Heuristics vs. Best Practices

In this post, I’d like to explore an important anti-pattern that I’ve seen emerging more and more recently, using Dave Snowden‘s classic piece:

A Leader’s Framework for Decision Making

The anti-pattern that I’ve observed is fairly straight forward: in a genuine attempt to foster learning and help people avoid past mistakes in dealing with a complex challenge practices rather than principles are being ascribed, leading to sub-optimal outcomes.

Let’s start with a more concrete definition of the two terms in the title of this post:

  • Best Practice – a procedure that is prescribed as being correct or most effective
  • Heuristic – a loosely defined rule (“rule of thumb”)

As well as Snowden’s definition of three types of problems (there’s also a fourth that is less relevant to this post):

  1. Simple problems – clear cause-and-effect relationships are evident to everyone; a right answer exists; known knowns; Example: solving a simple arithmetic problem
  2. Complicated problems – expert diagnosis required; cause-and-effect relationships are discoverable but not immediately apparent to anyone; more than one right answer exists; known unknowns; Example: diagnosing a car malfunction
  3. Complex problems – no right answers; emergent instructive patterns; a need for creative an innovative approaches; unknown unknowns; Example: forecasting the weather

As Snowden points out, (best) practices make sense only in the first two types of problems, the simple and complicated. They differ from one another by the level of expertise required to uncover the relationship between cause-and-effect, but once you have, following the best practice algorithm/checklist will lead you to the best possible outcome.

In complex problems, best practices don’t work. Solving complex problems is all about the unique context that you’re currently in, and what was the best solution to this problem in one context/situation may not be the best way to solve it in another. This is the realm of heuristics and principles, pointing you in the general direction of ways to classify the problem, and general approaches that seem to work for a specific pattern. But they cannot ascribe a specific solution that’s guaranteed to work.

Almost any problem that involves humans tends to be a complex problem. As multiple research papers seems to suggest, when it comes to human behavior, the relationship between cause-and-effect often times are murky at best, putting it squarely in the “complex” category.

And this is what I believe most people seem to (implicitly) fail to consider, when they ascribe a “best practice” solution to a human-oriented problem…


Heuristics vs. Best Practices

3 thoughts on “Heuristics vs. Best Practices

  1. I agree that best practices and heuristics feel safe, but can lead us away from the actual best solution for a problem. However, I do believe there are particular practices and heuristics that can prime you to discover how complex systems work. Maybe they are better termed as mindsets and behaviors?

    For example, rich picture exercises and user research are practices that help discover complex relationships between people.

    The hardest thing I have found when applying the Cynefin framework is getting agreement within the team which region we are actually in for a problem. If you think you are in complex territory, but are actually in complicated you waste time validating what you already know. If you think you are in complicated territory, but are actually in complex, you have the problem you describe.


    1. I was trying my best to avoid the terminology debate. You can replace “heuristics” with “principles” or “behaviors”. At the end of the day, the point is that an algorithmic checklist of actions is unlikely going to help you find a solution to a complex problem. You have to go one level of abstraction up to heuristics/principles/behaviors.
      You’re 100% right that both Type I and Type II errors exist in classifying problems as “complex” or “complicated”. In my subjective experience, I’ve seen more cases of misclassifying a complex problem as complicated and trying to solve it in over-simplistic tools, than the opposite.


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