(2024-05-01) Exploring Causality And Complexity In Strategy

Uncertainty Project: Exploring Causality and Complexity in Strategy. The idea of causality matters greatly, though, since as we apply our agency (as individuals, as organizations, and businesses) to take action, we base our choices on where we believe we can cause good things to happen.

When we shift from being data-centric to being decision-centric, we frame a situation or challenge first, and only then bring in the analysis and data.

When we set out to design a strategy, we consider various “levers to pull”, that will put the wheels in motion towards some desired outcomes. But this “lever” metaphor implies a cause-and-effect - it implies that causality is both understandable and understood in our context.

we tend to view these “levers” as the triggers of perfect machines - we talk with certainty about the effects we can cause with them

So without this categorization (in navigation capability 1), it’s easy to make some bad fundamental assumptions in strategy design:

So if we can’t talk about causality in our complex domains, what can we talk about? What can we use as “levers” to actively probe and navigate?

Alicia Juarrero, in her book “Context Changes Everything”, sought to explain how a context can exert influence on its component parts. She deliberately avoided the term “cause” to describe its impact, saying that causality had too much “baggage”. Instead she uses the concept of constraints to describe this influence.

she describes how constraints create coherence within the complexity: “Constrained interactions leave a mark. They transform disparate manys into coherent and interdependent Ones.

One example of how constraints surface in a business setting (and can also improve visibility of different domains), is Geoffrey Moore’s “Zone-to-Win” framework

Drive the Productivity Zone as a Complicated domain

Operate the Incubation Zone to run safe-to-fail experiments as a Complex domain

What’s interesting is that each zone typically operates in a different Cynefin domain

Stabilize the Performance Zone to behave closer to a Clear domain

in a context, these kinds of constraints (e.g. zones) can steer a different strategic approach into each zone:

When it's Complicated, we might analyze our way to some best choices, make decisions, and move forward with plans. When the world is changing quickly around us, though, we might have to admit that our efforts at improving productivity have shifted into a Complex domain. In this situation, we need more of an experimental mindset that seeks shorter feedback loops to “probe-sense-respond” and look for emergent patterns. (complex system)

In both cases, it’s good for a leadership team to use visual aids like Causal Decision Diagrams to help them develop a shared understanding.... “Simply drawing good collaborative pictures of our ‘common sense’ understanding of causation is such a great step forward that we shouldn’t allow formal theories of causation to get in the way. This can be dangerous though, so an important direction for DI (decision intelligence) is to translate formal causation theory into a form that can be used for non-technical practitioners.” ((2024-05-01) Causal Decision Diagrams)

In a Complex domain, you’ll want to encourage divergent thinking, and explore many angles for probing the system.

Data

when the causal diagrams are combined with the data sets (and some new theories on causality), it can offer data-driven support for decision-driven causal arguments.

If you are a leader exploring a challenge categorized as a Complicated domain, then as you look for “levers to pull”, you could capture them as causal diagrams

This marriage of causality and statistics was the life’s work of Judea Pearl, author of “The Book of Why”. He found ways to apply statistical techniques to explore and support hypotheses with a combination of causal diagrams and data.

He also offers a beautiful description of causality, as “listening”: When Y “listens” to X, then there is a causal relationship from X -> Y

Many of his examples came from medical contexts, where, for example, the causal relationship between symptoms and a disease are being investigated. In our business contexts, the analogous case might be to explore causal relationships between various contributing factors (symptoms) and the key challenge (disease). This hints at how sophisticated data analysis and Pearl’s causal algebra can be leveraged in a strategic decision architecture. Notice how the flow leads with questions and assumptions, and uses the data to serve the pre-existing hypothesis.