Month: June 2019

(P2): Surprise and Weak Signals

A goldfish with a look of surprise on the face.

  • Learning By Surprise
  • What is the Adjacent Possible?
  • The Hindsight Bias
  • “I wanted a perfect ending. Now I’ve learned, the hard way, that some poems don’t rhyme, and some stories don’t have a clear beginning, middle, and end. Life is about not knowing, having to change, taking the moment and making the best of it, without knowing what’s going to happen next. Delicious Ambiguity.”
    ― Gilda Radner

Instead of glossing over surprises as failures of understanding, we should focus on them until we have grasped their novelty and how that novelty needs to change our view of reality. We need to avoid abstracting from surprise to make it only another example of what we already know to be true.

Novel occurrences are novel for us, but they are also typically some “next step” from that with which we are already familiar. They are often called the “adjacent possible” because once they have occurred, it is fairly easy to see how they came about. This is true even if no one anticipates them. It is important to remember that in a Complex Adaptive System, there are always many adjacent possibilities for the future.

There is another common problem that results from rationalizing surprises. We look back on the surprise and try to figure out who accurately anticipated it. We think this will improve our prediction capability in the future.

Looking back does improve our understanding of the current situation. It doesn’t improve our ability to predict any genuinely novel future. If we examine what people thought about the future before the novel occurrence, we will see a very large number of ideas about what might happen.  The particular idea about the future that turned out to be accurate had no more or less information about its likelihood than many of the other ideas. The novelty tells us something useful about the current state of the CAS we are in and where it might evolve in the short term. It doesn’t improve our ability to foresee the genuinely new.

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(P2): Perceiving Weak Signals Overview

A woman with bruising injuries on her face from partner abuse, holding a sign that says 'Did you notice me?'

  • Great things are done by a series of small things brought together – Van Gogh
  • Even the largest avalanche is triggered by small things –Vernor Vinge
  • You’ve got to think about big things while you’re doing small things so that all the small things go in the right direction –Alvin Toffler
  • Men don’t pay attention to small things – Katherine Johnson

Because our usual habits make us ignore weak signals, we need to cultivate new habits that make it more likely we will notice them. These new habits can’t be automatic. They must involve reflective attention-not just attention to something that is there, but consideration of it’s meaning. Below is a list of techniques and concepts that I hope will aid you in seeing what is important, but almost not there:

  • Surprise Can Point to Weak Signals
  • Environmental Scanning for Weak Signals
  • Tinkering and Bricolage to find Weak Signals Right Around You
  • Learning About Weak Signals Through Safe-to-Fail Experiments
  • Thinking of Weak Signals as Insurgencies

I’ll try to amplify each of these in the posts below.

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(P2): So What Do We Do with Those Weak Signals

WhatToDoWithWeakSignals

First, we have to actually pay attention to them. Our default is to ignore them as unimportant. That means we have to have a way of making them stand out.  Most importantly, we have to conserve the meaning in the story of any weak signal instead of homogenizing that meaning or averaging it or abstracting it through ordinary statistical analysis. That is one of the strengths of SenseMaker. Its function is, first of all, to make raw weak signals stand out in a number of ways. We need to do the same.

Then, we have to ask ourselves about the value of the narratives we have acquired to support or undermine positive change. This isn’t simple to do. But our first order goal with these signals is to increase the ones that support positive change and decrease the ones that undermine it. Because these are weak signals, it is feasible for us to try out ways to do both of these in time frames that let us change our approach as we learn which weak signals we can effectively increase and decrease, and when we need to look at different initiatives to produce these outcomes.

The reason why this works at all in trying to change a CAS is that the cycle of experiment and evaluation is short. Such an approach respects the dispositional nature of CAS and doesn’t require us to use prediction and mechanical outcomes as the signs of progress.

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Part Two: Detecting and Using Weak Signals (Cynefin)

A Specimen Cynefin Diagram (not the newest, not the oldest). Simple / Obvious: The simple/obvious domain represents the 'known knowns'. This means that there are rules in place (or best practice), the situation is stable, and the relationship between cause and effect is clear. Complicated: The complicated domain consists of the 'known unknowns'. The relationship between cause and effect requires analysis or expertise; there are a range of right answers. The framework recommends 'sense–analyze–respond': assess the facts, analyze, and apply the appropriate good operating practice. Complex: The complex domain represents the 'unknown unknowns'. Cause and effect can only be deduced in retrospect, and there are no right answers. 'Instructive patterns ... can emerge,' write Snowden and Boone, 'if the leader conducts experiments that are safe to fail.' Cynefin calls this process 'probe–sense–respond'. Chaotic: In the chaotic domain, cause and effect are unclear.[e] Events in this domain are 'too confusing to wait for a knowledge-based response'. managers 'act–sense–respond': act to establish order; sense where stability lies; respond to turn the chaotic into the complex. Disorder / Confusion: The dark disorder domain in the centre represents situations where there is no clarity about which of the other domains apply.

Cynefin is a body of knowledge and tools to assist in changing CAS, among other things. Cynefin, as an enterprise intervention, also has developed a “narrative access and analysis tool” called SenseMaker™. Sensemaker allows the intervenors to accurately access raw views by the participants as short narratives without groupthink or homogenization. It is this ability that allows for the detection of weak signals.

Because SenseMaker has developed an app, it is possible for its users to engage huge numbers of people in a very short time. The example that had the most impact on my understanding of its capacities was an effort to work around the unwillingness of local citizens to say what they actually thought to US civil and military personnel in SE Asia.

The system was used to ask children to relate a story from their grandparents about the most important lesson that the grandparents had learned in their lives. Then the children sent the stories using the SenseMaker app. This project got 50,000 stories in four days.  There is simply nothing else that supports authentic narrative by real participants with the speed of SenseMaker.

Unfortunately for our community, SenseMaker is an enterprise tool and is priced that way. I have been exploring ways we might be able to use this system in our community, but I am some distance from a genuine solution.

That doesn’t mean that we can’t make use of the idea if we can come up with ways to assure fidelity to SenseMaker’s ability to easily access real raw narratives from participants.

I’ll discuss some ideas for using this general framework to get meaningful narratives in our community in later posts. For now, I hope you can see the importance of weak signals in the development and use of our FutureStrategy.

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