To meet the life-threatening challenges of global misgovernment, global warming, and environmental degradation, we need far more realistic conceptual insights into what is actually going on. The best current hope for sharpening our analytical vision is the integration of system dynamics and complexity theory.

System dynamics and complex-adaptive systems are the two most sophisticated perspectives for analyzing some of the most difficult problems facing mankind, from understanding the environment to achieving world peace, and these two analytical perspectives are in fundamental agreement about the interactive, subtle, evolutionary, and counter-intuitive nature of such problems. Yet the practitioners of one tend to ignore the other, to their mutual intellectual impoverishment, leading to the obvious questions of why these two methodological approaches are so isolated from each other in practice and how they might be integrated to strengthen our ability to understand our world.

Despite the many practical differences in terms of technical vocabulary, computer tools, and the regrettable tendency of the two sets of experts to attend separate conferences, system dynamics and complex-adaptive systems are perspectives on how the world works that share fundamental insights. Perhaps the most important of these is that the explanations for what is happening lie at the system level, which means that since the action frequently occurs at the level of the big picture, the big picture must be understood; reduction to the simplest level of course remains a useful tool but simply will never lead by itself to an understanding of, say, the impact of carbon emissions on global warming, the impact of global warming on local weather, or the relationship between globalization and Muslim politics.

Their agreement that the whole system must be understood does not, however, prevent major differences of approach, and to master our world, we need to overcome this difficulty at the conceptual level. The system dynamicist views the system under analysis as an interlinked set of forces (dynamics) propelling behavior and tries immediately to identify the dominant dynamic and the existence of other dynamics that might gain dominance at some future tipping point. The complex-adaptive systems thinker focuses more on the components of the system, rather than the forces linking them, starting with a picture of a system consisting of modules all interacting and adapting in reaction to each other, with both the modules and the whole system constantly evolving along a path so complex as to be essentially unpredictable.

Ultimately, this constitutes a huge difference, in that the system dynamicist aspires to collecting sufficient data so as to construct a set of differential equations to explain the world while the complexity theorist cautions that such an explanation is theoretically impossible. Since in practice we are all so far from having good data for most interesting problems, this theoretical argument can probably be safely set aside for the next few centuries.

According to Michio Kaku, Einstein’s brilliance rested on his extraordinary ability to conceptualize in a simple physical image a solution to a problem that no one, himself included, had ever been able to solve mathematically [Kaku, *Physics of the Impossible*, 199]. In other words, Einstein understood the solution before he developed the mathematical solution! In our ever-shrinking world, we do not have time to develop the mathematical solution to global warming or world peace – we will kill ourselves long before we gather the requisite data, even if the complexity theorists turn out to be wrong in cautioning that it is theoretically inconceivable that we could ever gather enough data to overcome the problem of huge differences in outcome resulting from tiny variations in initial conditions. In practice, then, we should aspire to match not Einstein’s math but Einstein’s intuitive understanding.

The priceless value of both system dynamics and complexity theory lies in the conceptual tools they provide for achieving sophisticated intuitive insights into problems we have not yet learned how to solve. In practice, system dynamics would be enriched by also considering the system as a set of adapting sub-system modules. In practice, a more accurate and powerful view of a complex-adaptive system could be constructed by drawing reference modes (simple time graphs) to define your current mental model of what you imagine is happening and by building causal loop diagrams of the dynamics linking sub-systems.

Today, anyone attempting to understand why global warming seems to be producing blizzards in West Europe or why Muslim societies freed from their dictators only welcome foreign invaders “with flowers” for the first few days before self-organizing national liberation movements needs to employ both the concepts of system dynamics and the concepts of complexity theory. Pretending that the melting of the Arctic ice and blizzards in England are unrelated or that Muslim insurrections against Western occupation are simply the work of “extremists” who “hate our way of life” is suicidal self-delusion.

As a helpful colleague immediately pointed out, there is in reality no such thing as “complexity theory,” only “complexity philosophy.” I cannot but concur, and the distinction is important. A complexity theory would state conditions and the outcomes such conditions would predict. Unfortunately, to my knowledge, complexity “theory” remains only a philosophy for interpreting reality, perhaps a bit more advanced than Plato’s concept of humans in a cave trying to peer out. As such, complexity concepts are very useful for provoking us to tread cautiously along the edge of an invisible chaos that threatens all highly structured organizations, for warning us to anticipate self-organization among–perhaps–those we have just defeated, for teaching us that our interactions change not only others but ourselves in an adaptable world, for forecasting that counter-intuitive behavior at a group level may arise from behavior at the individual level. But, the philosophical perspective of complexity does not tell us where the edge of chaos lies or what sort of new behavior might emerge. It does far more than I alluded to in my post, with a host of powerfully suggestive models that appear to be useful metaphors for the real world, but the solid techniques of system dynamics remain a useful starting point for understanding the real complexity of systems.

My understanding is that complex adaptive systems view predictability as impossible not due to lack of sufficient data or the subtle way in which a tiny change in initial conditions can lead to huge changes in outcome but due to a fundamental aspect of complex adaptive systems which makes them inherently impossible (literally impossible not just very difficult) to predict.