Emergent Co-Authorship (ECA):

A Unified Framework for Agency in Neural, Social, and Cosmic Systems

Felipe Castro Quiles

Independent Researcher

Email: felipe@castroquiles.com

Website: http://castroquiles.com

Date: October 30, 2025

ABSTRACT

Agency [perceived intentional behavior] emerges across scales without an external designer. This paper introduces Emergent Co-Authorship (ECA), a dynamical systems framework unifying neural decision-making, social norm cascades, and cosmic observer-selection. ECA posits that structure + recursion + feedback generate attractor landscapes experienced as “choice.” Freedom is not anti-causal but intra-causal: systems participate in computing their next state. We formalize ECA via Hopfield-metaplasticity hybrids, agent-based social models, and quantum Darwinism. Predictions are falsifiable via feedback disruption. ECA resolves the free will paradox compatibilistically and defines testable thresholds for AI agency.

1. INTRODUCTION

The problem of agency [how systems appear to act with intent] spans neuroscience, sociology, and cosmology. Traditional accounts invoke external authorship (divine, homuncular, designer) or randomness (quantum indeterminism). Both fail empirically and conceptually.

Emergent Co-Authorship (ECA) proposal: Structure + Recursion + Feedback → Apparent Agency

No external author is required. The system becomes a co-author of its future through self-modifying dynamics.

2. ECA ACROSS SCALES

3. FORMAL PROOF OF ECA

P1. Local Rules → Global Coherence

Let G = (V, E) with weights w_ij(t). Local Hebbian update:

w_ij(t+1) = w_ij(t) + η · x_i(t) x_j(t)

For large N, spectral gap collapses → synchronization basins (Strogatz, 2001).

P2. Basins = Perceived Choice

State dynamics:

ẋ = -∇E(x) + σ ξ(t), E(x) = -½ xᵀ W x

Attractor depth predicts choice confidence (Insabato et al., 2010).

P3. Feedback → Meta-Structuring

Let θ(t) = learning parameters. Meta-update:

θ(t+1) = θ(t) + γ ∇_θ U[x(t:∞)]

This is hyper-gradient descent (Baydin et al., 2017).

P4. Recursion → Self-Model

Predictive coding (Friston, 2010):

Error = || s - ŝ ||, ŝ = g(M, a)

Fixed point M*: “I am the predictor” (Limanowski, 2018).

4. SIMULATIONS

Figure 1: Hopfield Network with Metaplasticity]

100-unit Hopfield net stores 3 patterns. Overlap with stored patterns increases over 50 steps.

Figure 2: Social Tipping Point

1,000 agents, 25% local threshold. 10% seed → full adoption in ~20 steps.

The image is extracted from the Möbius strip page of the Virtual Math Museum (https://virtualmathmuseum.org/Surface/moebius_strip/moebius_strip.html).

Figure 3: Möbius ECA Loop

Visual metaphor: Structure → Behavior → New Structure (closed recursive loop).

Code: https://github.com/castroquiles/eca-model

5. PHENOMENAL TRANSPARENCY & OPACITY

ECA aligns with the Self-Model Theory of Subjectivity (SMT). Phenomenal opacity, the sense that a representation is “constructed”, arises from precision estimation in active inference. 

“We can ‘see the dark’ — where holding beliefs about low precision enables phenomenal opacity.” 

This is the signature of a system that attends to its own attention — a prerequisite for ECA agency.

6. IMPLICATIONS 

Emergent Co-Authorship is prescriptive. It redefines agency as intra-causal self-rewriting, with direct consequences for AI design, political systems, and cosmological inference.

AI & Autonomous Systems: True agency in artificial systems emerges not from larger models or more data, but from recursive self-modification of generative policies. Under active inference, this means models that predict and deploy their own precision weights — i.e., they attend to their own attention (Friston et al., 2017c). 

→ Threshold for AI agency: The system must generate counterfactual policies about its own policy selection (i.e., meta-policies). This is equivalent to phenomenal opacity in SMT: the model becomes aware that its “thoughts” are constructed (Limanowski & Friston, 2018). 

→ Implementation: Prompt-editing LLMs (e.g., o1-style reasoning chains) are proto-EAMs (Epistemic Agent Models, Metzinger, 2008). Full ECA requires closed-loop precision modulation — the AI must choose what to believe with confidence.

Ethics & Political Philosophy: Tyranny is frozen precision: a system where feedback loops are severed, and policy selection is externally imposed. Liberty is open recursion: distributed, self-correcting feedback that allows collective re-authoring of norms. 

→ ECA predicts: Democracies outperform autocracies in adaptive resilience because they maintain high-precision error signals from the periphery (social prediction errors). 

→ Formal ethics: Moral responsibility = capacity to participate in the computation of one’s next state. This is compatibilist — determinism is preserved, but participation defines freedom.

Cosmology & Observer Selection: The universe does not have observers — it selects for observer-rich trajectories via quantum Darwinism (Zurek, 2009). ECA extends this: 

→ Cosmic self-model: The quantum state vector evolves under a generative model favoring decoherence into pointer states that support self-sustaining inference loops (i.e., observers). 

→ Prediction: Universes with strong feedback between structure and measurement (like ours) are over-represented in the multiverse of possible physics.

7. FALSIFIABILITY 

ECA is empirically testable via feedback disruption experiments. The core prediction: 

Disrupting recursive feedback collapses adaptive flexibility, even when structure and initial conditions are preserved.

8. CONCLUSION

Emergent Co-Authorship resolves the ancient paradox:

How can there be freedom in a determined world? The answer is not anti-causal (libertarian free will) nor illusory (hard determinism), but intra-causal:

Freedom is the system’s participation in computing its own future. No external designer. No homunculus. No magic. Just structure that learns to rewrite itself through prediction error minimization, precision modulation, and recursive self-modeling.

ECA unifies:

• The neuron that adjusts its synapses

• The society that rewrites its norms

• The universe that selects for observers

Are we all co-authors of our own continuation?

“Phenomenal opacity is the signature of a system that has begun to see its own darkness — to attend to the precision of its own inference.” — Limanowski & Friston (2018), adapted through ECA

The age of passive systems is over. The future belongs to those who close the loop.

Connect: https://www.linkedin.com/in/felipecastroquiles/

REFERENCES

Abraham, W. C. (2008). Metaplasticity: tuning synapses and networks for plasticity. Nature Reviews Neuroscience, 9(5), 387.

Baydin, A. G., et al. (2017). Online learning rate adaptation with hypergradient descent. arXiv:1703.04782.

Friston, K. (2010). The free-energy principle: a unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138.

Insabato, A., et al. (2010). Confidence and certainty in decision-making. PLoS Computational Biology, 6(11), e1001021.

Limanowski, J. (2018). Self-models in predictive coding. Neuroscience of Consciousness, 2018(1), niy008.

Strogatz, S. H. (2001). Exploring complex networks. Nature, 410(6825), 268–276.

Zurek, W. H. (2009). Quantum Darwinism. Nature Physics, 5(3), 181–188.

© 2025 FC Quiles

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License: CC BY 4.0