Dynamic Complexity in Systems or, Why Failure is Inevitable

Systems that are dynamically complex are challenging to understand and manage because their behavior is shaped by hard-to-predict relationships that play out in different ways when conditions vary. When causes are separated from their effects in time (and in space), it’s tough to draw a straight line from action to result. Nonlinear factors can dramatically change behavior with only small changes in their inputs, once a threshold or inflection point is reached. Simple heuristics, such as linear rules-of-thumb, don’t apply in such cases. And more sophisticated statistical techniques are ineffective when the situation shifts into new regimes outside historical bounds, such as when a new technology dramatically changes costs or when environmental conditions result in previously unseen weather patterns.

Dynamic complexity is inescapable in today’s organizations and markets. It’s inevitable that you’ll encounter lags and biases in noticing, measuring, and responding to the effects of this type of complexity. Let’s take a closer look at why this matters.

Behavioral research is shedding light on human limitations in predicting and understanding complex systems—and their implications. For example, studies of decision making show that individuals, organizations, and markets consistently overlook the effects of delays and cumulative effects ¹.

Scholars are not immune to these challenges; dynamic complexity may contribute the difficulty of inferring patterns of behavior from verbal theories. Thanks to such factors, individuals and social systems encounter profound difficulties in learning from experience; persistent underperformance, unaddressed problems, and catastrophic failures result ². The tendency of social systems to resist attempts to improve performance has been traced to cognitive limitations in understanding dynamics ³.

When cause and effect are separated in time, drawing the right inferences is difficult. You need not be an entrepreneur navigating a boom-and-bust market to suffer its effects.

Not only is the separation in time a barrier to learning, so too is the separation in space. Actions in one part of the system play out elsewhere. Competition among Western mass retailers has consequences for factory workers in Vietnam, and demand for organic food in American supermarkets affects farming practices in Mexico.

Nonlinearities also mean history cannot always guide. If humans, markets, and ecosystems respond in ways that vary, the future cannot be predicted by extrapolating from the past. For example, once a critical mass of consumers started choosing iPhones over BlackBerrys, the entire market shifted dramatically. BlackBerry’s past experience was not a useful guide to its future once these defections reached a critical level.

Nonlinearities arising from multiple factors that interact and unfold over time are the hallmark of dynamic complexity. As in most of human life, developments in markets and society are the result of multiple factors operating together in ways that cannot be teased apart, analyzed separately, and then added up. The assumption of separability is an analytical convenience that makes study more tractable, even as it yields less insightful understanding.

But let’s not forget that in the real world, things are not neatly separable: actions cause effects; effects set the stage for actions and shape them. Mutual simultaneous influences are the rule, not the exception, even if your spreadsheet software complains about “circular references.”

All of this means that when you are trying to accomplish anything novel, chances are that you will not get it right the first time. You may even find that your attempts to solve a problem actually worsen it, thanks to unanticipated consequences. This is why we encourage application of systems thinking. Systems thinking teaches us that it’s more fruitful to take an endogenous view that seeks to explain how the results are a product of factors in which you play a part as well. You have much more power to shape outcomes if you can better understand how the problems and opportunities you face today are connected to your own past actions and are influenced by the structure of the industry, society, and ecosystem in which you play a role.