LeverPoint


About LeverPoint

GrowthLeverPoint is devoted to the synthesis of System Dynamics, Complexity Science, Network Theory, and non-Aristotelian thinking. Examples of trans-disciplinary applications might include performance management, business process optimization, organizational design, solution synthesis such as integration of multiple intelligences theory with organic feedback models in education, and possibly even development of lever points for manipulating physiological variables for disease prevention or athletic training and performance. Essentially anywhere you have system complexity and problems to solve.

Problem Solving

In any problem solving environment (such as business, politics, or any society of autonomous agents) the destructive solutions are easy enough to find. These are often the “obvious” answers. If you have a problem with compliance, then obviously a training solution is what you need right? Not always. The destructiveness of win-lose solutions might not be obvious at first. Sometimes they are destructive only in that they fail to address or solve the real issues. You might even win in the short term, only to find that the problems come back later, even larger.

Not all problems can be solved by analysis and planning. Some problems are empirical in nature. You need some kind of hands-on, or simulated try-and-see capability.

The ability to rapidly create complex simulations for scenario analysis could provide the opportunity for all sides to recognize win-win solutions instead of only the destructive ones. But when cause and effect are widely separated in space and time, how can you simulate the system, identify the core problems within it, and possible solutions?

Systems methods can provide exactly that. And Complexity Science provides even more models for devising organic, evolving solution structures.

Cause and Effect

Finding a lever point in a system is like developing a vaccine. Suddenly you can make a small change that creates a large effect. To see the lever points, we need to identify feedback loops that drive separation of cause and effect. Feedback loops create illusions like seasonal behavior within systems. This makes it challenging to predict the consequences of modifications to systems.

There Are No Attributes

We also need to realize that since abstract attributes do not exist in reality, solutions that try to deal with abstract attributes only, are not founded in reality. Attributes are only relevant within the context of a given system. While this seems obvious, people still make decisions that are completely dependent on the unstated viewpoint that attributes are in some way “real”. Further, they believe that pushing harder on an “attribute” like “presentation skills” or “efficiency” can improve “it” and generate “performance gains”.

Now that I have put that in print, some folks will interpret this to mean that such things do not “count”. This misses the point though. It is the taking them as real, and making decisions on that supposition that I am talking about. The attributes are only “real” within a narrow context. Outside of that context, they are not real, and they do not exist in some separate, archetypal form . So to change an attribute, you do not push on the attribute…you push on a lever point within the system that pulls the attribute in the direction you want it to move.

Part of what drives such Aristotelian modes of thought is the way we approach metrics. Metrics are devoid of context. Without context you have disembodied “attributes” such as defect-per-thousand-units, or net profit. What we might lose sight of is that metrics are useful in identifying impact, or even areas of investigation. Not so useful is to apply statistical reasoning to an individual agent or other subset of the system. If we do, then our models, simulations, and knowledge creation suffer.

Knowledge Creation

And so we head into the world of systems! You are invited to participate in this journey. No one has all the answers. Knowledge creation requires dialog, and the more the better. The result? You might start seeing systems everywhere you look!

Continue the Journey:

There Are No Attributes

Bibliography

Glossaries

© Copyright 2008 Keith Sherwood

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