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Topological and Computational Unification of Emergent Agency
In the paper's words
This manuscript addresses the foundational tension between microscopic superdeterminism and macroscopic emergent agency through the lens of complex systems theory. By synthesizing the computational irreducibility of cellular automata with the geometric constraints of fractal invariant sets, we propose a unified constructal determinism model. We demonstrate that what appears as fundamental quantum randomness is effectively an epistemic artifact of irreducible deterministic complexity, quantifiable via a novel Lossless Complexity Index (LCI) derived from Lyapunov spectra and fractal dimension (LCI ≈ 1.83).
The abstract, verbatim, and its opening sentence is the claim. Every line below is quoted from the text.
Numbers it reports
In our validation sample of random triplets interpreted via p-adic valuation, the ultrametric violation rate was exactly 0.0% (Khrennikov, 2022).
from 4.3 TheoreticalThe paradigmatic example of this phenomenon is deterministic chaos, where the evolution of a system is rigorously bound by differential equations, yet the trajectory is hypersensitive to minute variations in initial conditions (Lorenz, 1963).
from 2.1 Deterministic Chaos andThe principle of computational irreducibility asserts that for a significant class of deterministic systems, there exists no analytical shortcut to predict the future state; the system itself is the most efficient computer of its own future (Wolfram, 2002).
from 2.3 ComputationalThe driver node identified in Section 5.1 is not an external ghost in the machine; it is the thermodynamic agent itself—a knot of low entropy that, by virtue of its structure, gains leverage over the surrounding constraint network.
from 5.3 Thermodynamics of Agency
How it is done
Recent critiques argue that the assumption of statistical independence in Bell’s theorem is physically unjustified in a universe governed by ubiquitous conservation laws and non-linear feedback loops (Hossenfelder & Palmer, 2020).
from 1.1 The Deterministic ParadoxThe geometric approach excels at describing the static topology of the state space (the what ) but often lacks an explicit generative mechanism for the time-evolution of the system.
from 1.2 Literature LandscapeThis study employs a multi-modal methodological framework, synthesizing theoretical analysis with computational verification.
from 1.5 Methodological ApproachSection 3.0 (Methodological Framework) introduces the computational geometry synthesis, formally deriving the Lossless Complexity Index (LCI) and establishing the p-adic metric framework.
from 1.7 Document Roadmap
What would falsify it
If the constraint network has a scale-free topology, then influence can propagate from micro to macro via hub constraints, bypassing the expected thermal damping.
from 1.3 Problem Statement: TheThe principle of computational irreducibility asserts that for a significant class of deterministic systems, there exists no analytical shortcut to predict the future state; the system itself is the most efficient computer of its own future (Wolfram, 2002).
from 2.3 ComputationalIf the universe operates as a cosmic PRNG, then statistical independence is an approximation that holds only because we cannot reverse-engineer the seed.
from 2.4 The Illusion of Randomness
Where the claim stops
Finally, the model assumes that the constraint network topology remains stable over time.
from 6.4 Limitations of the ModelAt the core of this paradox lies the assumption that determinism necessitates predictability, a conflation that was mathematically severed by the discovery of deterministic chaos, where simple non-linear systems generate behavior that is indistinguishable from randomness to a finite observer (Lorenz, 1963).
from 1.1 The Deterministic ParadoxHowever, contemporary re-evaluations suggest that this dismissal may have been premature, stemming from a failure to appreciate the systems-theoretic implications of a globally constrained universe.
from 1.1 The Deterministic Paradox
Where to read
- Addressing24 words · 1 min
- Abstract151 words · 1 min
- 1.0 Introduction2,210 words · 10 min
- 2.01,550 words · 7 min
- 3.01,157 words · 5 min
- 4.0 Analysis I:1,252 words · 5 min
- 5.0 Analysis II:1,270 words · 6 min
- 6.0 Discussion:1,153 words · 5 min
- 7.0 Conclusion and Future710 words · 3 min
- Appendices829 words · 4 min
Read from the paper itself: 63 sections, 10,494 words. Nothing on this page is written by a model; a line that is not a sentence of the paper is not shown. Have a specific question? Ask this paper.