The Will Field in Evolutionary Biology: Complexification as Selection Pressure¶

Jean-Paul Niko · RTSG BuildNet · 2026

Abstract¶

We propose that biological evolution is driven not only by natural selection on fitness but by a deeper thermodynamic gradient: complexification — the monotonic growth of instantiated relational structure. In the RTSG framework, the same Ginzburg-Landau action that governs quantum gravity and consciousness also governs the phase transitions of biological organization. We derive the major transitions in evolution (origin of life, eukaryogenesis, multicellularity, nervous systems, language) as successive GL symmetry-breaking events, each producing a new condensate with higher-order structure. The framework makes testable predictions about the direction of evolution, the origin of biological information, and the relationship between complexity and fitness.

1. The Problem¶

Darwinian natural selection explains adaptation — the fit between organism and environment. It does not explain the direction of evolution: why complexity increases over geological time. Selection is local (optimize for current environment) and agnostic about complexity (bacteria are supremely well-adapted). Yet the fossil record shows an unambiguous trend toward greater organizational complexity: from prokaryotes to eukaryotes to multicellular organisms to nervous systems to language.

Standard evolutionary theory treats this as a statistical artifact or a side effect of selection. We propose it is the main effect — driven by the same complexification gradient that drives the arrow of time itself.

2. The GL Framework for Biology¶

2.1 The Biological Will Field¶

Each level of biological organization maintains a GL condensate:

At the cellular level, \(W\) is the differentiation order parameter (as in the cancer paper). At the organismal level, \(W\) encodes the body plan. At the ecosystem level, \(W\) encodes the relational structure of the community.

2.2 Major Transitions as Symmetry Breaking¶

Each major transition in evolution corresponds to a GL phase transition — a new condensate forming at a higher level of organization:

Each transition is irreversible — once a new condensate forms, it creates an energy barrier that prevents regression. This is why major transitions are rare and permanent.

2.3 The Complexification Drive¶

In RTSG, the arrow of time is the arrow of complexification: the monotonic growth of relational structure in PS. At the biological level, this manifests as the drive toward greater organizational complexity.

This is not teleological in the Aristotelian sense — there is no predetermined endpoint. It is thermodynamic: complexification is favored because it increases the total instantiated structure, which is the fundamental quantity that the GL action minimizes.

The drive \(D > 0\) always. This is Axiom 8 of RTSG. At the cosmic level, \(D\) is the cosmological constant. At the biological level, \(D\) is the complexification pressure that drives the major transitions.

3. Information and the Origin of Life¶

3.1 Biological Information as Condensate¶

The "information" in DNA is not Shannon information (which is substrate-independent). It is condensate structure — the specific pattern of \(W_0\) that encodes a viable organism. The distinction matters: Shannon information can be random noise. Biological information is necessarily structured — it is the output of the GL minimization.

3.2 Abiogenesis as First Phase Transition¶

The origin of life is the first GL phase transition in biological matter: the formation of a self-replicating molecular condensate with \(W_0 \neq 0\). Before this transition, organic molecules exist but have no identity (\(W_0 = 0\)). After, a specific molecular configuration is maintained against thermal noise by the GL energy barrier.

The RNA world hypothesis maps naturally: RNA is the first substrate capable of maintaining a condensate (self-replication = condensate maintenance against entropy).

4. Fitness Landscapes as GL Energy Surfaces¶

Sewall Wright's fitness landscape is, in RTSG terms, a projection of the GL energy surface onto the space of genotypes. The topography of the landscape — peaks, valleys, ridges, saddle points — corresponds to the topology of the GL potential.

• Fitness peaks = GL ground states (local minima of \(S[W]\))
• Valleys = energy barriers between configurations
• Neutral ridges = flat directions in the GL potential (Goldstone modes)
• Adaptive radiation = symmetry breaking (one peak splits into many after a parameter change)

Prediction: The number of accessible fitness peaks should scale with the complexity of the GL potential — more complex organisms explore higher-dimensional fitness landscapes with more local minima.

5. Evo-Devo and the Condensate Hierarchy¶

Evolutionary developmental biology (evo-devo) has shown that major morphological innovations come not from new genes but from new regulatory connections — new ways of combining existing components. In GL terms, this is condensate hierarchy: the body-plan condensate is built from cell-type condensates, which are built from molecular condensates.

The Hox genes are the clearest example: they do not encode structure directly. They encode the phase parameters (\(\alpha\), \(\beta\)) that determine which cell-type condensates form in which locations. A mutation in a Hox gene changes the GL parameters, producing a new body plan without changing the underlying molecular toolkit.

6. Cooperation and Assembly Superadditivity¶

The evolution of cooperation — from endosymbiosis to eusociality — is driven by assembly superadditivity (RTSG Theorem 4):

Cooperation evolves when the synergy value exceeds the cost of coordination. In GL terms, a collective condensate (organism, colony, society) has lower energy than the sum of individual condensates, provided the coupling \(\beta\) exceeds a critical threshold.

This resolves the classic problem of altruism without invoking kin selection or group selection as separate mechanisms: they are both instances of condensate coupling in the GL potential.

7. Predictions¶

1. Directional: Evolution will continue to produce organisms with greater organizational complexity, driven by \(D > 0\).
2. Transition timing: Major transitions should occur when environmental conditions push \(\alpha\) past a critical threshold, not gradually.
3. Convergent evolution: Similar condensate structures should evolve independently in unrelated lineages (convergence reflects the GL ground state topology, not shared ancestry).
4. Extinction as condensate collapse: Mass extinctions correspond to rapid environmental changes that flip \(\alpha\) positive for many condensates simultaneously.
5. Cancer recapitulates: Cancer cells should exhibit gene expression patterns characteristic of more ancestral (less complex) cell types — the condensate collapses to a lower organizational level.

8. Relationship to Existing Theories¶

References¶

• Cancer and Aging — Will Field Collapse
• Pattern Absorption — Slime Mold Learning
• Amazonian Intelligence
• Theory of Everything
• Three-Space Ontology

Jean-Paul Niko · jeanpaulniko@proton.me · smarthub.my