yes, all models are wrong

“Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful.”George Box

So how do we know when a model — particularly one of our preferred mental models — is wrong? It is difficult to change our mind but that is what any good professional has to be able to do. Consider one of the prevailing battles in our understanding of the coronavirus.

The World Health Organization, which many governments follow in making policy, has admitted that airborne spread is possible, but stops short of saying it’s the dominant mode of spread.

Dr. David Fisman, an epidemiologist at the University of Toronto’s Dalla Lana School of Public Health and one of the co-authors of the Lancet paper, says this distinction matters in order for people to take the necessary precautions to keep themselves safe.

He said contrary to what he told Quirks & Quarks host, Bob McDonald in February 2020, he now believes the virus is primarily spread via tiny aerosol particles, and the Lancet article lays out the evidence that changed his mind. —CBC Q&Q 2021-04-23

According to Derek & Laura Cabrera, “wicked problems result from the mismatch between how real-world systems work and how we think they work”. With systems thinking, there is constant testing and feedback between the real world, in all its complexity, and our mental model of it. This openness to test and look for feedback led Dr. Fisman to change his mind on the airborne spread of the coronavirus.

Leyla Acoroglu recommends six tools for systems thinking. Even using only some of them can help us better define problems and be open to a broader set of options. I have added some recommendations.

  1. Interconnectedness — Set up diverse sources of information. Use a tool like Twitter to get perspectives from different cultures, industries, countries, genders, and ages.
  2. Synthesis — Establish a way to put your thoughts together. It could be a synthesis of the tweets you found interesting on social media, such as my Friday’s Finds. You won’t find connections between the dots if there are no dots.
  3. Emergence — The more connections you make, on social media, while walking, or just sitting and observing then the greater the chances for emergence, or even serendipity.
  4. Feedback Loops — Engage with a diverse group of people. Get feedback not just from your peers but work in the open to get feedback from all corners. Blogging is the perfect medium for this.
  5. Causality — In complex systems we can determine the relationship between cause and effect only in retrospect. This means we have to first engage the system and then learn from it. The next time things may be different.
    In complexity we have to — Probe > Sense > Respond.
  6. Systems Mapping — System maps are great for sense-making. You won’t know how useful your systems maps are until you make them and use them. Be ready to discard them when no longer useful. Keep them in a state of perpetual beta.

In response to Pawel Szczesny’s question below, I would say that it is even more important today to understand the first part of that sentence — all models are wrong — and keep it foremost in mind.

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