The real-world basis for situational process management
by Jonathan Sapir, on
Situational process management is an important part of workflows.
It is not necessary to change. Survival is not mandatory.
W. Edwards Deming
Flocking birds are an example of Complex Adaptive Systems (CAS). There is no lead bird micro-managing the other birds and telling them all where to fly. The individual birds self-organize by adhering to a few simple rules.
Xpeditor works well because it is grounded in the way that knowledge workers actually work, rather than the way we think they should work. We always try to make things run like clockwork, but they rarely do. Instead of trying to make the clock work better, we need a more realistic way of looking at the way things really happen.
The self-organization leads to unpredictable results; you cannot predict the specific patterns that will emerge, but the behavior is adaptive and highly robust. In contrast, a large, monolithic production application is a complicated system – brittle, predictable, and hierarchical – requiring detailed planning and control. A CAS is NOT complicated: instead, it is adaptive, non-hierarchical, self-organizing, with robust emergent properties. Simple rules can lead to emergent results.
Applying the lessons of CAS can help us build tools that are more robust, more innovative, self-organizing and can quickly adapt to changes in the environment. Adopting a different frame of reference changes one’s perspective so that what was remote and unnatural becomes sensible and natural.
CAS recognizes the difficulty of planning everything in detail, especially when working within an unpredictable and constantly changing environment. It suggests that the best way to plan is by establishing minimum specifications (what coarse-grained steps need to be done?), a general sense of direction (what is the ultimate objective?), and a few basic principles (e.g., how hand-offs take place) on how to get there. Once the minimum specifications have been set, individuals self-organize and adapt as time goes by to a continually changing context.
Order is not preordained before the work begins – it emerges from the interaction of the independent participants through an iterative learning process. Because a CAS can quickly learn and adapt and is capable of efficiently aggregating the collective intelligence of its many participants, it is a far better organizational model for knowledge workers.
Examples of CAS concepts
Sub-optimal: A CAS does not have to be perfect in order for it to thrive within its environment. Any energy spent on being better than “good enough” is wasted energy. Once it has reached the state of being good enough, a CAS will trade off increased efficiency every time in favour of greater effectiveness.
Connectivity: The ways in which the agents in a system connect and relate to one another is critical to the survival of the system, because it is from these connections that the patterns are formed and the feedback disseminated.
Simple rules: Complex adaptive systems are not complicated. The emerging patterns may have a rich variety, but like a kaleidoscope, the rules governing the function of the system are quite simple.
Edge of chaos: A system in equilibrium does not have the internal dynamics to enable it to respond to its environment and will slowly (or quickly) die. A system in chaos ceases to function as a system. The most productive state to be in is at the edge of chaos where there is maximum variety and creativity, leading to new possibilities.
Nested system: Most systems are nested within other systems, and many systems are systems of smaller systems.