Complexity Futures: New Paradigms 2026 Resources

Friday, April 24 + Saturday, April 25, 2026

OVERVIEW

The philosopher and historian of science Thomas Kuhn distinguished between periods of “normal science” and periods of “revolution.” As he explains in The Structure of Scientific Revolutions (1962), “Normal science, the activity in which most scientists inevitably spend almost all their time, is predicated on the assumption that the scientific community knows what the world is like.” Periods of revolution, or extraordinary science, are those times when we discover that the world and reality are not as we had thought. In the twenty-first century, AI, quantum computing, genetic engineering, new understandings of social and economic systems, theories of consciousness, computational medicine, computational mathematics, and even astrobiology suggest that the world and universe as we know them today might stand to change in profound ways tomorrow.

At SFI’s Complexity Futures: New Paradigms 2026, we investigated the frontiers of complexity science and philosophy, exploring what is implied by the insight that we no longer know “what the world is like.”

To best ground this meeting in ongoing research programs, we explored research at the fraying edges of existing paradigms and ideas for future directions. A few of the topics that we addressed include:

Has intelligence been misunderstood, including what intelligence implies and what it can and cannot accomplish?
Have the limits to prediction been overstated and could new computational methods overcome barriers that classical approaches could not?
Do we really understand the nature of living matter? If not, what new principles are required to make progress in biochemistry and biology?
Can new understandings of self-organized, collective, emergent intelligence give us new ways to think about human social systems?
How do global demographic shifts influence the way society conceives of itself and change future visions of society?
Is science itself poised for a paradigm change through new forms of algorithm-assisted social learning and knowledge synthesis?
Is our long-standing taxonomy of academic disciplines still relevant? Is it a barrier? Do we need new frameworks that apply across multiple domains to better address the scientific and societal challenges of today?