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Having taken steps to bury the old year we look ahead to the new and attempt to predict what lies ahead. Eddie at The Greenhouse links an article by Hari Kunzru, futurecasting, that traces a brief but interesting history of a type of predictive modelling from the early days of cybernetics in the 1950s to more current efforts at scenario building. Kunzru's objective is to illuminate the errors of the dot com bubble and its advocates, especially Peter Schwartz and GBN. There are parallels both in time frame and intellectual failure to those of Donella Meadows and the Club of Rome spoken of in the earlier post Mental Tools.
I think its useful to recognize that these failures weren't the work of a rogue or two, that they were pervasive in the era, and felled planners from Siberia to California. Cosma Shalizi has some wry but useful comments at Three-Toed Sloth about these events. The idea that it is possible to predict and/or control socioeconomic systems if only enough data can be gathered and processed by cleverly programmed and sufficiently powerful computers is the still living root of these failures. GBN and Schwartz are still in business though their luster has dimmed of late and they are sometimes cruelly caricatured as old hippies who sold out. Many others, such as Demos and Institute For The Future (IFTF) to name just two, are attempting to develop hybrid approaches that retain value while avoiding over shoot. Some are political, some are public service and some are business oriented. The theme of a recent issue of Conservation Ecology is Toward a “Science of the Long View”. CE adds something useful to long term planning by grounding it in testing. Scenarios and models are improved by the requirement that they be testable, setting in motion a discovery machine that helps turn SWAGs into useful plans.
In The Art of the Long View: Paths to Strategic Insight for Yourself and Your Company, Peter Schwartz argues for an approach to decision making in business and management that involves a strategic cross-scale framework (Schwartz 1996). He develops a rationale and methodology that incorporate slow, broad variables, which he calls "macro factors" and describes as the big picture or a long-term perspective, with small, short variables or "micro factors." He also suggests that key uncertainties be identified as part of this approach. The heart of his methodology is the development of scenarios that can be used to elaborate strategies that are robust to plausible alternative futures.These are mainly research activities at present but perhaps it will be more at some point in future. Perhaps applications based on discoveries made by research in complexity science, evolutionary game theory and statistical methods coupled with sufficiently powerful networks and processors will be developed and deployed. Futurism shares some features with science fiction though it is a bit more constrained to plausible developments. Science fiction has many stories that postulate societies based on such systems. In addition to questions about the possibility of systems able to gather sufficient data in real time and process it, there are concerns about the type of societies that might result. Even if we can do this do we want to? Such concerns aren't limited to dark scenarios where powerful computers run amok. Even relatively benign scenarios such as those of Iain Bank's Culture series of novels impose profound and perhaps unacceptable changes on human society. In Ken MacLeod's Fall Revolution series the "black planner" that overtly and covertly manipulates humanity for its own good, which necessarily includes continued existence and control by the black planner, seems to be both the best hope for world socialism and the strongest reason to oppose socialism. The seductive promise of peace, harmony and abundance in a non-wage world is tainted by a whiff of futility, irrelevance and slow degeneration.However, it should be noted that Schwartz and others call this process an art, not a science. We argue that it requires going beyond planning and into scholarly discourse, data and information gathering, and long-term learning. That is, the development of scenarios is only the first step in a longer-term process that involves "testing" which of these scenarios might be unfolding and what information is needed to distinguish among these alternative futures.
We borrow from Schwartz's title and suggest that the current and ongoing special issues of Conservation Ecology are aiding in the development of a "science of the long view." These contributions are filling the large gap between science and policy in coupled systems of people and nature in two different ways. The first involves studies that integrate disciplines over decades and centuries, i.e., they take the long view. The second involves the development of new models, technologies, and techniques to understand how ecosystems operate over broad temporal and spatial scales.
Scenario-based planning is one approach to developing a long view. Although scenario-based planning dates back more than 50 yr, it is only in recent years that it has been applied to natural resource issues. This type of planning is an important part of the Millennium Ecosystem Assessment of the capacity of the world's ecosystems to support social and economic development. One current application involves the Northern Highland Lake District in Wisconsin, USA, where a team of scientists and stakeholders has been developing alternative futures in an area that is undergoing rapid change.
Long-term studies, i.e., those that last more than the lifetime of a single investigator, of coupled social and ecological systems are critical to understanding the long view. This issue contains a number of articles that help explain these complex dynamics as they play out over time spans of centuries to millennia. The adaptive cycle (Holling 1986) and cross-scale panarchies (Gunderson and Holling 2002) are two key theoretical constructs that help explain long-term dynamics. These are wonderfully demonstrated in Redman and Kinzig (2003) and in Trosper (2003).
Even if prediction and planning systems become possible, and they can be constrained to the more benign range of possible outcomes, why would we want to do this? Are such ideas an unrecognized reflection of the mechanical world view of the industrial revolution, steam age mentalities tarted up in information age kit? The reasons often cited point to the seemingly intractable defects of liberal societies, their unfairness and inequities. Other reasons include worries about conflicts as the world effectively shrinks due to the speed and range of our technologies, the growth of population and expected competition and scarcity, and ecological disaster as we foul our nest. Careful and honest advocates of planning systems recognize the loss of something important for humanity in such systems but see them as worth the price, a continuation of the loss of liberty and possibility that has accompanied each step in human social evolution since we first settled down and took up farming.
Others offer a more expansive view of the future that sees the development of modern technologies as enabling an escape from the rigidities we accepted when we settled down and began the 10,000 year climb up the technology ladder. They argue that for the vast majority of the existence of our species we were free of the constraints of impersonal social and hierarchical systems, that we are deeply, physically adapted to liberty and that the best and kindest thing we can do is to guide implementations of advanced technologies toward liberation rather than constraint. They argue that equity is only relevant to societies experiencing scarcity, that economic coercion isn't possible when all have sufficient materials. Knowledge will end material scarcity.
Still others argue that as machine intelligence increases beyond human levels the future becomes unpredictable, not even comprehensible except by analogy since the 'fast folk' will soon be as far beyond human as humans are beyond chimps. They expect that humans will enhance themselves through a combination of mechanical and biological changes enabled by intelligent machines as well as human technological advances. It's unclear what our present political and social musings can contribute to such a world. They may be no more relevant than the grunts and screeches of chimps are to us as they dimly conduct their rudimentary social lives.
The unpredictability of the future doesn't diminish the usefulness of speculation and planning though it constrains the time frame and dissolves the attraction of grand visions and final solutions. Developing ever better methods for modelling near futures, coupled with sensibly modest objectives, can help us avoid some dead ends and wrong turns. By seeking to develop a science of the long view - with testing, feedback and reevaluation - we can reduce the oppressive and proselytory defects of scenario planning as it has been used, as well as improving accuracy.
UPDATE: