Excerpt

Ilya Prigogine and Grégoire Nicolis speculate about the future of the science of complex systems after first making clear what they mean when they talk about complexity. At the time of writing, 1985, chaos theory was at the height of its development, Mitchell Feigenbaum had just understood the foundations of bifurcations in nonlinear maps, several Italians—including Giorgio Parisi—had just uncovered the mechanism of stochastic resonance as an explanation for (non–human made) climate change, Manfred Eigen published his idea of the hypercycle, and Prigogine had just cashed in his Nobel Prize on the role of irreversible processes on structure formation. It is the fascination with these developments that is reflected in this paper. From today’s perspective, they entertain a rather limited notion of complexity: Complex systems are nonlinear and dissipative (not in equilibrium); they are characterized by bifurcation phenomena and self-organization. Noise and stochasticity play the role of a decision-maker whenever bifurcations allow for multiple possible options in the evolution of a system. But even in the mid-1980s this was a limited view, given that the Santa Fe Institute was founded in 1984 by people who had on their minds concepts like algorithmic complexity, energy landscapes, agent-based models, networks, genetic algorithms, and co-evolution, and who wanted to explain stock markets, genetic networks, the origin of languages, biological evolution, etc.

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