the LfS Managing complex adaptive systems page, which provides annotated links to a number of key on-line resources in this area.
A major breakthrough in understanding the complex world of organizations and socio-ecological environments is 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 system. It is useful, however, to distinguish between ‘simple’, ‘complicated’ and ‘complex adaptive’ systems.
According to a classic report in healthcare by Sholom Glouberman and Brenda Zimmerman systems can be understood as being simple, complicated, and 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 a problem (cf. simple systems), but also to issues of coordination or specialised 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, interconnectedness 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. Formulae 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. You cannot separate the parts from the whole. The most useful solutions to problems usually emerge from within the family and involve values. An outline of the management differences between complicated and complex systems can be seen below in Table 1.
Table 1 Managing complicated and complex systems
| Complicated systems (like sending a rocket to the moon) | Complex adaptive systems (like raising a child) |
| Formulae are critical and necessary | Formulae have limited application |
| Sending one rocket increases assurance that the next will be OK | Raising one child provides experience but no assurance of success with the next |
| High levels of expertise in a variety of fields are necessary for success | Expertise can contribute but is neither necessary nor sufficient to assure success |
| Rockets are similar in critical ways | Every child is unique and must be understood as an individual – relationships are important |
| There is a high degree of certainty of outcome | Uncertainty of outcome remains |
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 adaptive in that they have the capacity to change and learn from experience. Examples of CAS include ourselves (human beings), 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 problem, 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. Of course, many issues will have all system types present (simple, complicated and complex), and there may well be multiple systems involved. What is important is distinguishing between system types, and managing each in the appropriate way.
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 Table 2 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 is having an array of approaches and being aware of when to use which approach. Most issues will have simple, complicated and complex system types present, and there may well be multiple systems involved.
Table 2 Different leadership tasks for different systems (from Anderson & McDaniel 2000; Snowden & Moone 2007)
| Complicated systems | Complex adaptive systems |
| Role defining – setting job and task descriptions | Relationship building – working with patterns of interaction |
| Decision making – find the ‘best’ choice | Sense making – collective interpretation |
| Tight structuring – use chain of command and prioritise or limit simple actions | Loose coupling – support communities of practice and add more degrees of freedom |
| Knowing – decide and tell others what to do | Learning – act/learn/plan at the same time |
| Staying the course – align and maintain focus | 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 complex outcomes matter and this is a new capability. This capability uses different words. We can determine complicated outcomes. We can only enable complex outcomes. We can specify complicated systems. We can only intervene in complex systems. Often, the best way to think about whether a system is complex or complicated is to ask – ‘what is the outcome’; ‘is it achievable through a command and control structure’ and if the latter is no, then it’s usually complex.”
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 changed to improve the system. Changing what people do, 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 tools (e.g. databases, checklists, guidelines) for this change to ‘stick’. One-sized 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.
Finally, as with raising a child, people working in these complex adaptive situations need to keep learning about that situation, and to keep talking and working together in an ongoing way. Future visions and common goals need to be openly discussed and negotiated, and tentative pathways forward charted. While some actions will be taken by individual agencies working alone, new layers of creative collaborative and partnering arrangements will need to emerge. In these situations agencies should look to theories of change, 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.
Related resources
The previous blog post provides an introduction to theories of change, and a set of annotated links to key resources in this area can be found from the LfS web page – Theory of change. The BetterEvaluation Blog also has a very useful and related posting Addressing complexity which discusses the growing topic of how to address complexity in evaluation.
A recent TEDx talk about how to do improvisation as an art provides a number of relevant lessons for those of us looking to support participation and collaboration in arenas such as health, community development and natural resource management. In his presentation at TEDx Victoria in Canada, improviser and storyteller 
