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Complex systems strategy: thinking aloud

February 13, 2008

I have been playing around with a way to describe a complex system in such a way that you ca move to a management approach which has some coherence. It is still in its early stages, but it is starting to inform the design of narrative databases and I have used it experimentally with some success. I am, as a part of that starting to introduce a vocabulary for a range of intervention methods (and software variants to SenseMaker™). This is a balance between being accurate/authentic and being comprehendible to managers who do not have to invest in a detailed understanding of complexity theory. So for comment but please no derision, here it is (and it assumes knowledge of CE methods and techniques)

First the metaphor: Imagine a ring of magnets of an irregular and fluctuating surface. The magnets can vary in strength and from time to time reverse polarity. Inside this ring there are a large number of iron particles, that can in term be magnetised and attract each other. As the magnets change in strength the patterns of interaction of the iron particles changes with patterns forming, and at times stabilising those patterns in their turn. From time to time the patterns are radically disrupted by a phase shift in one of more of the magnets, but settles back. Causality in a linear sense does not exist, some magnets have more influence that others, but possibly only because of proximity, and resonant effects with other magnets.

So, starting to think about strategy; in any system we have a range of entities (individuals, ideas, physical forces etc) which are in various ways influencing and impacting events and each others. We can call these modulators and we can map them. Conventional consultancy, narrative enquiry, future backwards and other technique allow this. We can for each modulator identify what we know about it, what we know about its past changes, how it might change in the future (we might even create scenarios at this level) and we can also identify some, or maybe all of its proximate effects on adjacent agents.

landscape
Modulators then become the basic entities around we construct the tagging systems for a narrative database. By gathering large volumes of narrative indexed around those modulators we can map the impact of those modulators, and using fitness landscapes as illustrated to identify the stabilities that form, and with fragmented scenarios we can also model future states and see the impact on those landscapes of changes in modulator strength. The attractors represented as hollows in the landscape are stabilities. Initially sensitive to modulator change, as they become stronger modulator variation has to be more extreme to achieve any change. The peaks are instabilities, areas of potential change and of possible violent and unpredictably fluctuation.
Strategy is then understanding the stability of the system, carrying out safe-fail experiments to test the possibilities and sensitivities of the system, monitoring for evolution. Creating barriers, or fire breaks to reduce the influence of some modulators.

In modeling we also see how much of historical data (and future possibilities) we can account for by our modulator maps. The unaccounted is our dark matter quotient - an assessment of risk.

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