Induced Self-Disclosure Under Partial Constraint: A General Principle of Adversarial Systems

This paper formalizes a general structural principle governing adversarial and constrained systems. When partial constraint is applied to an agent’s uncertainty-reduction process without fully denying task feasibility, the agent compensates through expansionary behavior. This compensatory expansion increases the agent’s observable footprint, producing structured self-disclosure detectable by independent observers. Unlike denial-based frameworks, which suppress behavior entirely, partial constraint preserves task viability while increasing operational uncertainty. The resulting behavioral bloom converts resilience mechanisms such as redundancy, sampling, and persistence into exposure surfaces. The paper identifies boundary conditions under which the effect strengthens or collapses and distinguishes this regime from saturated systems in which dissipation is suppressed and failure manifests as catastrophic rupture. The framework is descriptive rather than prescriptive and is intended to support diagnosis, risk assessment, and failure prevention across domains including artificial intelligence, cyber systems, markets, ecological systems, and other adversarial environments.