Collective Computation Group @ SFI

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 fundamental problems in evolutionary theory concerning collective behavior, collective computation, and collective intelligence—at all levels of biological organization—from societies of cells to societies of individuals to machine-human hybrid societies. Our work uses insights and tools from biology, statistical physics, cognitive science and neuroscience, complexity science, animal behavior, information theory, theoretical computer science, and dynamical systems. Our work is empirically grounded and often motivated by deep understanding of model systems.

RESEARCH TOPICS

  • COLLECTIVE COMPUTATION OF FUNCTIONAL MACROSCOPIC WORLD
  • SPACE & TIME IN ADAPTIVE SYSTEMS: INCLUDING ORIGINS OF SPATIAL & TEMPORAL STRUCTURE & IMPLICATIONS FOR UNCERTAINTY REDUCTION & PREDICTION
  • BIOLOGICAL STOCHASTIC CIRCUIT CONSTRUCTION & EVOLUTION
  • THEORY OF COLLECTIVE BEHAVIOR
  • INFORMATION PROCESSING: INTELLIGENCE & INFERENCE, COARSE-GRAINING, & COMPRESSION & DOWNWARD CAUSATION IN ADAPTIVE SYSTEMS
  • ROBUSTNESS, REGULATION, & CONFLICT MANAGEMENT
  • CONTROL & TUNING OF BIOLOGICAL AND SOCIAL SYSTEMS
  • CRITICAL & TIPPING POINTS IN SMALL-SCALE ADAPTIVE SYSTEMS
  • INDUCTIVE GAME THEORY (strategies, pay-offs, and game structure from time series data)
  • STRATEGY AND DECISION-MAKING BY INDIVIDUALS AND COLLECTIVES
  • INDIVIDUALITY IN EVOLUTIONARY THEORY
  • INVENTION & INNOVATION

MODEL SYSTEMS & DATA
Time series and network data from strategic, adaptive systems including animal and human societies, neural systems, and (new project) slime molds. We like systems that have the minimum degree of relevant complexity for the problems we are trying to solve.

Our collective computation work has so far focused on primate societies, the world wide web, and neural 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.


SFI | @C4COMPUTATION | C4VIMEO | JESSICA FLACK | DAVID KRAKAUER