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Patrick McMillen

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Authored papers (1)

  • Collective intelligence, understood as William James' capacity to reach the same goal by different means, operates not only in beehives and ant colonies but as a scale-free organizing principle across all biological substrates — from gene-regulatory networks capable of Pavlovian conditioning, to Xenopus melanocytes executing all-or-none neoplastic conversion, to planarian fragments stochastically regenerating 1-head and 2-head worms at a stable ~1:2 ratio. The paper introduces the multiscale competency architecture (MCA) as its unifying conceptual instrument, which formalizes how each hierarchical level — molecular, cellular, tissue, organismal, and swarm — navigates distinct problem spaces (metabolic, physiological, morphological, behavioral) and how higher levels deform the energy landscape for subunits without micromanaging them. Specific mechanistic evidence includes: bioelectric disruption of GlyCl-expressing instructor cells in Xenopus tadpoles driving 70% of cohort animals into a fully-converted melanoma-like phenotype with no partially-converted individuals until an AI-parameterized model predicted a drug combination that finally produced them; keratocyte fragments electrotaxing to the anode while intact keratocytes migrate to the cathode, demonstrating that collective behavior can directly contradict the summed tendency of components; and mouse neural crest cells grafted into chick embryos successfully navigating the foreign embryonic face to form teeth, while collectives of rhombomere cells resist neighbor re-induction that overrides individual cells. The SCHEEPDOG electrotactic platform is named as a cross-disciplinary tool for steering keratinocyte collectives with patterned dynamic fields, operationalizing the distinction between individual and collective cell behaviors. The paper argues these examples compel developmental biology, regenerative medicine, and cancer research to adopt behavioral-science formalisms — including active inference, perceptual bistability modeling, and causal information theory — to predict and control large-scale morphogenetic outcomes that molecular pathway mapping alone cannot address.

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