I haven’t been very actively writing here recently. I think I got trapped in the question ‘what meaningful could I write that is not already out there?’. Well, anyway, just a short post today with some thoughts that I have carried around for a while. I just came across a post on the Aid on the Edge blog that was talking about South Africa and the uncertainty of its future:
We human beings do not like uncertainty. We seek to understand what events portend, taking comfort in coming up with an answer. (…) Yet sometimes there is more wisdom, and more comfort to be taken, in acknowledging a more humbling truth – that which of many alternative futures (including ones we cannot imagine) will come to pass is unknowable, is a product of decisions and actions that have not yet been made. This understanding of change as something ‘emergent’, evolving, which can unfold in far-reaching yet ex ante unpredictable directions, is the key insight of ‘complexity theory’ – an insight which can offer a useful dose of humility to governance prognosticators.
The question that comes to my mind when reading this is how to handle the tension of the uncertainty of the future and the deeply institutionalized need for planning in development institutions.
I have worked with a systems dynamics approach combining causal loop diagrams with a method called the sensitivity analysis. It helps us to determine the relative importance of impact factors in a system and characterize them as active, critical, passive, and buffering. Together, these two tools allow to select impact factors that could be targeted by development agencies in future projects.
Now, what is the value of causal loop diagrams? Some people say that they are not more than an improved version of linear causal chains, but still not able to reflect ‘real’ complexity, i.e., the unpredictability of complex systems. Loop diagrams still work with cause and effect relationships, the cause and effect between two factors that can be connected with other factors to eventually build a loop. Yet, complexity sciences say that in complex systems cause and effect are hard to determine, so why bother?
I think that the causal loop analysis and the sensitivity analysis allow us to evaluate the factors that are most relevent and focus on them. They further illustrate some of the more prominent feedback mechanisms of the system that could amplify or hamper our interventions or that we could even use to change some of the dynamics of the system in our favor. They also cater the need of planning or at least of establishing a rational base for planning.
But yeah, we have to avoid falling back into the ‘we can predict the future’ trap, trying to build a prefect model of a system (just remember, models of complex systems have to be as complex as the real thing to accurately simulate it). Complex systems remain inherently unpredictable and our actions need to be tuned to the reactions of the system to any intervention. The above mentioned tools help us to make sense of the dynamics of a system and to select the more promising interventions. They do, however, not release us from the need of an experimental (or may I call it evolutionary) approach to solving real problems in real systems.
I would appreciate any thoughts on that in the comments!
Hallo Marcus
Das gefällt mir sehr, was Du da schreibst, und ich bin ganz Deiner Meinung. Sicher sind CLD nicht einfach “eine Fortsetzung linearen Denkens” oder eine Fortsetzung des “laundry list thinking”. Lineares Denken ist nicht in der Lage, feedback loops zu bilden.
Allerdings müssen wir aufpassen, dass wir nicht aus lauter Ehrfurcht vor Komplexität, Ungewissheit, Konstruktivismus, Veränderungsnotwendigkeit und Ambiguitätstoleranz in die Knie gehen. Ein Gehirn funktioniert nun mal so, dass es laufend versucht, die Zukunft zu prognostizieren. Anders könnten wir gar nicht leben. Warum hast Du denn einen Weihnachtsbaum gekauft? Weil Du “weisst”, dass übermorgen Weihnachten ist und zumindest die Kinder einen Lichterbaum sehen wollen. Das ist ja nichts anderes, als die Zukunft vorwegzunehmen.
Entscheidungen werden vor dem Hintergrund einer angenommenen Zukunft getroffen. Es nützt nichts zu sagen: “Freunde, vergesst es, die Zukunft zu prognostizieren! Macht es wie die Vögelein: diese jubilieren im Jetzt ohne sich darüber Sorgen zu machen, ob sie morgen etwas zu fressen finden” (entsprechend irgend eines Bibeltextes). Ein Manager, der so handelt, würde nicht lange überleben.
Es gibt verschiedene Prognosestrategien. Wenn ich z.B. die Vergangenheit extrapoliere, ist das sicher grundfalsch. Wenn ich die Extrapolation aber bloss für ein kleines Zeitintervall mache, dann kann ich gewiss sein, dass sie in der Mehrheit der Fälle stimmt. Ab und zu werde ich grässlich überrascht, weil in diesem Zeitintervall etwas Unerwartetes passiert, aber nur in den selteneren Fällen.
Mein Ratschlag ist daher: Prognostiziert ruhig! Seid aber auf der Hut vor Unerwartetem und lernt, damit umzugehen!
Gruss und frohe Festtage,
Peter
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I quite like how John Kay argues in “Obliquity – Why our goals are best achieved indirectly”: Planning should be indirect, so maybe planning for the future shouldn’t be about the future? Apparently this mindset seems to be in line with what Steve Jobs describes as “how it’s only possible to connect the dots afterwards”. Beforehand when you created the dots or when they appeared in your life you didn’t expect (or plan) that there will be connections between them later on.
Hello Marcus, thanks for the interesting post. Do you know of any good articles exploring the relationships between evolutionary and systems thinking?
Cheers,
Alesh
Hi Alesh.
Thanks for your comment. An evolutionary approach seems to me something very natural in a complex world. Manly two books I read recently inspired that thinking: ‘Complexity – a Guided Tour’ by Melanie Mitchell (on which I posted a review here) and Eric Beinhocker’s ‘The Origin of Wealth’, in which he describes evolutionary approaches as centerpeace for business planning in a complex economy.
Maybe others have some articles that specifically target evolutionary approaches in complex environments?
Best, Marcus