My previous column noted the difficulty -- indeed, impossibility -- of predicting the path of technological evolution for any but the most trivial incremental changes. This is one of those historical truths commonly overlooked in practice, but its implications for the environmental and sustainability discourses are profound.

Thus, for example, one of the most common predictive techniques used in the sustainability literature is “backcasting” -- defining the world that one wants to be in decades hence, and then backcasting to determine what policy initiatives are necessary to make that world happen. In the most sophisticated approaches, this is understood not as a “predictive,” but as a “scenario” exercise -- that is, not as a statement that one actually expects the postulated future to happen, but as a thought experiment to determine what policies might be valuable in moving towards a scenario emphasizing one aspect of possible futures. But many believe that the postulated state is actually achievable by making appropriate social and political decisions -- that “sustainability” is a simple matter of defining a distant future, then just figuring out and implementing policies to make it happen.

This is an unhelpful myth. The idea that anyone understands enough about current historical, social and technological trends to predict the future even several years out, much less decades in the future, blinks any historical experience. It is a reflection of wishful thinking coupled to a powerful ideology, a projection of teleology rather than a reasoned understanding of how human systems actually evolve. A scenario, properly used, is a stimulus to conceptualizing potential future paths, and building robust option spaces within which to respond to various alternatives and contingencies. An alleged prediction, however, is unhelpful because it has the opposite effect: it validates rigid ideological viewpoints and stifles the exploration of alternatives. A scenario process builds resiliency; a prediction process tears resiliency down. More subtly, a scenario process encourages exploration of many dimensions of a complex and unpredictable future and thus multicultural understanding; a prediction process often encourages cultural and ideological imperialism by demanding fealty to a particular worldview.

This reliance on over-simplistic and prescriptive approaches is sometimes justified because immediate action is seen as necessary to maintain the stability of current systems, even given conditions of high uncertainty. And it is certainly true that paralysis by analysis, or defense of the status quo by alleging inadequate data to support change, is an undesirable outcome. The subtlety arises, however, from the means by which action is taken, and modified. The predictive model, which posits a known future state that can be reached by specific policy actions determined by backcasting, encourages definitive actions which are seen as both necessary and dispositive. On the other hand, the scenario process implies a different approach that accepts the reality that future states of complex adaptive systems cannot, in fact, be known. This does not mean, however, powerlessness; rather, it implies a process orientation. As changes in policy and technology occur, the state of the appropriate system is monitored, and its responses identified; these, in turn, become the grist for further scenario development, and policy and technology initiatives. There is still the understanding that some paths are “better” than others (based on whatever criteria are appropriate under the circumstances: preservation of regional biodiversity, economic development, national security, etc.), and a constant dialog with the system thus attempts to achieve paths that best support the desired mix of outcomes. The difference between the approaches is both operational -- a flexible dialog process as opposed to a prescriptive set of inflexible specific policies and actions -- and philosophical: the former assumes that goals for systems performance are appropriate, but that the system itself is a teleological and unpredictable a priori, while the latter approach is at least implicitly teleological.

There is another aspect to this contrast in approaches as well. The flexible approach, facing a world verging on an unpredictable and transformative integration of the human and the non-human, and the technological and the natural, combines a profound humility and appreciation of our ignorance with the knowledge that, if we properly understand our condition, we are not powerless to shape the future based on rationality and ethical principles. The predictive approach, however, reflects an extraordinary hubris that presumes we understand far more than we really do, and that we are both ethically commanded to, and capable of, defining future states. It is the powerful dream that motivates all utopianism, and in this, it is both dangerous, and the more powerful for being implicit.

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Brad Allenby is professor of civil and environmental engineering at Arizona State University, a fellow at the University of Virginia’s Darden Graduate School of Business, and previously was AT&T’s vice president of environment, health, and safety.