Reinventing Work: From Optimization To Adaptation

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- 6 min read

"I dispensed with the false comfort that making detailed plans can bring – and falling back on the notion of control – in what was a highly uncertain environment. Detailed annual plans may have been standard practice a hundred years ago, but in our modern world this is far from ideal.”

This telling quote from business pioneer Koldo Saratxaga is in our book.

And he's damn right. We no longer live in an era where, every day, an identical Model T would roll off Henry Ford’s production line. We can no longer predict with any certainty what the next year will bring. Change, as they say, is the only constant. Today’s world is very different to that of just a few decades ago.

The result? Organizations have become more complex. Far from solving the problem of complexity, we’ve worsened it with bureaucracy. Many companies have fallen into this trap. They attempt to predict the future and try to wrest control from chaos. They believe commanding and controlling is still the way to go.

Spoiler alert: it's not.

Prediction is hopeless

“Science and engineering have assumed that the world is predictable, and that we just need to find the proper laws of nature to be able to foresee the future,” says computer engineer Carlos Gershenson. "But the study of complex systems has shown this assumption is misguided."

Gersherson studies complex systems in the field of urban mobility. Think of traffic jams, metro systems and so on. The many interactions in such systems make them complex. For example, think of the countless interactions between pedestrians, cars, buses, trains, and bicycles.

"The problem may be that most engineers are taught traditional methods based on predicting what is to be controlled, and they try to improve on those methods. But for complex systems, prediction is almost hopeless. The moment you achieve optimality, the problem changes. The solution is obsolete already".

Adaptation beats optimization

Instead of wasting effort trying to optimize systems that can't be optimized, complexity scientists point to adaptation. Let's look at an example from Gersherson's research:

"Traffic-light systems are normally timed and programmed in a way that’s supposed to be efficient, but then the precise number of cars stopped by each traffic light varies constantly. Even if you’re basing it on a traffic measurement that it’s around 13 cars per minute on average, one minute there will be 20, and another there will be zero, and another there will be six.

"Coordinating all these programmed traffic lights to keep vehicles moving is a problem. It gets more and more computationally demanding as you have more intersections to coordinate, and it changes as you add and subtract cars. It’s impossible to predict. Since optimization is so computationally demanding, you need to use adaptation.

"Self-organizing traffic lights have sensors that let them respond to incoming traffic by modifying the timing of the signals. They are not trying to predict, they are constantly adapting to the changing traffic flow. But if you can adapt to the precise demand, then there is no idling. The only reason for cars waiting is because other cars are crossing.

"The traffic light tells the cars what to do. But because of the sensors, the cars tell the traffic lights what to do, too."

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Swarm intelligence

The traffic-light example shows the power of adaptability in complex systems. In the pioneering organizations that we've researched over the years we've seen similar approaches. Pioneers have created their own versions of complex adaptive systems.

From manufacturing companies like Irizar, Haier, and Morning Star to services providers such as Centigo, Nearsoft, Buurtzorg and Vagas.

They recreate a natural phenomenon called 'swarm intelligence' as seen in ant colonies:

"Social insects work without supervision. In fact, their teamwork is largely self-organized, and coordination arises from the different interactions among individuals in the colony. Although these interactions might be primitive (one ant merely following the trail left by another, for instance), taken together they result in efficient solutions to difficult problems (such as finding the shortest route to a food source among myriad possible paths). The collective behavior that emerges from a group of social insects has been dubbed “swarm intelligence.”

Other examples of swarm intelligence include birds flocking, hawks hunting, bacteria growing, fish schooling and microbes learning. Some of the common characteristics are:

  • Flexibility: the organization can adapt to a changing environment;
  • Robustness: the group can still perform its tasks, even when one or more individuals fail;
  • Self-organization: activities are neither centrally controlled nor locally supervised.

Pioneering organizations structure themselves in a way that it allows for these characteristics to arise. They split into highly autonomous units, find a balance between cooperation and competition, let go of central control, organize feedback to flow between the various units, and so on.

For more exploration of complexity science and the link with progressive organizations, check out this much deeper post by Joost.

The point I want to make here, however, is that the way we're looking at management science is as outdated as the use of centrally controlled traffic lights. There was a time and place for that—just as there was for traditional command-and-control management.

But that time is now long gone.

Reinventing work

Organizational scientists have too long assumed the world is predictable—that we just need to find the right laws of nature to foresee the future. Clearly, that's no longer the case.

If 2020 taught us anything, it's that trying to predict makes little to no sense. All those carefully crafted corporate plans were dispensed with as swiftly as the corona virus spread. But it's not just this 'black swan' event that shows the traditional management model is broken. It was broken well before the pandemic.

Think of 2020 as a sneak peek into the future. Hopefully, not of health, but of an ever more complex, globally interconnected and unpredictable world.

In order to prosper, we need to reimagine organizations and reinvent work. We should move from optimization to adaptation. From top-down stupidity to swarm intelligence.

In the soon-to-be-launched Corporate Rebels Online Academy we'll discuss structures of progressive organizations in much more detail. For more information on that course,  check out this post.

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Replies (13)



Thoughts please on what the Reddit community managed to do to the hedge fund managers of Wall St short selling GameStop - surely swarm intelligence at work...

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this article is rather binary and non-sensical to me: "We should move from optimization to adaptation. From top-down stupidity to swarm intelligence." Of course, adapting is critical, but in what direction? Towards the optimal state. They want to emphasize that the optimal state changes constantly, but we would hope we constantly adapt towards the optimal, would we not?

Of course, liberating the intelligence of the masses, of big data, of our front line people is critical, but the very process of doing that is decided and executed from the top-down, is it not?

I'm all for the agility and wisdom that adaptable and collaborative organizations can generate, at the same time, planning for it and leading the organization to realize it is even more important.

My 2 cents...

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