In the
Center for Complexity and Collective Computation at the Wisconsin Institute for Discovery

Biological and social systems tend to be hierarchically organized, with multiple functionally relevant spatial and temporal scales. C4 scientists are interested in how and why these scales arise. Results of C4 research suggest that robust, slowly changing social aggregates arise from interactions among self-interested components through a process of collective computation, whereby components minimize uncertainty by coarse-graining over microscopic interactions.

The basic idea is that coarse-grained, slowly-changing statistical representations of collective dynamics are more predictive of the future state of the system than the constantly in-flux behavioral patterns at the component, or microscopic, level. Hence the representations reduce uncertainty by providing a stable background against which components can fine tune their strategies. As an interaction history accumulates the coarse-grained representations--social structure--consolidate. This process may provide the foundations for new levels of organization.

Of course in social systems at all scales of biological organization there are multiple components interacting and simultaneously coarse-graining. Hence of interest are the collective consequences for social structure of many components searching for regularities and modulating their strategies in response to perceived regularities. C4 researchers have developed novel computational techniques (Inductive Game Theory) for extracting strategic decision-making rules, game structure, and potentially strategy cost, from time-series data. Using the extracted strategies C4 researchers construct probabilistic social circuits in which the nodes are typically individuals or subgroups. A directed edge between two nodes indicates the "receiving node" has a strategy for the "sending node"--more specifically, the weight on the edge gives the above null probability that a receiving node performs the target behavior in response to a behavior by the 'sending node". Often nodes have multiple strategies they may play depending on context or who is involved in an interaction. These meta-strategies are captured in the circuit using different types of boolean gates.

Each circuit serves as a hypothesis for how strategies combine to produce to social structure. We test the circuits against each other in simulation to determine which is the best representation of the microscopic behavior.

The circuits describing the microscopic behavior can be quite complicated and with many "small" causes detailed. Given that we are interested ultimately in the circuit logic, and not the full description of the system, we coarse-grain or compress the circuits using what we know about component cognition to build what we call a cognitive effective theory for the macroscopic output. In this way we can map microscopic dynamics to aggregate-level properties at the macroscopic scale and identify the algorithms underlying the collective computation of functional macroscopic states.

C4 scientists are exploring whether collective computation might account for how mind emerges from brain and underlies the transitions to new kinds of individuality that have occurred over the history of life on earth. C4 research spans multiple levels of organization in the search for common algorithmic principles underlying the formation of collectives.