I have written several times recently about the need for resiliency and managing in a complex environment, given how unpredictable the world is and referencing Chaos Theory. Given the importance of the Butterfly Effect, the impact of Chaos Theory, it is worth expounding on the theory and consequences. An article in the MIT Technology Review, When the Butterfly Effect Took Flight by Peter Dizikes, does a great job of explaining the theory and impact.
What is the Butterfly Effect?
In the 1960s, Edward Lorenz, a meteorology professor at MIT, entered data into a computer program simulating weather patterns and then took a break while the computer processed the information. Upon reviewing the results, he noticed an outcome that led to what is now known as the Butterfly Effect.
Lorenz’s computer model inputted twelve KPIs, such as temperature and wind speed. During this particular simulation (one that he had run previously), he rounded off one variable from .506127 to .506. Dizikies writes, “to his surprise, that tiny alteration drastically transformed the whole pattern his program produced, over two months of simulated weather. The unexpected result led Lorenz to a powerful insight about the way nature works: small changes can have large consequences. The idea came to be known as the ‘butterfly effect’ after Lorenz suggested that the flap of a butterfly’s wings might ultimately cause a tornado. And the butterfly effect, also known as ‘sensitive dependence on initial conditions,’ has a profound corollary: forecasting the future can be nearly impossible.”
This seemingly innocuous finding challenged some core scientific principles. Isaac Newton published “laws” in 1687 that suggested a tidily predictable mechanical system, known as the “ clockwork universe.” Mathematician Pierre-Simon Laplace whose work was important to the development of engineering, mathematics, statistics, physics, astronomy, and philosophy, wrote that if we knew everything about the universe currently, then “nothing would be uncertain and the future, as the past, would be present to [our] eyes.”
Lorenz’s findings challenged both Newton and Laplace, as unpredictability does not impact how they explain the world. Dizikies explains, “the tiny change in [Lorenz’s] simulation mattered so much showed, by extension, that the imprecision inherent in any human measurement could become magnified into wildly incorrect forecasts…. After Lorenz, we came to see that determinism might give you short-term predictability, but in the long run, things could be unpredictable. That’s what we associate with the word ‘chaos’.”
This concept of chaos amplifies how the world is nonlinear. “The principle of chaos drove home the importance of non¬linearity, a characteristic of many natural systems. If a group of 100 lions has a net gain of 10 members a year, that increase in population size can be plotted on a graph as a straight line. A group of mice that doubles annually, on the other hand, has a nonlinear growth pattern; on a graph, the population size will curve upward. After a decade, the difference between a group that started with 22 mice and one that started with 20 mice will have ballooned to more than 2,000. Given that type of growth pattern, the real-life pressures on species — normal death rates, epidemics, limited resources — will often cause their population sizes to rise and fall chaotically. While not all nonlinear systems are chaotic, all chaotic systems are nonlinear,” explains Dizikies.
Butterflies are not random
A critical element of Chaos Theory is that it does not imply randomness. Dizikies writes, “One way that he demonstrated this was through the equations representing the motion of a gas. When he plotted their solutions on a graph, the result — a pair of linked oval-like figures — vaguely resembled a butterfly. Known as a “Lorenz attractor,” the shape illustrated the point that almost all chaotic phenomena can vary only within limits.” The key here is that the butterfly is a range of possible results, but there are boundaries.
While the effect is not random, it is also not predictable. Nature’s interdependent chains of cause and effect are too complex to disentangle. Thus, you do not which butterfly, or gnat, may have created a given storm.
The Butterfly Effect and gaming companies
The value of understanding the Butterfly Effect for gaming companies goes beyond knowing if you need to bring an umbrella to the office. Just as weather patterns are unpredictable due to the myriad of factors that can cause a storm, the business environment is equally unpredictable. A new law in a market you are not engaged or a product launch in a different industry can end up changing the dynamics of your business. The most obvious recent example is how bats in Wuhan, China ended up driving catastrophic effects on the travel (and many other) businesses, while driving online gaming revenue to unprecedented levels.
The Butterfly Effect is why you need a resilient business
As the future is not predictable, it is critical that you build a resilient business that can quickly adjust to major changes in your ecosystem. To be resilient and deal with change, you need to move from a hierarchical, command and control structure, to one that empowers your company to react quickly to butterflies half a world away.
- In the 1960s, Edward Lorenz identified the Butterfly Effect when inputting multiple KPIs into a weather program, rounding one number, and seeing that the seemingly insignificant rounding change led to a momentously different outcome.
- The importance of small changes on outcomes shows that activity is not predictable, and this challenge extends to the business environment.
- To manage effectively and overcome the unpredictability of the world, you need to build a resilient business and move away from a hierarchical, command and control structure.
2 thoughts on “Chaos Theory, the Butterfly Effect, and Gaming”
It also highlights that in the real world, there is very limited meaningful difference between “unknown” and “random”. Functionally, they’re the same thing, and sometimes knowledge is not binary (this is a good decision or a bad decision), but rather, probabilistic (this has a 70% chance of being a good decision and a 30% chance of a bad decision).