Data has become incredibly sought-after – so much so, that the promise of its value has driven a present-day version of gold fever. These are two common assumptions when it comes to data: more is better, and something is better than nothing. Unfortunately, these assumptions invite what professor Jerry Z. Muller has termed the Tyranny of Metrics. Despite our best intentions, when we carelessly measure the wrong things and make decisions based on that data, it can cause far more harm than good.
For this reason, when activating behavior change in our programs, we focus not on designing for data but rather on designing for evidence.
While all evidence is data, not all data is evidence. Identifying what justifiably counts as evidence is a careful and systematic process. And designing to capture that evidence requires diligence and rigor.
Many organizations simply pay lip service to evidence (and science, more generally). This article will delve into one of the methods we use to design for evidence: mechanistic explanations.
What Is a Mechanistic Explanation?
At Cognician, we take a mechanistic explanation approach when designing challenges and reflections. We do this to provide reliable evidence of behavior change. But what exactly is a 'mechanistic' explanation, and how is it different from the types of explanations we commonly use?
To illustrate our point, let's use the example of a self-winding watch. How would a regular person on the street explain how it works? They'd probably say something like, "You move your arm, which winds the spring so that it keeps time."
In contrast, how would an expert watchmaker explain how it works? Well, the explanation might go something like this, "The watch consists of a 'rotor' or 'oscillator' that is powered by the movement of the wearer's wrist. As the wrist moves, it automatically moves the rotor, which, as it swings, winds the mainspring inside its barrel ..."
What makes the watchmaker's explanation different is that it accounts specifically for how all the parts fit together in a causal system to produce the desired behavior of the watch – reliably keeping time. Furthermore, the explanation accounts for all the pertinent parts, relationships, and interactions.
What Is the Value of a Mechanistic Explanation?
While a typical explanation oversimplifies and glosses over causal relationships, a mechanistic explanation does the opposite. By using mechanistic explanations when designing our learning programs, we are able to craft more effective behavior change interventions. This brings us to the final question. What can a watchmaker do that the regular person on the street cannot? They can craft a watch that works, fix it when it's broken, and adapt it to work as intended under different conditions. That's what we are able to do with our challenges and reflections.
To learn more about how we can help you activate change in your organization with expertly designed programs, book a call today.
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