GENESIS Transformation Framework v1.1 — A Universal System Language for Psychology, Burnout, Organizational Dynamics, and AI-Augmented Work

DescriptionBackground and OriginThe GENESIS Transformation Framework (GTF) v1. 1 transfers the mathematical core of the GENESIS biomedical modeling series (R20–R23, Fuerste 2025/26) — a bistable dynamical systems model originally developed for ME/CFS and Long COVID — to psychological disorders, organizational dynamics, and AI-augmented work environments. The underlying mathematics is the Cusp Catastrophe, described by three meta-parameters: α (feedback gain / cognitive reactivity), ρ (resilience resources), and Δ (erosion / allostatic load). These parameters, their regime structure, and their intervention logic are shown to map with structural — not merely metaphorical — precision onto established constructs in clinical psychology, occupational science, and organizational theory. Core ContributionsThe framework advances four interconnected theoretical contributions: Bistable System Language for Psychology and Organizations. Three topological regimes — monostable healthy, bistable vulnerable, and bistable trapped — correspond directly to clinical and organizational states. Chronification, whether as depression, burnout, or toxic organizational culture, is reframed as a topological problem rather than a content, personality, or management problem. This reframing transforms intervention logic: therapy failure in Regime 3 is a topological failure, not a patient failure. Orthogonality Rule and Intervention Sequencing. The sequencing protocol α↓ → ρ↑ → Δ↓ follows from the mathematical dependency structure of the parameters. The orthogonality rule — that interventions targeting only one parameter axis structurally fail in Regime 2 and Regime 3 — explains a large class of empirically documented intervention failures in burnout treatment and organizational change management that existing frameworks leave unaccounted for. Digitalization as β-Layer Amplifier. The central theoretical hypothesis of v1. 1: digitalization does not act as a direct operator on any single parameter, but increases β — the amplification constant of all existing feedback loops. Formally expressed as αₑff = αbase · (1 + β·D) · vdigital, this hypothesis explains why the same technology produces radically different outcomes in different organizational states, why AI-augmented work accelerates regime transitions, and why standard change management sequences — digitalize first, optimize later — are structurally contraindicated in Regime 2 and 3 systems. The Log-In Effect describes the resulting bistable trap under digitalization pressure as a three-phase dynamic with an intervention window that closes faster than classical burnout trajectories. AI-Forward Compiler. A process-level application of the GENESIS sequencing logic to organizational AI integration: every process is diagnosed by its parameter profile (αP, ρP, ΔP) before AI augmentation is initiated. Regime 3 processes — high error amplification, low resilience, high technical debt — are contraindicated for AI integration without prior process reconstruction. The Compiler provides a formal methodology for sustainable AI integration that addresses the most common failure mode: applying AI augmentation to already-dysfunctional processes, thereby accelerating collapse rather than enabling recovery. Methodology: Multi-AI Peer SynthesisThis document was developed through the AI2AIR. Vibe. Lab Tiny Team methodology — a structured multi-AI collaborative research process integrating six independent review sessions from Claude (Anthropic), ChatGPT (OpenAI), Gemini (Google), Grok (xAI), LeChat (Mistral), and Perplexity, with the human researcher as governance layer, integrator, and falsification function. A subsequent seven-reviewer meta-analytical cycle — including an external assessment by DeepSeek — evaluated the framework against current literature in dynamical systems psychiatry, occupational psychology, organizational theory, and AI cognitive load research. The review cycle produced a stable convergence-divergence pattern consistent with the framework’s own epistemological predictions: convergence on the β-amplifier hypothesis, orthogonality rule, and AI-Forward Compiler as robust contributions; divergence on boundary conditions including empirical calibration gaps, stochastic underspecification, equity and global disparity dimensions, and the phenomenological limits of mathematical modeling. Divergences are reported transparently and inform the research agenda below. The Tiny Team methodology itself — AI-assisted epistemic collaboration with explicit role assignments, Clean Chat protocols, and Human-in-the-Loop governance — is documented as a secondary contribution and proposed as an independent subject for methodological investigation. LimitationsThe GTF v1. 1 is a conceptual transfer framework, not a clinical manual or diagnostic instrument. The three meta-parameters are quantitatively calibrated for biological systems (GENESIS R21–R23 series) but remain conceptually defined without quantitative calibration for psychological and organizational transfer domains. Critical parameter values — ecrit, Dfunc, Δe* (D) — have not been established for depression, burnout, or organizational dynamics. The sequencing logic and operator catalogue are formal reasoning frameworks, not precision prescriptions. Additional boundary conditions are documented in Chapter 11, including scale homogeneity limits, time-scale inequivalence across domains, stochastic overlay, and the phenomenological dimension not captured by bistability mathematics. Research AgendaThe GTF v1. 1 constitutes the opening document of an empirical research program. Three priority directions are identified on the basis of the multi-reviewer synthesis: The primary requirement is longitudinal ESM (Experience Sampling Method) studies with validated psychological instruments to estimate α, ρ, and Δ proxies in clinical populations, enabling quantitative calibration of the parameter space for psychological transfer domains. Collaboration with established CSD research groups — in particular the Wichers and van de Leemput groups — represents the natural path toward this calibration. The second priority is an equity and global disparity extension addressing the differential impact of Regime 3 dynamics under digitalization pressure across gender, age, and global economic context — a dimension identified as absent in the current version and necessary for the framework’s applicability in international organizational contexts. The third priority is a dedicated methodological paper on AI-assisted epistemic collaboration as a research paradigm, documenting the Tiny Team methodology, its governance architecture, its epistemological properties, and its limitations as an alternative to conventional peer review.