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Quantum uncertainty and wavefunction collapse remain among the most conceptually unresolved aspects of microscopic physics. Here, we investigate whether uncertainty and collapse-like electron localization may admit a complementary thermodynamic interpretation through recursive entropy-field dynamics. The electron is modeled as a dynamically evolving entropy field geometry composed of structural, electromagnetic, and thermal entropy components maintained through continuous recursive interaction with the surrounding vacuum entropy field. Within this framework, uncertainty emerges from incomplete temporal accessibility to rapidly evolving recursive electron configurations occurring beneath experimentally accessible timescales. Repeated recursive phase sampling naturally produces probabilistic measurement statistics and approximately Gaussian localization statistics. Electron–photon interaction is further modeled through phase-matched recursive entropy coupling, where repeated entropy transfer progressively reorganizes the electron entropy geometry toward localization. Numerical simulations reproduce finite-width Dirac-delta-like localization behavior, localization saturation after a finite number of recursive cycles, and intrinsically nonzero collapse timescales, with a lower bound of approximately 3.2 × 10−20 s for an electron at rest under ideal recursive coupling. These results suggest that wavefunction collapse may emerge as a finite recursive thermodynamic localization process rather than an instantaneous state projection. The framework further provides a qualitative thermodynamic interpretation that relates normalized recursive accessibility to Born probability, discusses recursive entropy evolution as a possible physical basis for intrinsic quantum timescales, and outlines how Bell-type correlations may emerge within an extended recursive entropy framework. Together, these results establish a phenomenological foundation for exploring recursive entropy dynamics underlying quantum uncertainty and localization while identifying model-dependent predictions that may provide targets for future experimental investigation.
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John T. Solomon (2026) studied this question.
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