Chapter 13. Visions and Challenges – Priorities for Professionals

Foresight is Becoming Evidence-Based and Scientific

Foresight today is not a science, and it will never fully become one. But as our digital platforms, data, algorithms, theories, and models improve, looking to the future will move from today’s intuitive, data-poor, and ungrounded theoretical and methodological approaches to a more balanced blend of art, empiricism and science.

As always, our most universal foresight theories and models will continue to grow out the natural sciences (physics, chemistry, biology, neurobiology, statistics) and social sciences (psychology, anthropology, sociology, economics, political science). More specifically, fields like astrobiology (what features of life are likely to be the same, on all planets?), complexity studies (what is a complex adaptive system?), acceleration studies and information theory (why do we see accelerating complexification and information production across all universal and Earth history, even during catastrophes, and how can we better describe and predict it?), evolutionary developmental biology (how do evolution and development interact in complex systems?), neurobiology and cognitive science (how do living beings think, and form models of the future?), nanotechnology (why do the realms of the very small yield such big productivity advances?), and computer science (what is machine intelligence and autonomy, and why do they keep accelerating?) are just some of the sciences that will help us better understand what is predictable and what is not.

All of these in turn will advance the science of probability (how do we reason from incomplete information?), discussed earlier.