In 1957, the Russians threw a two-foot diameter ball into outer-space and the world was forever changed. Sputnik was the first man-made object to conquer space and thus opened the potential for a virtually limitless set of explorations. The Sputnik satellite was the starting gun for the space race and led to the public’s fascination with all things scientific in a faddish manner that had never been experienced before or since.


In 1957, the world was also still aglow in the economic expansion that peacetime after World War II had brought. Scientific management as first proposed by Frederick Taylor at the turn of the century, and then in the 1950’s popularized by leading management superstars like Robert McNamera, coupled with the rise of business schools, nicely complemented this economic expansion and made it difficult to ascertain which was the leading factor; was the economic expansion a consequence of scientific management and the rise of business schools, or was scientific management and the rise of business schools a consequence of the economic expansion?

The reality is that Sputnik and scientific management had a lot in common; they were both associated with scientific progress, and they both made it appear that all problems could be solved with nothing more than a combination of scientific knowledge and some brainpower. It was an era that inspired Timbuk3’s song lyrics “I studied nuclear science … future’s so bright I gotta wear shades”.

As an undergraduate student I studied chemistry and physics – (and of course, I wore shades!). When I was wearing my lab coat on campus, I was always intrigued that everyone, including my friends who knew I was a dufus, would treat me with a different level of respect than if I was wearing my normal outfit of tennis shorts and a dirty t-shirt. (Of course, I was wearing tennis shorts and a dirty t-shirt under my lab coat). The point is that there was a level of respect, and a level of expectation that the scientist, (even if that scientist was me), would know something that others didn’t. This change in the level of respect is what I called “The White Lab Coat Effect”. It was a bit humourous, a bit puzzling, a bit sinister, but also a very inappropriate level of confidence in “The White Lab Coat”.

The White Labcoat effect makes a powerful statement. Photo by Trust “Tru” Katsande on Unsplash

The “White Lab Coat Effect” is everywhere around us to this day. Notice the number of news stories that state, “most scientists agree …”, or the number of advertisements (particularly for health products) that have a scientist or doctor wearing a white lab coat touting the benefits of whatever product is being advertised. It is a subtle yet powerful effect to make you believe the claims no matter how valid they may be.

The “scientific” view of things assumes that issues or situations can be “solved” or “optimized” or “calculated” to any degree of desired precision. For many things, this is true. We can “solve” the trajectory of a comet, or even the trajectory of the International Space Station. We can “optimize” the packaging of a container for shipping efficiency. We can “calculate” the future value of a bank balance if we invest with some guaranteed interest rate. These situations are what systems analysts called complicated systems. Complicated systems are systems, (situations, issues) that work by known law of physics, nature or mathematics. Furthermore, complicated systems are completely reproducible, meaning that doing the same thing, produces the exact same outcome, each and every time.

With a complicated system, knowledge counts. If the situation is complicated, and if you know the rules or laws governing the complicated system, then you can control and predict the outcomes. Scientists of course work in their labs to understand complicated systems, and to discover more complicated systems and understand better the laws and rules that govern these complicated situations. This is the basis, as well as the value, of scientific knowledge and learning.

The issue is that not all situations of interest are complicated. There are also systems that are what those who study systems call “complex”. In everyday language, complex and complicated are used as synonyms for each other, but to a systems scientist they are very different things. A complex system is one in which there are components or agents (for instance consumers in a marketplace), who can interact with each other (for instance by observing the habits of other people, or even interact through social media), and who can adapt (change their mind, or change their opinion based on their interactions with other people). When these three elements are present, a completely different set of outcome phenomena occur, of which the major one is leaderless emergence.

Leaderless emergence is when the group of agents interact in such a way that patterns in their actions are clearly discernable, yet completely unpredictable. The stock market is a classic example of emergence. No one dictates whether the stock market rises or falls, but the stock market does rise and fall, but when, and by how much it rises or falls, is completely unpredictable. Another example is consumer fads such as fidget spinners, ripped jeans, or even the popularity of a rock band such as Nickelback (and also their fall from favour).

The issue for business managers is that the vast majority of decisions and issues that the manager need to deal with are complex in nature and are not complicated. Despite the appearance of the validity of the material in our business school textbooks, there are very few aspects of business that consistently and precisely obey rigid rules and laws. Implementing the same managerial actions rarely, if ever, produce the same outcomes. There are no laws for producing products that have viral appeal. There are no rules that allow us to predictably manage groups of people. We do not have actionable sets of consumer behavior rules that allow us to produce blockbuster products. Instead we have “New Coke”, the Edsel and McDonald’s pizza, which were all products that were produced with state-of-the-art management techniques, but famously flopped. We also have boom and bust cycles in the economy, and dysfunctional organizations with disenfranchised employees.

The Human aspect of Business. Photo by Photo by Hunters Race on Unsplash

It is not all bad news, but the reality is that business is more complex than it is complicated. Business is not as cut and dried as physics is, and to me that is a good thing; it means that in business we need to think, rather than just know. It means that we need to explore and try things. It means that the business manager cannot be replaced by an AI bot. (Although the parts of business that are subject to rules and laws, such as parts of accounting and finance along with quantitative analysis are indeed experiencing a rise of the bot replacing the human business analyst.) The fact that business is as much (if not more) complex than complicated, means that it is wisdom, critical analysis, risk taking, and human understanding that are the key skills for success. These are all much more interesting than mere memorization of facts and equations.

As reported in his wonderful book Superforecasters, cowritten with Dan Gardner, political scientist Philip Tetlock describes the long-term study that he did of economic forecasters and of political pundits. Amongst his many findings is that these experts were lousy at predicting. Furthermore, the more confident and professional (and scientific) the experts were judged by the general public to be, the worse their forecasts turned out to be. This is clear evidence that we tend to get sucked in by the “White Lab Coat Effect”.

Business is much more complex than it is complicated. Business and management is much more about thinking and understanding and risk taking than it is about scientific knowledge. It is not the age of Sputnik scientist; it is the age of the human manager. As business managers, and as business students, we should celebrate this.


P. Tetlock, D. Gardner, “Superforecasting: The Art and Science of Prediction”, Signal, 2015