arXiv:2606.28295v1
Abstract
The competitive equilibrium of general equilibrium theory exists as a fixed point and is, by the theorys own results on aggregate excess demand, in general silent on whether that fixed point is unique, stable, or attained. This paper takes the economy to be not a configuration to be solved for but a process to be recovered, an asymptotically mean stationary information source carrying a partially identified operator of statistical dependence, populated by agents that are finite-capacity information channels. Within this adaptive order the competitive, rational expectations equilibrium is recovered exactly, as a joint limit taken along an explicit scaling path. Three parameter limits and two fixed-point conditions deliver it, the entropy rate falls to zero, agent channel capacity diverges, selection intensity grows infinitely sharp, adaptive learning reaches its expectationally stable rest point, and the recovered structure ceases to coevolve. At that corner the limiting object satisfies the axioms of the canon and its rest state is a Walrasian equilibrium, away from it the adaptive economy is a strict generalisation, carrying a positive entropy rate and a recovered dependence structure that the equilibrium primitive cannot express. We give the nesting as a theorem, establish the result by result correspondence with existence, with the Sonnenschein Mantel Debreu indeterminacy, and with the regular economies recovery, and characterise exactly what the equilibrium limit erases.
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Detailed mathematical audit
01Statements3 reported findingsContains wrong statements
Several formal claims used by the proposed nesting theorem are false or not mathematically specified. In particular, arbitrary random preference relations are not equivalent to random utilities, and the claimed universal eigenpair-recovery threshold is not a valid spiked-covariance theorem.
Random relations are not equivalent to random-utility fields
Page 8 · Definition 6 · arXiv:2606.28295v1
A scalar utility function induces the complete transitive relation exactly when . A probability law on such functions can therefore represent only stochastic choices arising from mixtures of complete transitive rankings and obeying the associated random-utility constraints. Definition 6 allows arbitrary binary relations, explicitly including intransitive cycles and context-dependent reversals, and then calls a random-utility representation 'equivalent.' For three alternatives, the deterministic cycle is an immediate counterexample: no real-valued can satisfy all three strict inequalities.
Full paper, version 1 ↗The eigenpair threshold and consistency claim are not universal
Pages 7–8 · Proposition 5(iv) and proof sketch · arXiv:2606.28295v1
The paper asserts recovery whenever without specifying a covariance model, normalization, aspect-ratio limit, or bulk spectrum. Even in the standard rank-one spiked covariance model with , the BBP transition is expressed relative to the bulk covariance, and above threshold the empirical eigenvector has a limiting overlap strictly below one for a fixed spike; it is not a consistent estimator of the population eigenvector. Thus neither the threshold nor the stated conclusion follows from the cited phase transition.
The hypotheses do not imply a Walrasian equilibrium
Pages 11–13 · Theorem 11 and Lemma 12 · arXiv:2606.28295v1
Conditions (A1)–(A4) concern a Lyapunov function, selection, learning, and structural fixity. No commodity space, endowments, budget correspondence, excess-demand function, or market-clearing condition is defined in the theorem. The proof nevertheless adds 'satisfying the market-clearing conditions' only at the conjunction step. That conclusion is not a formal consequence of the listed assumptions. Lemma 12 likewise invokes uniform convergence in the other parameters, but Definition 10 and (A1)–(A4) contain no such uniformity hypothesis.
02Proofs2 reported findingsContains incorrect or incomplete proofs
The central arguments are proof sketches that assume the conclusions they need: a covariance is identified with an unspecified operator, market clearing appears without a model that defines it, and path independence is deduced from uniform convergence that was never assumed or established.
Second-order comovement does not identify the asserted operator without a forward model
Pages 7–8 · proof sketch of Proposition 5 · arXiv:2606.28295v1
A covariance form is a positive self-adjoint operator, but the paper's directed dependence operator is introduced independently. No equation maps to the observed covariance. Consequently, the sentence that covariance 'determines, and is determined by' is an additional identification assumption, not a proof. The later resolvent and finite-sample claims therefore have no defined statistical experiment from which they can follow.
The limiting conclusion is inserted rather than derived
Pages 28–29 · proofs of Theorem 11 and Lemma 12 · arXiv:2606.28295v1
Laplace concentration of a finite Gibbs law proves only choice concentration at a unique maximizer; it does not construct prices or prove feasibility and market clearing. The final paragraph simply declares those missing properties. The Moore–Osgood interchange in Lemma 12 requires uniform convergence or joint continuity at the corner, but the proof asserts both without deriving them from the standing assumptions.
03Novelty0 reported findingsNo non-novelty findings
No non-novelty findings.