Simplified Complexity: Thinking in the White Spaces

By Gingrich, Gerry | Strategic Forum, May 1998 | Go to article overview

Simplified Complexity: Thinking in the White Spaces


Gingrich, Gerry, Strategic Forum


Conclusions

* Change, not stability, is the constant in today's world. Massive amounts of information flow around our global system in milliseconds, breaking down the borders of nation states and shifting the balance of power among old and new players. Widely separated and disparate places around our globe communicate across electronic networks in real time, reducing the effects of distance and time.

* Closer to home, America maintains its position as the leading edge player in the information society, adapting to the global information ebb and flow more quickly than any other nation on earth. We rethink our national security strategy more and more frequently, and the number and variety of relevant factors increase daily. Our public and private organizations continue to re-size, restructure, and reengineer. And our citizens enter new educational programs, lose and change jobs, and explore new models of management and leadership. As Carl Builder warns, it is a time for humility, not hubris.

* Military thinkers, politicians, scientists, and corporate executives are all looking for ways to understand the dynamics of global change and to prepare for the 21st century. Many are looking to the new science of complexity for answers. The science of complexity, however, does not yield answers, at least not in the sense that we have typically sought to describe our world and predict its events since the beginning of the Scientific Revolution. What it does yield is a new way of thinking about the world.

Old or New: Simple or Complex

For the last 300 years, Western science has progressed through the discovery and accumulation of "laws," each judged and measured against the aesthetic of simplicity. Laws that are simple are better than those that are complex. In contrast, the new way of thinking is ruled by an aesthetic of complexity. Empowered by the computer and the powerful manipulations it makes possible, this new thinking focuses on complex phenomena. Indeed, the new way of thinking assumes that many worldly phenomena are complex, not simple, and that ipso facto, the scientific description of these phenomena must also be complex.

As a framework for illustrating the differences between the old and new ways of thinking, consider regression analysis, a popular technique used by both the hard and soft sciences to model worldly phenomena. Regression analysis develops mathematical models to describe the functional relationship between a phenomenon, y, and a set of variables, [x.sub.1], [x.sub.2], ..., [x.sub.n]. Old thinking, the traditional paradigm of Western science, argues in favor of simplicity. So the fewer the x's in the regression model, the better. Thus we have typically built our regression models of political and economic phenomena by starting with a very small number of variables and adding new ones only if they are statistically important. For example a traditional political scientist might use the two variables of military strength and economic health to build the first iteration of a hypothetical model describing the possibility of an outbreak of a major regional conflict (MRC) such as the Persian Gulf War. Additional variables might be useful for a full understanding of the nature of MRCs, but unless the political scientist can prove that they're statistically important, they'll be excluded from the model.

New thinking, or the complexity paradigm, would build its first iteration of a regression model by including all the variables relevant to describing and understanding the complex MRC phenomena. So a nontraditional political scientist might create the first iteration of a MRC model by including the variables of military effectiveness, political leadership, and civil-military balance in addition to the variables of military strength and economic health. Each of the five variables would remain in the final model as long as it was not proven to be statistically insignificant. …

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