We explore this question using minimal two-scale models of collective patterning and decision-making. We find that the subunits of the collective segregate and integrate in patterns that reflect the kind of collective intelligence that’s required to solve the problem at hand. I'll present three examples that illustrate this idea. The first example is a multicellular model of the developing embryo consisting of the physiological and genetic scales and develops a simple linear physiological gradient from homogeneous conditions. We show that this model employs an emergent macroscale causal network that essentially compresses the network into two nodes with positive and negative interactions a way that explains how the collective generates the gradient pattern. The second example is a multicellular model of the neural plate of xenopus embryos consisting of the bioelectric and genetic scales underlying brain development in frog embryos, where a "correct" bioelectric pattern is required for normal development of the brain. We show that this model employs a second-order causal network comprising of higher-scale-to-lower-scale interactions to forge a collective decision. This network shape-shifts over time, forming clusters that slide across the tissue and influencing genes in distant cells in alternating positive-negative fashion, suggesting how bioelectric information is integrated over space and time. Our final example is a multicellular bioelectric model endowed with an electrostatic force field that regulate each other and designed for the purpose of developing a minimal vertebrate face pattern. This model exploits the long-reach of the electric field to produce a causal network where the cells on the boundary of the pattern drive the activity in the bulk. This strategy facilitates a stigmergic mechanism where the bulk and boundary trigger off each other in a sequential fashion to build the parts of the face pattern.
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