Accelerating Change 2005. September 16-18, Stanford University. Artificial Intelligence and Intelligence Amplification. Transforming Technology, Empowering Humanity

How Do We Improve the Study of Accelerating Change?

One of ASF's long-term goals is to improve the way we look at the future by encouraging the development of multidisciplinary educational programs exploring technological forecasting, futures studies and accelerating change at the undergraduate, professional, and graduate level. We see this as helpful in at least two broad academic domains, the first near-term, applications and technology oriented, the second longer-term, big picture and systems oriented:

1. Acceleration Studies (a predictive, policy, applications-oriented program) and
2. Development Studies
(a technical, theory-oriented program).

1. Acceleration Studies at the graduate level might reasonably include aspects of such subjects as forecasting, roadmapping, scenario development, futures studies, risk management, science and technology studies (infotech, physics, nanotech, biotech, neuro and cognitive science), productivity metrics, technology assessment and policy, history of science and technology, cybernetics, sociology and economics, information science, productivity metrics, engineering and operations research. In addition to baseline forecasting, this program would focus on the benefits, choices, and risks of a range of potential developments in accelerating systems of change, and would necessarily also consider the emerging sociopolitical and ethical issues of rapidly increasing technical productivity and machine intelligence. Today's science and technology studies, operations research, and technology policy graduate programs offer a useful start toward this kind of curriculum, but need to become more acceleration-aware.

2. Development Studies at the graduate level might reasonably include aspects of such subjects as evolutionary developmental (evo-devo) biology, niche construction theory, systems biology, astrobiology and astrophysics, complex systems research, nonlinear mathematics, phase transition theory, hierarchy theory, catastrophe theory, anthropic theory, theory of computation, engineering, physical and social science, cybernetics, information and autonomy theory, philosopy of science and technology, and other disciplines relevant to modeling the accelerating development of physical domains of change in a range of universal and local systems. This program would focus on dynamical models of change in complex systems, including the universe as a complex system, and would necessarily also consider philosophical and teleological issues of the meaning and purpose of universal change in relation to current scientific theory and technical developments at individual, institutional, national, and global scales. Again, today's complex systems graduate programs provide a useful start toward this kind of curriculum, but still have some shortcomings with regard to broadly modeling accelerating change.

A good understanding of science, technology, and social history is vital to modeling accelerating change, and should be a necessary prerequisite to graduating methodologically-sound technology consultants and "futurists," in the ASF definition. In skimming through much of the futures studies work since the early and more rigorous efforts of 1970's, it becomes clear that many futurists in recent decades have been both forecasting-challenged and science and technology-unaware. Fortunately, there has recently been a resurgence of higher quality, forecast-supported, science and technology informed futures work.

Harold Linstone, editor of the journal Technological Forecasting and Social Change, is one example of the scientifically-grounded futurist we will need more of to build foresight in coming decades. When futurists require predictive validity as a basis for their efforts, they rapidly come to understand that only a special subset of future events are particularly easily predicted, making them uniquely important to model and understand, from a policy perspective.

As Stephen Steele notes, at present there are few futurists engaged in falsifiable predictions. This is a real shortcoming to our profession, as there are a host of "developmental" processes we can predict with high certainty given past history, processes advanced by accelerating technological change and the economic development such change enables. Such processes don't revert periodically to baseline, as do so many cyclical or pendular social changes (market bull/bear cycles, political centralization/decentralization cycles, etc.), but instead continue to advance more or less steadily, irrespective of culture. Such obvious trends as increasing globalization, higher GDP and per capita incomes, more democracy (at least at the national level), greater informational transparency, less warfare, faster and smarter computers, and many other less obvious ones, such as the increasing "weightlessness" of GDP in advanced nations, decreasing world population, and increasing environmental responsibility and energy efficiency of human society, both in developed and emerging nations, all appear to be in this class, and need to be much better understood.

Accelerating change is something that strategic decisionmakers, mainstream futurists and average citizens all need to understand better in order to substantially improve our collective decisionmaking. ASF believes everyone with an interest in the future needs to work together to help make predictable developmental processes, enabled by accelerating technological change, much more obvious to the general public. We will do our part in coming years to advocate for statistically-backed prediction as a core futurist methodology.

Key Questions
How does computation affect our environment?
What is accelerating technological change?
Why is accelerating change important?
What is the universal story of accelerating change?
What is the "technological singularity" hypothesis?
Where might accelerating change take us in the 21st century?
What are our main benefits and risks with regard to accelerating change?
How do we improve the study of accelerating change?


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