Reality Distortions in Finite Cognitive Systems: A Generalized Bayesian Stochastic Template for Minds, Machines, Markets, and Societies

This paper builds on our previous work on "Apophenic/PROVIDENCE Machines", where we introduced the scalarization viewpoint: when high-dimensional evidence is compressed into a low-dimensional readout, distinct situations can become operationally indistinguishable, making machine "apophenia" a structural possibility. Here we generalize that idea into a mechanism-level template for chunked, resource-bounded cognition under feedback, with an explicit focus on when internal coherence can decouple from reality-tracking. We formalize a geometric core: chunk embeddings live in a finite-dimensional Hilbert space, context is summarized by a normalized direction (a "narrative ray"), and selection is biased toward alignment with that ray. We then provide a conceptual equivalence to generalized Bayesian retrieval: the tilted selection rule is exactly a Gibbs-posterior (generalized-Bayes) update, yielding a generalized Bayesian RAG view in which the "query" is the evolving ray direction. The analysis also pinpoints where the Bayesian analogy breaks: projection dominance (one-dimensional folding), posterior collapse to a point representative (mean-field or single-sample propagation), and endogenous query dynamics via aggregation and normalization. Iterative self-consistency pressure yields contractive dynamics that suppress drift-like components while damping orthogonal alternatives below an operational legibility threshold. In this regime, frame switches cease to be routine deliberative options and become rare-event transitions (hitting-time/escape problems) from metastable basins. As an illustrative special case, we instantiate the template in a mode-dependent three-state agent (wake/deep/REM): when external anchoring is largely suppressed but internal iteration continues, dream-like dynamics arise as transient apophenic stabilization of recombined stored content rather than as reliable new factual evidence. We separate internal and external viewpoints using information filtrations: when observations are policy-dependent and independent anchors are inaccessible, an agent need not be able to falsify its own active frame from within, while an outside observer with a richer filtration can detect persistent channel discrepancies via independent records. A central implication is exponential leverage. In metastable settings, exit hazards and exit probabilities depend exponentially on effective barrier-to-noise ratios (Kramers/large-deviation scaling), while calendar-time survival is governed by the integrated hazard time. We distinguish the typically hard-to-forecast identity/timing of specific exit triggers from the more diagnosable onset of fragility (weakening restoring margins consistent with critical slowing down). We extend the framework from dyadic inside-outside asymmetries to multi-agent communication: when communication becomes endogenous evidence, shared anchors can stabilize corrigibility while compressing alternatives, whereas anchor mismatch and low overlap can be exponentially amplified into fragmentation via trust-flip coupling, mean-field tipping, and metastable lock-in. Finally, we discuss constructive interventions (anchoring injection, auditing, exploration, and legibility-floor reduction) alongside the dual-use risk of evidence-channel control, emphasizing that "cooling-down" can improve detectability and timely anchoring without necessarily amplifying diffusive exits, and that these regime parameters are safety-critical and require governance in high-stakes deployments. Supplementary Material File (cognitive_systems_bayesian_stochastic_template.pdf) - Download - 695.28 KB Information & Authors Information Version history Copyright This work is licensed under a Creative Commons Attribution 4.0 International License

Authors Metrics & Citations Metrics Article Usage 106views 61downloads Citations Download citation Thomas Richter. Reality Distortions in Finite Cognitive Systems: A Generalized Bayesian Stochastic Template for Minds, Machines, Markets, and Societies. Authorea. 04 February 2026. DOI: https://doi.org/10.22541/au.177023495.54959943/v1 DOI: https://doi.org/10.22541/au.177023495.54959943/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.