The Structural Diagnostic Toolset (TSDT)

Read full text
Authors: Dean Tyldesley
Cited by: 0

Abstract

The Structural Diagnostic Toolset (TSDT) is a deterministic, domain-agnostic structural classification engine designed to evaluate whether a constrained system can persist under cumulative irreversible load and finite resolution capacity. It operates through a unified stress functional that integrates divergence, monotonic irreversibility, and bounded capacity, and classifies systems into stable, saturated, collapsed, or identity-violating states. TSDT does not predict outcomes, optimize decisions, or evaluate morality; it strictly determines structural feasibility and persistence boundaries under defined invariants. It is intended for system architects, AI safety researchers, governance bodies, policy designers, and risk analysts who need to determine whether a configuration remains structurally viable before forecasting, optimisation, or strategic planning. TSDT is particularly useful in environments where cumulative constraints, informational loss, and finite capacity create hidden instability, allowing analysts to detect agency saturation, coherence breakdown, or invariant violation prior to collapse. —————————————————————————— The Relational Cognitive Navigator (RCN) adapts to resolve conflicts with reality but cannot detect its own breaking point, making the Structural Diagnostic Toolset (TSDT) a necessary stabilizer. Without the TSDT to monitor Resolution Capacity, the RCN would accumulate "Baggage" and "Conflict" until it reached a state of saturation, resulting in structural collapse or the loss of core identity. By enforcing the Coherence Formula, the TSDT acts as a structural governor that ensures the RCN’s adaptation never compromises the system's fundamental stability or "Invariants." ORCID: 0009-0006-6041-352XWeb of Science ResearcherID: OYD-9537-2025

Fields of Study & Subjects

Complex Systems and Dynamics Complex Systems and Decision Making Diverse Interdisciplinary Research Studies

Publication details

Journal / source
OSF Preprints (OSF Preprints)
Publication date
15 February 2026
Language
EN
Document type
Preprint