John Kania, Mark Kramer, and Patty Russell offer some helpful advice to strategic philanthropists, but their analysis makes the simple too simple and the complex too complex. In the authors’ dichotomous universe, simple problems can often be solved through “rigid and predictive model[s] of strategy,” but “when the line has been crossed . . . into the entirely different world of complexity,” prediction is no longer possible and must be replaced by “complexity science” and “emergent” strategies.

Granted that some problems are more challenging than others, it’s more useful to think of simple and complex problems as lying on a continuum rather than on two sides of a divide. Strategic planning and prediction are essential from one end of the continuum to the other, and there is no point at which they are replaced by complexity science.

These are not purely academic points. Constant feedback, learning, and adaptation are essential across the range of simple, complicated, and complex problems. And too much philanthropy fails for want of these essentials. To paraphrase Cassius, the fault is often not in our problems but in ourselves.

Let’s begin with a paradigmatic “simple” strategy—a workforce placement program for youths recently released from prison. Its simplicity lies in the fact that once the program has a track record, the plan that worked for last year’s cohort is likely to work for next year’s as well. Kania and colleagues rightly observe that such programs are governed by the laws of cause and effect. They have statistically predictable outcomes for a group of participants (though not for any individual participant.)

But even in this relatively simple realm, a manager must continually monitor the organization’s performance and adapt its plan to changing external circumstances. Moreover, a simple strategy is usually the culmination of a more complex developmental process: In line with the venerable military adage that “no plan survives contact with the enemy,” the process of developing a novel service delivery program is characterized by trial and error. And as software entrepreneur Jim Manzi points out in Uncontrolled: The Surprising Payoff of Trial-and-Error for Business, Politics, and Society, efforts to replicate a successful program with different populations are also fraught with uncertainties because of myriad unknown contextual factors. In short, continuous learning and adaptation are pervasive throughout the phases of “simple” strategies.

Now let’s look at a paradigmatically complex strategy—policy advocacy. In The Elusive Craft of Evaluating Advocacy, published in the summer 2011 issue of Stanford Social Innovation Review, Steve Teles and Mark Schmitt write: “It’s the nature of politics that events evolve rapidly and in a nonlinear fashion, so an effort that doesn’t seem to be working might suddenly bear fruit, or one that seemed to be on track can suddenly lose momentum. . . . What matters is whether advocates can choose the tactic appropriate to a particular conflict and adapt to the shifting moves of the opposition. . . . Successful advocacy efforts are characterized not by their ability to proceed along a predefined track, but by their capacity to adapt to changing circumstances.”

What makes advocacy strategies complex are the numerous actors and activities that may contribute to an outcome, uncertainties about causality, and strategic behavior by the various participants. Even so, at any given time a policy advocate must make estimations about causality—for example, “We should devote resources to persuading Senator Jones about the merits of our policy proposal because he has great influence with his peers; and judging by our observations, Senator Jones is most open to these arguments.

Analogously, the Rockefeller Foundation’s seeding of organizations to create the infrastructure for impact investing and the collective impact initiatives referred to by the authors require predicting how each organization in the network can contribute to solving the problem at hand. And these examples disclose another feature of many strategies to address complex problems: Even a complex overarching strategy often involves creating or supporting particular organizations; and as a matter of operational necessity, each of those organizations must have a simple and somewhat rigid strategy akin to that of the service delivery organization described above.

Kania and colleagues’ analysis reminds me of A Subway Named Mobius, a wonderful short story by A. J. Deutsch, in which the addition of one line to the Boston MTA’s already complicated system transforms it into a complex system governed by non-Euclidian geometry. Well, strategic philanthropy isn’t like that. Efforts to cordon off some area as complex take pressure off philanthropists to embrace adaptive planning and implementation across the spectrum.

I’ll conclude with a plea about language. The text box on “How to Move to an Emergent Model” revels in the jargon of virtually every problem-solving and design notion in vogue today. I wonder whether persuading foundation leaders to adopt these ideas might call for a more emergent, systems-mapping, stakeholder-analytic, sensing, cultural-framing, human-centered design, and story-telling communications strategy than the authors have employed here. At least it’s worth prototyping.

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