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On Epiphany and Apophany by Liz

Published Wed 9 Sep 2015 11:14

We probe, then sense, then respond.

If you’re familiar with Cynefin, you know that we categorize the obvious, analyze the complicated, probe the complex and act in chaos.

You might also know that those approaches to the different domains come with a direction to sense and respond, as well. In the ordered domains – the obvious and complicated, in which cause and effect are correlated – we sense first, then we categorize or analyze, and then we respond.

In the complex and chaotic domains, we either probe or act first, then sense, then respond.

Most people find action in chaos to be intuitive. It’s a transient domain, after all; it resolves itself quickly, and it might not resolve itself in your favour… and is even less likely to do so if you don’t act (the shallow dive into chaos notwithstanding). We don’t sit around asking, “Hm, I wonder what’s causing this fire?” We focus on putting the fire out first, and that makes sense.

But why do we do this in the complex domain? Why isn’t it useful to make sense of what we’re seeing first, before we design our experiments?

As with many questions involving human cognition, the answer is: cognitive bias.

We see patterns which don’t exist.

The term “epiphany” can be loosely defined as that moment when you say, “Oh! I get it!” because you’ve got a sudden sense of understanding something.

The term “apophany” was originally coined as a German word for the same phenomenon in schizophrenic experiences; that moment when a sufferer says, “Oh! I get it!” when they really don’t. But it’s not just schizophrenics who suffer from this. We all have this tendency to some degree. Pareidolia, the tendency to see faces in objects, is probably the best-known type of apophenia, but we see patterns everywhere.

It’s an important part of our survival. If we learn that the berry from that tree with those type of leaves isn’t good for us, or to be careful of that rock because there are often snakes sunning themselves there, or to watch out for the slippery moss, or that the deer come down here to drink and you can catch them more easily, then you have a greater chance of survival. We’re always, always looking out for patterns. In fact, when we find them, it’s so enjoyable that this pattern-learning, and application of patterns in new contexts, forms the heart of video games and is one reason why they’re horribly addictive.

In fact, our brains reward us for almost seeing the pattern, which encourages us to keep trying… and that’s why gambling is also addictive, because a lot of the time, we almost win.

In the complex domain, cause and effect can only be understood in retrospect.

This is pretty much the definition of a complex domain; one in which we can’t understand cause and effect until after we’ve caused the effect. Additionally, if you do the same thing again and again in a complex domain, it will not always have the same effect each time, so we can’t be sure of which cause might give us the effect. Even the act of trying to make sense of the domain can itself have unexpected consequences!

The problem is, we keep thinking we understand the problem. We can see the root causes. “Oh! I get it!”… and off we blithely go to “fix” our systems.

Then we’re surprised when, for instance, complexity reasserts itself and making our entire organization adopt Scrum doesn’t actually enable us to deliver software like we thought it would (though it might cause chaos, which can give us other opportunities… if we survive it).

This is the danger of sensing the problem in the complex domain; our tendency to assume we can see the causes that we need to shift to get the desired effects. And we really can’t.

The best probes are hypothesis-free.

Or rather, the hypothesis is always, “I think this might have a good impact.” Having a reasonable reason for thinking this is called coherence. It’s really hard, though, to avoid tacking on, “…because this will be the outcome.” In the complex domain, you don’t know what the outcome is going to be. It might not be a good outcome. That’s why we spend so much time making sure our probes are safe-to-fail.

I’ve written a fair bit on how to use scenarios to help generate robust experiments, but stories – human tales of what’s happening or has happened – are also a good way to find places that probes might be useful.

Particularly, if you can’t avoid having a hypothesis around outcomes (and you really can’t), one trick you can try is to have multiple outcomes. These can be conflicting, to help you check that you’re not hung up on any one outcome, or even failure outcomes that you can use to make sure your probe really is safe-to-fail.

Having multiple hypotheses means we’re more likely to find other things that we might need to measure, or other things that we need to make safe.

I really love Sensemaker.

Cognitive Edge, founded by Dave Snowden of Cynefin fame, has a really lovely bit of software called Sensemaker that collects narrative fragments – small stories – and allows the people who write those stories to say something about their desirability using Triads and Dyads and Stones.

Because we don’t know whether a story is desirable or not, the Triads and Dyads that Sensemaker uses are designed to allow for ambiguity. They usually consist of either two or three things that are all good, all bad or all neutral.

For instance, if I want to collect stories about pair programming, I might use a Dyad which has “I want to pair-program on absolutely everything!” at one end, and “I don’t want to pair-program on anything, ever,” at the other. Both of those are so extreme that it’s unlikely anyone wants to be right at either end, but they might be close. Or somewhere in the middle.

In CultureScan, Cognitive Edge use the triad, “Attitudes were about: Control, Vulnerability, or Indifference.” You can see more examples of triads, together with how they work, in the demo.

If lots and lots of people add stories, then we start seeing clusters of patterns, and we can start to think of places where experiments might be possible.

A fitness landscape from Cognitive Edge shows loose and tightly-bound clusters, together with possible directions for movement.

A fitness landscape from Cognitive Edge

In the fitness landscapes revealed by the stories, tightly-bound clusters indicate that the whole system is pretty rigidly set up to provide the stories being seen. We can only move them if there’s something to move them to; for instance, an adjacent cluster. Shifting these will require big changes to the system, which means a higher appetite for risk and failure, for which you need a real sense of urgency.

If you start seeing saddle-points, however, or looser clusters… well, that means there’s support there for something different, and we can make smaller changes that begin to shift the stories.

By looking to see what kind of things the stories there talk about, we can think of experiments we might like to perform. The stories though have to be given to the people who are actually going to run the experiments. Interpreting them or suggesting experiments is heading into analysis territory, which won’t help! Let the people on the ground try things out, and teach them how to design great experiments.

A good probe can be amplified or dampened, watched for success or failure, and is coherent.

Cognitive Edge have a practice called Ritual Dissent, that’s a bit like the “Fly on the Wall” pattern, but is done in a pretty negative way, in that the group to whom the experiment is being presented critiques it against the criteria above. I’ve found that testers, with their critical, “What about this scenario?” mindsets, can really help to make sure that probes really are good probes. Make sure the person presenting can take the criticism!

There’s a tendency in human beings, though, to analyze their way out of failure; to think of failure scenarios, then stop those happening. Failure feels bad. It tells us that our patterns were wrong! That we were suffering from apophany, not epiphany.

But we don’t need to be afraid of apophany. Instead of avoiding failure, we can make our probes safe-to-fail; perhaps by doing them at a scale where failure is survivable, or with safety nets that turn commitments into options instead (like having roll-back capability when releasing, for instance), or – my favourite – simply avoiding the trap of signalling intent when we didn’t mean to, and instead, communicating to people who might care that it’s an experiment we want to try.

And that it might just make a difference.


This article was originally published here.