HOW DO THE NOISY STRATEGIES AND DECISION-RULES OF HETEROGENEOUS ACTORS COLLECTIVELY COMBINE TO PRODUCE EMERGENT FUNCTION AT THE AGGREGATE SCALE?
WHAT WE DO AND WHO WE ARE
We work on how nature collectively computes solutions to problem to overcome subjectivity introduced when noisy sensors estimate regularities in noisy environments. We study how these computations are refined in evolutionary and learning time and their implications for the emergence of biological space and time. We work at all levels of biological organization, from societies of cells, to animal societies, to markets, to hybrid human-machine systems.
We use insights and tools from biology, statistical physics, cognitive neuroscience, complexity science, animal behavior, information theory, theoretical computer science, and evolutionary theory. Our work is empirically grounded and often motivated by deep understanding of model systems.
WHY COLLECTIVE COMPUTATION?
Physics is dominated by concepts like pressure, temperature and entropy. These emerge through simple collective interactions and provide deep insights into the behavior of the physical universe.
Biology makes use of comparable collective concepts, including metabolism, conflict management, and robustness but in contrast to physics, these are “functional” properties. And whereas physics produces order though the minimization of energy, living systems produce order through the addition of information processing. Why biological systems have this extra step and whether it makes them fundamentally subjective and uncharacterizable by laws are big, open questions.
We propose that biological systems overcome the intrinsic subjectivity of information processing by collectively computing (in evolutionary, developmental, or learning time) their own local macroscopic worlds thereby creating or consolidating regularities that can be used to do work efficiently. The macroscopic output can be phenotypic traits, properties of social structure, or properties of system dynamics like the optimal separation of timescales between microscopic and macroscopic behavior.
The broader impacts of this way of thinking have the potential to be enormous. If this view is correct laws operating on universal quantities derived from microscopic processes might also govern biological systems. But in contrast to physical systems identifying these laws in living systems will require a theory of collective computation—an understanding of the algorithms adaptive systems use to compute and how error and imperfect information can be overcome through endogenous coarse-graining and compression to produce slowly changing, predictive, and therefore, functionally useful, aggregate-level features.
Read more about some of C4 research in Quanta Magazine.
SFI | @C4COMPUTATION | SFI COMMUNITY LECTURES | JESSICA FLACK | DAVID KRAKAUER