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Making Decisions in a Complex Adaptive System


In Think Twice: Harnessing the Power of Counterintuition, Mauboussin does a good job adding to the work we’ve already done on complex adaptive systems:

You can think of a complex adaptive system in three parts (see the image at the top of this post). First, there is a group of heterogeneous agents. These agents can be neurons in your brain, bees in a hive, investors in a market, or people in a city. Heterogeneity means each agent has different and evolving decision rules that both reflect the environment and attempt to anticipate change in it. Second, these agents interact with one another, and their interactions create structure— scientists often call this emergence. Finally, the structure that emerges behaves like a higher-level system and has properties and characteristics that are distinct from those of the underlying agents themselves. … The whole is greater than the sum of the parts.


The inability to understand the system based on its components prompted Nobel Prize winner and physicist Philip Anderson, to draft the essay, “More Is Different.” Anderson wrote, “The behavior of large and complex aggregates of elementary particles, it turns out, is not to be understood in terms of the simple extrapolation of the properties of a few particles. Instead, at each level of complexity entirely new properties appear.”

Mauboussin comments that we are fooled by randomness:

The problem goes beyond the inscrutable nature of complex adaptive systems. Humans have a deep desire to understand cause and effect, as such links probably conferred humans with evolutionary advantage. In complex adaptive systems, there is no simple method for understanding the whole by studying the parts, so searching for simple agent-level causes of system-level effects is useless. Yet our minds are not beyond making up a cause to relieve the itch of an unexplained effect. When a mind seeking links between cause and effect meets a system that conceals them, accidents will happen.

Misplaced Focus on the Individual

One mistake we make is extrapolating the behaviour of an individual component, say an individual, to explain the entire system. Yet when we have to solve a problem dealing with a complex system, we often address an individual component. In so doing, we ignore Garrett Hardin’s first law of Ecology, you can never do merely one thing and become a fragilista.

That unintended system-level consequences arise from even the best-intentioned individual-level actions has long been recognized. But the decision-making challenge remains for a couple of reasons. First, our modern world has more interconnected systems than before. So we encounter these systems with greater frequency and, most likely, with greater consequence. Second, we still attempt to cure problems in complex systems with a naïve understanding of cause and effect.


When I speak with executives from around the world going through a period of poor performance, it doesn’t take long for them to mention they want to hire a star from another company. “If only we had Kate,” they’ll say, “we could smash the competition and regain our footing.”

At first, poaching stars from competitors or even teams within the same organization seems like a winning strategy. But once the star comes over the results often fail to materialize.

What we fail to grasp is that their performance is part of an ecosystem and removing them from that ecosystem — that is isolating the individual performance — is incredibly hard without properly considering the entire ecosystem. (Reversion to the mean also likely accounts for some of the star’s fading as well).

Three Harvard professors concluded, “When a company hires a star, the star’s performance plunges, there is a sharp decline in the functioning of the group or team the person works with, and the company’s market value falls.”

If it sounds like a lot of work to think this through at many levels, it should be. Why should it be easy?

Another example of this at an organizational level has to do with innovation. Most people want to solve the innovation problem. Ignoring for a second that that is the improper framing, how do most organizations go about this? They copy what the most successful organizations do. I can’t count the number of times the solution to an organization’s “innovation problem” is to be more like Google. Well-intentioned executives blindly copy approaches by others such as 20% innovation time, without giving an ounce of thought to the role the ecosystem plays.

Isolating and focusing on an individual part of a complex adaptive system without an appreciation and understanding of that system itself is sure to lead to disaster.

What Should We Do?

So this begs the question, what should we do when we find ourselves dealing with a complex adaptive system? Mauboussin provides three pieces of advice:

1. Consider the system at the correct level.

Remember the phrase “more is different.” The most prevalent trap is extrapolating the behavior of individual agents to gain a sense of system behavior. If you want to understand the stock market, study it at the market level. Consider what you see and read from individuals as entertainment, not as education. Similarly, be aware that the function of an individual agent outside the system may be very different from that function within the system. For instance, mammalian cells have the same metabolic rates in vitro, whether they are from shrews or elephants. But the metabolic rate of cells in small mammals is much higher than the rate of those in large mammals. The same structural cells work at different rates, depending on the animals they find themselves in.

2. Watch for tightly coupled systems.

A system is tightly coupled when there is no slack between items, allowing a process to go from one stage to the next without any opportunity to intervene. Aircraft, space missions, and nuclear power plants are classic examples of complex, tightly coupled systems. Engineers try to build in buffers or redundancies to avoid failure, but frequently don’t anticipate all possible contingencies. Most complex adaptive systems are loosely coupled, where removing or incapacitating one or a few agents has little impact on the system’s performance. For example, if you randomly remove some investors, the stock market will continue to function fine. But when the agents lose diversity and behave in a coordinated fashion, a complex adaptive system can behave in a tightly coupled fashion. Booms and crashes in financial markets are an illustration.

3. Use simulations to create virtual worlds.

Dealing with complex systems is inherently tricky because the feedback is equivocal, information is limited, and there is no clear link between cause and effect. Simulation is a tool that can help our learning process. Simulations are low cost, provide feedback, and have proved their value in other domains like military planning and pilot training.

Still Curious? Think Twice: Harnessing the Power of Counterintuition.