# Chapter 18 - Systems, Complexity, and Self-Organization

Take a shallow pan of oil, heat it evenly from below, and beyond a certain critical temperature difference, something remarkable happens: the fluid spontaneously organizes itself into a strikingly regular pattern of hexagonal convection cells, each one a small, self-sustaining loop of rising and falling fluid, tiled across the pan with an orderliness that looks, to an untrained eye, like it must have been deliberately designed. Nobody designed it. No blueprint, genetic or otherwise, specifies the hexagons. They emerge, reliably and reproducibly, purely from the local physics of heat, viscosity, and gravity acting on a simple fluid, a phenomenon called Rayleigh-Bénard convection, and it is one of the founding case studies of an entire field of science - the study of self-organization and complexity - that offers, I will argue in this chapter, some of the most promising, least metaphysically fraught tools available for understanding exactly the kind of robust, convergent pattern formation that first drew Sheldrake toward morphic resonance, without requiring any appeal to non-local fields at all.

## What Self-Organization Actually Means

Self-organization, in the precise sense complexity scientists use the term, refers to the spontaneous emergence of ordered structure or pattern in a system, arising purely from local interactions between the system's components, with no external controller, no central blueprint, and no long-range coordinating signal required. The hexagonal convection cells are a clean physical example; the coordinated, seemingly telepathic movement of a starling murmuration (which we flagged in Chapter 7 as a case where Sheldrake-style field explanations have been offered) is a biological one, now well-modeled through purely local rules - each bird adjusting its position and velocity based only on the handful of nearest neighbors it can actually see, with no need for any bird to perceive or respond to the flock's overall shape, let alone any non-local field connecting distant members.

The key conceptual move complexity science makes, and the one most relevant to evaluating morphic resonance fairly, is this: **emergent, system-wide order does not require system-wide coordination**. This sounds almost paradoxical stated baldly, but it is one of the best-established and most thoroughly modeled findings in all of modern science, and it's worth sitting with a concrete example until it feels intuitive rather than merely asserted. Craig Reynolds's 1987 "boids" simulation - one of the most influential computational models in the history of complexity science, still taught in introductory courses today - demonstrated that a computer-simulated flock, in which each individual "boid" (bird-oid object) follows only three simple local rules (steer to avoid crowding nearby flockmates; steer toward the average heading of nearby flockmates; steer toward the average position of nearby flockmates) produces flocking behavior, including realistic-looking whole-flock maneuvers around obstacles, splitting and rejoining around obstructions, and coordinated turns, that is visually almost indistinguishable from real bird flocking - with absolutely no global rule, no shared field, and no bird (or boid) ever accessing information about the flock as a whole. The apparent "wholeness" of the flock's behavior is a genuine emergent property of the system, in the sense that it's not reducible to any single component's behavior taken in isolation, but it is fully and completely explicable, in the boids model at least, by local rules alone.

## Attractors and the Geometry of Possibility

To understand why this matters for morphic resonance's core claims about developmental robustness and behavioral convergence, we need one more piece of technical vocabulary from dynamical systems theory: the concept of an **attractor**.

Imagine the complete space of all possible states a system could theoretically occupy - for a simple pendulum, this might be described by just two numbers, its current angle and its current speed; for a developing embryo, it would be an almost unimaginably vast space describing the position and chemical state of every cell. A dynamical system's behavior, over time, can be described as a trajectory moving through this abstract "state space," governed by the system's own internal rules - its equations of motion, or in a biological system, its biochemical and mechanical interaction rules. An attractor is a specific region of this state space - sometimes a single point, sometimes a more complex shape, a loop for cyclic behavior, or in more exotic cases a fractal "strange attractor" for chaotic systems - toward which trajectories starting from a wide range of different initial conditions will reliably converge over time, and to which the system will tend to return even after being perturbed away from it, provided the perturbation isn't too severe.

_[Figure: Waddington's epigenetic landscape, redrawn alongside a modern gene-regulatory-network attractor basin diagram. Waddington's ball rolls down a branching canal system toward one of several valleys; the modern diagram shows the same qualitative picture derived from an explicit system of coupled differential equations, with basins of attraction shaded and labeled by cell fate. The point of placing the two images side by side is that the 1940s metaphor and the 2020s mathematics describe exactly the same phenomenon - one intuitively, one derived from measured molecular interaction strengths.]_

This is one of the most useful available frameworks for reinterpreting the developmental robustness and canalization puzzle from Chapter 11. Conrad Waddington's "epigenetic landscape" metaphor of a ball rolling down branching canals toward stable valleys is, in fact, an early, intuitive, pre-formal anticipation of exactly this dynamical-systems concept, and modern computational developmental biology has, in the decades since, begun building genuinely quantitative, mathematically explicit models of specific developmental gene-regulatory networks that demonstrate exactly this kind of attractor behavior directly from the known biochemistry, with no need to posit any additional non-local organizing field. Here is the mechanism stated with a bit more precision than the metaphor alone provides: a network of mutually regulating genes can be written as a system of coupled differential equations, one for each gene, describing how its expression level changes as a function of every other gene's current expression level. Solve that system numerically, starting from many different initial conditions - representing different early perturbations, different starting concentrations of key regulatory molecules - and you find that trajectories don't wander freely through the full space of mathematically possible states. They collapse onto a small, discrete number of stable attractor states, each one corresponding to a specific cell type or developmental outcome, with the vast majority of the theoretically possible state space simply never visited by any realistic trajectory. That collapse - measured, published, reproduced across multiple model organisms and multiple independent research groups - is a rigorous, quantitative, entirely conventional mechanistic account of exactly the "found its way back to the right answer despite perturbation" behavior that the Spemann-Mangold organizer experiment demonstrated a full century before anyone had the mathematics or the computing power to model it directly, and that motivated Sheldrake's search for a non-physical organizing principle in the first place.

