P O S T E D B Y A L B E R T
I. Introduction
In his lovely essay, “The Evolving American Foundation,” James Allen Smith describes how the germ metaphor ignited the imaginations of many who worked in the foundation field at the turn of the last century. This metaphor was especially seductive because it suggested we might understand and address our social ills the way we diagnose and cure disease. According to Smith, the germ metaphor fell out of favor some time later and was supplanted by others that more accurately reflected social complexities.
But the use of the germ metaphor has stuck with us. It’s clearly related to the metaphor of the root cause which persists in the parlance and thinking of philanthropy. We still hear of organizations that aim, for example, to address the root causes of poverty in America. A casual Google search on the phrase “root cause(s)” will turn up many examples. The metaphor of the root cause has many cousins in philanthropy, among them the lever and the key (as in the “key to the puzzle”).
Nowadays we recognize, as others did long ago, that there are conceptual difficulties in identifying anything like a root cause of poverty, beyond the condition of not having any money. The phenomenon of poverty is part of a dynamic system of many parts, interacting in complicated ways. This complex system has no discernible root that we can yank out of the ground as we might the root of a noxious weed.
We shouldn’t let ourselves be too awed by the image of the complex system. Physicists, economists, electrical engineers, and others have been successful in modeling very complex systems and using these models to predict the behavior of these systems over time. Jay Forrester, for example, used one such model to analyze industrial business cycles and another to analyze the behavior of the stock market. What’s unusual about these models is that they lack a point of origin—there’s no discernible “root.” What you have instead are “feedback loops” and entities called “stocks” and “flows.” These constructs enable you to predict how a system will respond when you change one of its properties.
The figure below, taken from a May 2009 Scientific American article, traces the causes of food shortages in developing states. This model suggests that while food shortages have multiple proximate causes (e.g., loss of topsoil, spreading water shortages, and reduction in crop yield), these causes can themselves be traced back to a single root cause: population growth, which itself has multiple causes not shown in the diagram. Assuming this analysis is correct, a funder attempting to address food shortages without addressing population growth would appear to be on a fool’s errand.
As foundation professionals we talk about complex systems, yet we don’t often apply the tools developed in other domains to study them. In our weaker moments, we might even invoke the complexity of a system as an excuse for our inability to make any headway on a given problem.
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