Scenarios versus socio-ecological resilience

When uncertainty is high we often run scenarios to get a better understanding of how our systems might react to change or cascades of change.  I’ve used scenarios quite often myself in the past.  We develop some story lines, some possible drivers of change, and then we see what insights might emerge as we recognize how our systems might respond.

These are valuable and up until I got deep into socio-ecological resilience thinking, scenarios were one of the best tools I had to imagine change and to recommend multi-benefit solutions.  So scenarios are good!

In addition to scenarios, I’ve found that supporting and encouraging the capacity of a system/ecosystem/community/economy to adapt to change is a powerful way to move beyond scenarios.  And the way to do that is to incorporate the attributes of resilient systems.  You could then run scenarios to see how well your system might respond to various alternative futures.

Since I’ve worked on salmon issues most of my professional life, they are a great example of what this means.  Salmon do not project scenarios.  They do not calculate stream flows or plot the best way to avoid a log jam.  What they do is rely on a highly evolved life cycle that gives their species significant capacity to adapt to change.  We need our own evolution in how we manage cities and infrastructure, too.  We need to apply the attributes of resilience in how we design cities and infrastructure.  When we do then we can begin to find new solutions to old problems and to new problems that have yet to emerge.

To my surprise I recently learned that robots and artificial intelligence are moving down this same pathway.  Thanks to the interview and podcast with Nikos Salingaros, I learned that the Mars Rover and the iRobot vacuum cleaner use the same strategy as salmon: they have exquisite sensing capabilities and a suite of strategies and tactics to deploy when they encounter a novel situation. Flexibility and adaptability are the goal.

I’ve got nothing against scenarios or trying to calculate the probability of possible future events.  The point here is that those are not enough in a world that is synergistic and dynamic and constantly changing.  And the speed of change is accelerating as we reach tipping points, climate change, and new technologies that outpace our normal speed of accommodation.

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