Jan Bosch is director of the research center, professor, consultant and angel investor in startups. You can contact him at jan@janbosch.com.

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The traditional way of organizing companies was in functional departments, where people with the same skills and training could focus on specific challenges, solve them, and then pass the result to the department that integrates all parts of all functions into one working system. The perceived benefit was that each expert could only focus on his domain and not worry about anything else.

In practice, this model did not work very well and the cost of coordination between teams increased slightly as each function made all sorts of assumptions about the other parts of the system that generally turned out not to be accurate because of those assumptions based historical data. As the saying goes, adoption is the mother of all mistakes.

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As individuals, we tend to take the same approach. Whenever we need to solve a problem, we look for ways to reduce it to the essentials and ignore anything that we don’t think is relevant. The challenge is that most of us tend to ignore more than is actually relevant.

This is problematic for at least two reasons. First, in a digital world, the pace of change is much faster than before and continues to accelerate. As a result, things that you previously thought were stable are likely to change now as you focus on the challenge at hand. The risk is that if you solved the problem with a nice, elegant solution, the problem itself has shifted and the solution no longer fits the context because it is assumed that there are no more interfaces.

A fine example in the automotive industry is the CD player. For years, automotive companies have been looking for reliable solutions to store multiple CDs in the infotainment system and allow drivers to access as much music as possible. Just as good solutions became available, the industry switched to Bluetooth access and cell phone-based streaming solutions. We now all listen to Spotify while driving instead of juggling CDs.

Second, we tend to underestimate second-order effects. Whenever we develop solutions, we usually focus on the particular result we want to achieve, that is, the first-order effect. However, especially in complex systems, the effect of actions is not easy to predict, and the result can easily be the opposite of what was intended.

The world is full of examples of unintended consequences. Most historians consider the Versailles Treaty at the end of World War I to be the root cause of World War II, even if it was to ensure that Germany would be restricted and not be able to wage war. Another illustrative example is Mao’s four-pest campaign in China, in which the population was asked to exterminate sparrows because they ate seeds after they were planted. The result was plagues of locusts in which entire harvests were lost. Both examples resulted in millions of deaths.

A controversial current example is the government’s approach to combating Covid-19. With entire societies closed and all medical workers focused on treating corona patients, many wonder what secondary effects preventable deaths from untreated diseases like cancer have, as well as poor mental health and increased suicide rates due to the lack of human contact. Could it be that the deaths and deteriorated quality of life due to the secondary effects are exceeding the lives saved by downtime and the concentration of the entire medical facility on Covid?

The best approach is threefold: scope, humility, and experimentation

I believe the best approach to dealing with these issues is threefold, that is, scope, humility, and experimentation. The first thing you need to do when facing a challenge is to make sure that you don’t inadvertently narrow the problem into too narrow a focus. As Einstein famously said, you should make everything as simple as possible, but not simpler. In most cases, this means adding more to your scope than you traditionally would have, because everything changes so quickly.

Second, we need to exercise humility. Instead of assuming we know, it is wise to accept that most of the time we actually don’t know anything. It does so because there is much more to know than what our poor brain can contain, and because many things are just not apparent until they happen. Look no further than the stock market: even people who have worked in the industry for decades and spend all of their time in the market can’t beat the market in the long run. Of course there are exceptions like Warren Buffet, but these are exceptions that prove the rule. The Mahn-Krüger effect shows not only that newbies tend to overestimate their skills, but also that real experts know how little they know about their subject.

Third, the better approach is to figure out how to conduct experiments to test the effectiveness of certain measures, rather than taking decisive action in the face of uncertainty. One example is the Peltzman effect, in which, for example, regulations to increase road safety lead to road users taking more risks with increasing perceived safety. Many laws and regulations do not have the effect intended by the legislator, but are neutral or negative due to secondary effects. In many cases, the only way to understand the consequences of an action is to conduct the experiment, learn from it, and then decide what to do next.

In a digital world, the pace of change is fast and increasing. As a result, factors that could previously be considered stable enough to be ignored when tackling a challenge need to be included in the scope. It is therefore important to think holistically, to accept that there is much that we do not know or cannot know at all and, if possible, to experiment to understand whether the intended effects are actually achieved and negative consequences are avoided. The road to hell is paved with good intentions, but I don’t think either of us wants to end up there.

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