  To date, formal models of collective intelligence have lacked a plausible
mathematical description of the relationship between local-scale interactions
between highly autonomous sub-system components (individuals) and global-scale
behavior of the composite system (the collective). In this paper we use the
Active Inference Formulation (AIF), a framework for explaining the behavior of
any non-equilibrium steady state system at any scale, to posit a minimal
agent-based model that simulates the relationship between local
individual-level interaction and collective intelligence (operationalized as
system-level performance). We explore the effects of providing baseline AIF
agents (Model 1) with specific cognitive capabilities: Theory of Mind (Model
2); Goal Alignment (Model 3), and Theory of Mind with Goal Alignment (Model 4).
These stepwise transitions in sophistication of cognitive ability are motivated
by the types of advancements plausibly required for an AIF agent to persist and
flourish in an environment populated by other AIF agents, and have also
recently been shown to map naturally to canonical steps in human cognitive
ability. Illustrative results show that stepwise cognitive transitions increase
system performance by providing complementary mechanisms for alignment between
agents' local and global optima. Alignment emerges endogenously from the
dynamics of interacting AIF agents themselves, rather than being imposed
exogenously by incentives to agents' behaviors (contra existing computational
models of collective intelligence) or top-down priors for collective behavior
(contra existing multiscale simulations of AIF). These results shed light on
the types of generic information-theoretic patterns conducive to collective
intelligence in human and other complex adaptive systems.
