How do you effectively create impact and move fast in a complex world? I'm not going to lie, it's complicated. Fortunately, there are some amazing mental models that you'll want in your latticework so you can better engage with our complex, ambiguous and unpredictable world.
Let's start with embracing and thinking through complexity, perhaps the easiest to talk about and the hardest to do consistently. For that, some go-to models are:
- Map vs Territory
- Probabilistic Thinking
- Fishbone Diagram
- Stability Zones
UNDERSTAND & ACT
Next comes understanding and navigating complexity. Here I'll suggest the following powerful models:
- Scientific Method
- Cynefin Framework
- Double-Loop Learning
WATCH YOUR BACK
Finally, and overlapping with the previous models, how do you take action with a high degree of confidence? And no, not total confidence - as per my reference to probabalistic thinking earlier ;).
To that end, I believe when acting in complexity it's essential to 'watch your back' or consider what might go wrong — a quick nod to inversion, even though it didn't make the cut. The two models that I went with are:
- Second Order Thinking
- Risk Matrix
Here's what that all looks like in a 'latticework snapshot'.
BUT WAIT, THERE'S MORE
A quick disclaimer — most of the models in ModelThinkers are designed to combine into a Latticework of Models to navigate complexity, but the ones I've listed above are certainly strong contenders to make a 'top list'.
I'll be posting alternative combinations on this topic in the future but, for now, let's jump into each of the models I've mentioned in more detail:
Such a fundamental model — it seems a bit tacky including it in a list when it should be featured by itself. You can probably tell it's one of my favourites. And more needed than ever in the context of complexity.
Is this talk of complexity and ambiguity stressing you out? Particularly in contexts related to massive transformation or change, you might want to include this model in your 'complexity toolkit'. Inspired by Alvin Toffler, it guides you to areas of stability as you face into change and information overload elsewhere in your life.
Probabalistic thinking is about embracing, even approximating, uncertainty. It's a way of thinking and a mindset, as much as a model. Unfortunately, it tends to run counter to common expectations and mainstream media coverage.
After straying into Cynefin territory, I'd thought I'd go with something immediately practical, tangible and actionable. Of course, Prototyping can be viewed as part of the Scientific Method and the Cynefin framework, but it's a useful reminder to test things out. Want to extend a bit further? Combine prototypes with Split Testing.
One of the fundamental models behind ModelThinkers and skeptical thought, this model will ensure that you are never 100% sure in any of your beliefs or ideas again. That's actually more useful than you might think, especially when you relax into it as a way of life and thinking — and continue to take action and be optimistic through ambiguity. Which leads nicely to the next model.
Here's one that you might not be using yet. At it's simplest level it can be a guide to applying appropriate methods to varying contexts... but it can get deeper than that. View the model's link and go back to Snowden's resources to immerse yourself in this potentially powerful approach — inspired by the Scientific Method and with links to Agile Methodology.
Creating feedback loops of action is a start, but also be sure you take time to learn deep lessons and challenge your fundamental mental models before taking action once more. This model will help.
Use this tool to try and make sense of complexity, by mapping cause and effect relationships on a range of criteria. Remembering the Map vs Territory model, this will be inadequate to explain reality, and it still. might be useful.
At it's simplest, this model encourages you to ask 'what would happen next?' It's an important reminder and even safeguard through complexity, where a focus on the immediate challenge or decision point can obscure unforeseen implications down the line. The irony? In complexity it's even harder to identify potential second order consequences, let alone beyond that.
If you're working in complexity, you're going to have to risk being wrong and making mistakes. Rather than let that fact freeze you, just consider possible implications and their likelihood to manage and mitigate risks.
That's it for this exploration into complexity and decision making. What resonates for you? What would you add? What's been your experience? Let us know in the comments below.