Overview

We seek to identify and generalize underlying quantitative patterns shared by nominally unrelated complex evolving systems, all the while maintaining an optimism that a general theory of complexity is within reach.

Complex systems science is an effort to discern and theorize common patterns in complex systems from multiple scientific perspectives. Many scientific disciplines are already associated with powerful models and theories: in biology, for example, there is the theory of evolution; in economics, there is utility maximization and game theory; and in engineering mathematics, there is Alan Turing’s theory of computation.

Complexity seeks to connect and generalize these theories, to find explanatory and predictive frameworks that allow us to, for example, describe biological mechanisms in computational terms or social structures in energetic terms. For the last three decades we have been steadily surveying the landscape of complex phenomena, and along the way we have found encouraging similarities between nominally unrelated complex systems. In applying quantitative methods across disciplines, we’ve developed a broadly useful toolkit for revealing the underlying quantitative order in evolving complex systems.

The now-firmly established field of network science, for example, forged in studies of the dynamics of human social relationships, has been generalized for the quantitative analysis of nearly any interacting, evolving system—from the food webs in ecosystems to the metabolic chemical reactions that precipitated Earth’s first living thing. A theoretic framework from biology known as allometric scaling has been extended to quantitative theories of cities and urbanization. Agent-based computational models refined for studying the spread of disease—in which many thousands of individual computer programs interact with each other in highly contingent webs of decision making—are today rapidly and decisively redefining economic theory.

As we continue our search for order in complex phenomena—holding to optimism that a general theory of complexity is within reach—we are identifying and generalizing quantitative approaches from across the scientific landscape to explain and predict the behaviors of many types of complex systems. We relentlessly extend and expand our exploration, relying on cross-disciplinary catalysis, scientific and quantitative rigor, and creative curiosity.