## Why This Doesn't Fully Dissolve the Puzzle

A fair reading of the complexity-science literature suggests it provides a genuinely powerful partial answer to the morphogenesis puzzle, not a complete one, and understanding exactly where the partial answer runs out matters for evaluating morphic resonance fairly rather than dismissively.

Attractor dynamics, as actually demonstrated in specific computational gene-network models, explain how a given, already-specified network of interacting genes and molecules can reliably converge on a small number of stable states from a range of starting conditions. What this framework does not, on its own, explain is why the specific network topology (which genes regulate which other genes, with what strengths and what response curves) that a given species happens to have evolved produces attractors corresponding to a coherent, viable, species-typical body plan in the first place, rather than producing attractors corresponding to some other, arbitrary, non-functional configuration - that explanatory burden falls to evolutionary history (natural selection, over immense timescales, having sculpted the network topology itself into one that produces functional attractors) rather than to attractor dynamics as a general mathematical framework, which is agnostic to whether a system's particular attractors happen to be biologically useful or not. This is not a weakness of complexity science exactly - it's a proper division of explanatory labor, with evolutionary biology explaining why particular network topologies exist and dynamical systems theory explaining why, once a topology exists, it produces robust convergent behavior - but it does mean the full, satisfying, first-principles account of biological form that Sheldrake found lacking in 1970s developmental biology has still not been fully assembled even today, in the sense of a single unified theory rather than a well-coordinated division of labor across several distinct fields (evolutionary biology, gene network dynamics, mechanical morphogenesis) each contributing a genuine piece.

## Autopoiesis and the Boundary Problem

One further concept from systems theory deserves mention, both for its own interest and because it bears directly on the "morphic unit" boundary problem we flagged as unresolved back in Chapter 3: the concept of **autopoiesis** (literally, "self-creation"), developed by the Chilean biologists Humberto Maturana and Francisco Varela in the 1970s, working independently of but roughly contemporaneously with Sheldrake's own early formulations. Maturana and Varela proposed autopoiesis as a rigorous, structural definition of what makes something a genuine living system, or more broadly a genuine self-organizing "whole," rather than an arbitrary collection of parts: an autopoietic system is one that continuously produces and regenerates its own components and its own boundary, through its own internal network of processes, in a way that maintains its own organizational identity even as its specific material constituents are continuously replaced (your own body, for instance, replaces the great majority of its constituent molecules over a period of years, while remaining recognizably, continuously "you" in organizational terms).

This is directly relevant to the "what counts as a morphic unit" problem from Chapter 3, because autopoiesis offers a considerably more rigorous, structurally specified, independently checkable criterion than Sheldrake's own qualitative, degree-of-coherence-based description: on the autopoietic criterion, a rock is clearly not a self-organizing whole of the relevant kind, since it does not actively regenerate its own boundary or internal organization through any ongoing process, while a cell, an organism, and arguably certain higher-order social or ecological systems that maintain their own organizational identity through continuous component turnover, plausibly are. Sheldrake has, in his own later writing, referenced autopoiesis approvingly as a compatible, complementary framework.

> **A research seed worth flagging on its own.** This is, I'd argue, one of the more genuinely productive and currently unclaimed pieces of theoretical synthesis work available anywhere in this book: formally translating Sheldrake's morphic unit concept into precise autopoietic terms - specifying, for a candidate morphic unit, exactly which self-production and boundary-maintenance criteria it does and doesn't satisfy - and seeing what falls out of that translation. Done rigorously, it would do two things simultaneously: sharpen the vaguest, most heavily criticized part of Sheldrake's framework (the boundary-condition problem identified in Chapter 3) into something independently checkable, and reveal, precisely, which of Sheldrake's broader claims survive the translation intact and which quietly depend on the vagueness being removed. This is squarely a philosophy-of-biology thesis, not a laboratory experiment, and it requires no funding beyond a library and sustained attention - which makes it one of the most accessible serious contributions in this entire book for a student without access to a wet lab or a compute cluster.

## Looking Forward

Complexity science and dynamical systems theory offer the single most productive non-mystical toolkit available for taking morphic resonance's underlying motivating puzzle seriously - robust, convergent, self-organizing pattern formation - without requiring any of the physics-defying non-locality Chapter 16 showed to be so problematic. They also, honestly, close off a meaningful portion of the original explanatory gap Sheldrake pointed to in 1970s developmental biology, in much the same way epigenetics, evo-devo, and bioelectric signaling did in Chapters 14 and 15 - while still leaving the specifically non-local, cross-lineage claim, the actual distinctive core of morphic resonance, entirely untouched, since attractor dynamics, boids simulations, and autopoiesis are all, notably, fully local theories, explaining emergent order through nearby interactions rather than through any distance-independent resonance.

Chapter 19 shifts from systems and matter to mind, taking up the philosophically thorniest territory in the whole book: panpsychism, the combination problem, and the question of whether taking morphic fields seriously requires also taking seriously the idea that some rudimentary form of experience or proto-consciousness might be a more basic and widespread feature of physical reality than mainstream physicalism assumes - a question Sheldrake has increasingly leaned into in his later work, and one that deserves the same careful, non-dismissive, non-credulous treatment we've tried to apply to every other theoretical avenue in this Part.