A neuroscience-informed theory of value: from neural computation to market equilibrium

Purpose This paper develops a neuroscience-informed theory of value linking neural computation to market equilibrium. Design/methodology/approach Valuation is modeled as a material neural signal externalized as price and disciplined through recursive gain, loss and constraint. Findings Prices emerge from interaction among cognitively heterogeneous agents and exhibit bounded convergence toward equilibrium under gain-maximizing valuation regimes, while alternative valuation regimes generate alternative convergence patterns or none at all. Perfect price uniformity is impossible due to irreducible neural variance. Originality The paper introduces the Law of Price Convergence and an associated impossibility theorem, deriving price formation directly from neural computation rather than assumed preferences. Equilibrium is reframed as distributed prediction-error minimization, yielding a unified biological foundation for value.