The Ambiguity-Triggered Latent-State Externalization Theory (ATLET): A Proposed General Systems Framework for Latent-State Activation, Endogenous Response Dynamics, and Unintended Externalization

This paper proposes the Ambiguity-Triggered Latent-State Externalization Theory (ATLET), a candidate framework for studying how a previously concealed internal state may become externally observable following an ambiguous external interaction, even when the interaction partner did not deliberately seek disclosure. The primary motivating case concerns an agent who performs an action without another person's consent or knowledge, subsequently encounters behavior whose meaning or intention is uncertain, and then discloses information about the action without having planned the disclosure in advance.The framework separates the initiating event from the later externalization process. It does not treat the original consent-related violation as proof of the proposed mechanism, nor does it assume that ambiguity necessarily produces disclosure, that disclosure verifies the truth of the disclosed content, or that every concealed state must eventually become observable. Instead, it represents the candidate process through latent-state persistence, ambiguity perturbation, internal interpretation, endogenous response activation, control-expression interaction, observable output, and subsequent observer inference.A general system representation is introduced for a network of N interacting components. A discrete-time state-space model, a normalized entropy measure for finite alternative interpretations, a logistic event model, and an output-based externalization rule are specified. A twelve-layer research architecture, ten methodological domains, operational definitions, seven testable hypotheses, competing explanations, a thirty-case study architecture, an ethical study design, a synthetic simulation, and a fourteen-step manuscript-development protocol are then presented.The framework is intentionally conditional. Its proposed contribution can be established only if the model yields reproducible, discriminating predictions, improves upon plausible alternative explanations, and demonstrates domain-appropriate measurement and causal identification. No claim of universal validity, absolute mathematical completeness, or historical originality is made in the absence of those tests.