Complexity Theory: Turning Chaos into Opportunity

Complex systems comprise myriad elements whose interactions produce collective order and patterns. Complexity theory examines the laws governing these dynamic processes—from complex atomic, molecular, and cellular systems in natural environments up to complex social and economic systems. It applies interdisciplinary approaches to investigate the interaction of multiple elements within a complex system, for example molecules in a material, cells in organic structures, or people in markets and organizations, and to find out how these interactions produce order and regular structure—or chaos and turbulence. A system is considered chaotic when minute changes to its initial conditions induce substantial change. You may have heard this referred to as the “butterfly effect:” The beat of a butterfly’s wing could trigger changes in weather on a global scale.

Markets and business enterprises are examples of economic systems in which humans enact multiple, interacting functions. The automotive industry, in particular, makes for a good field of research. Its products and business practices have undergone considerable change in recent years. Besides handling classic vehicle development, automotive companies invest their resources in researching the rapidly growing fields of digitalization and artificial intelligence. The increasing diversity of product variants and technologies is resulting in ever-greater complexity.

Classic liberalism, as well as classic 18th- and 19th-century physics, have traditionally failed to take this complexity into account when addressing economic systems. Instead, a linear propensity toward equilibrium was usually assumed that would result in economic forces naturally organizing to produce the “wealth of nations” (Adam Smith). However, as it is an open system that is constantly exchanging materials, energy, and information with other markets and the natural environment, a market economy can never approach final equilibrium. In the same way as a biological ecosystem, it is in constant flux and responds sensitively to the smallest of changes in its boundary conditions. Also, an economic system’s agents are humans, and thus possess the capacity to learn: Short-term fluctuation in consumer preferences, lack of reactivity in production behavior, even speculation in resource and real-estate markets all show how susceptible economic systems are to change. We call these highly interdependent systems “non-linear.” Take two competing products, for example, with positive feedback from rising revenue. If we want to find out what effects random fluctuations in the early stages will produce, we need a non-linear model. The slightest market advantage early on (perhaps a greater market share in a specific region, perhaps political contacts, or a better lobby) can entail knock-on effects that provide a breakthrough for one of the products. In turn, a certain technology may take hold more easily, increasingly gaining in significance in a way that could not have been predicted at the outset.

Even a technical standard, for example a computer operating system, that was not the ideal solution from a technical viewpoint, might come out on top in the end. Non-linear dynamics prove that a variant does not need to be the best in order to become established—in evolution, in economics, in politics. We need to remember this when heading into a competitive field so that we don’t underestimate the impact even the minutest change in circumstances can have and so that we remain prepared to take appropriate action.

The human mind cannot hope to keep track of the overall developments of every detail—in system design, for example, the many variables entail too rapid an increase in complexity. Production and sales address this by making more and more use of machine learning to train system components like robots in the tasks they need to perform in collaboration with humans (Industry 4.0).

But tomorrow’s markets will remain decisively dependent on humans as the driving force of businesses’ innovation potential. Complexity management succeeds where we exploit the non-linear dynamism of complex systems. We find the greatest promise in the behavior on the “brink of chaos:” Companies need to identify how closely their typical fluctuations can be allowed to approach instability to trigger surges in innovation. It is when we stand on the brink of chaos that we discover our greatest creativity.

One example would be a stable business venture that surrounds itself with a ring of start-ups, investing in innovative fluctuation with uncertain outcomes: The business would be operating intentionally on the brink of chaos—Silicon Valley’s proven modus operandi. Companies that favor excessive prudence by remaining on the straight-and-narrow and resting on their previous successes are ultimately overtaken and fail. There’s even an historical example at a global scale to show how to survive and prosper on the brink of chaos: The communist regime in China allowed controlled, incremental establishment of capitalist free-trade zones, ultimately producing an incredible economic boom. The former Soviet Union, on the other hand, pursued its command economy to an utter standstill that resulted in its total collapse.

Text first published in the Porsche Engineering Magazine, issue 1/2019.

Photo: Udo Keller, Stiftung/Hamburg

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