GTESI—the General Theory of Evolutionary Systems & Information—is a practical framework for understanding which technologies, policies, and systems persist—and why.

Built from first principles in thermodynamics and information theory, GTESI unifies insights from Shannon, Feynman, Einstein, and Ricardo to measure how systems evolve by exporting entropy and maintaining structure under pressure. It complements traditional tools like ROI, IRR, SWOT, and TEA by flagging hidden failure points and unseen strengths—especially in complex or emerging markets where conventional analysis runs out of signal.

The GTESI method analyzes systems through its unique metrics such as IPR, SCD, TRFI, and EED, drawing attention to where collapse or breakthrough is likely and what interventions can recompress the narrative to recover persistence.

GTESI doesn’t replace spreadsheets—it explains why the math sometimes fails, when a system’s structure, compression, rituals, or entropy balance break down. That’s when GTESI excels—offering clarity where other models go quiet.

In fields from finance to the bioeconomy to pandemic response, GTESI has already revealed predictive patterns:

• Spotting fragility behind inflated COVID preparedness narratives in 2019.
• Explaining why renewable diesel thrived—and why ethanol-to-jet still stumbles.
• Signaling hydrogen’s fragmentation risk vs. its symbolic power.
• Offering early warning on asset bubbles by tracking symbolic coherence and adaptive strain.

What do its concepts and acronyms mean?

IPR: Inverse Persistence Ratio: “Value without memory.”

IPR measures the gap between valuation persistence (e.g., market cap, investor enthusiasm) and operational memory (e.g., track record, cash flow, physical plant, ecosystem stability). A high IPR means price is sticking around — but the foundation is eroding. It’s like watching smoke in the sky when no-one is yelling “fire!”. Warning sign of symbolic inflation, over-valuation, or collapse risk.

SCD: Symbolic Compression Divergence: “When your story breaks from your system.”

SCD tracks the misalignment between public narrative (press releases, investor calls, strategic decks) and internal motion (technical progress, team stability, delivery timelines). A high SCD means the symbolic layer is leaking entropy — the story is losing coherence. Early indicator of reputational fragility, trust erosion, or memetic drift.

TRFI: Trust Ritual Failure Index: “Rituals keep systems sane.”

TRFI monitors the health of symbolic trust rituals: SEC filings, earnings calls, guidance cycles, leadership continuity, board signaling. A rising TRFI signals missed filings, ambiguous metrics, unexplained personnel shifts — cracks in the ceremonial foundation. When ritual breaks down, systems lose legitimacy — with investors, partners, regulators.

EED: Entropy Export Deficit: “Adaptation stalls, pressure builds.”

EED scores how well a system is exporting entropy — through innovation, expansion, alliances, new markets. A high EED means the system is hoarding entropy instead of offloading it — a pressure cooker instead of a pressure valve. Strong predictor of layoffs, retrenchment, sudden pivots, or collapse.

Core GTESI Concepts

How GTESI Works: A Diagnostic Tool

GTESI evaluates the motion-memory balance in a system. GTESI doesn’t replace financial models — it explains why they fail when they do. A healthy system shows

• Entropy exported (not bottled)
• Symbols that match reality
• Rituals that maintain trust
• Compression that enables repetition (manufacturing, contracts, team function)

A brittle system shows:

• IPR: Inverse Persistence Ratio — symbols outlasting performance
• SCD: Symbolic Compression Divergence — narrative drift
• TRFI: Trust Ritual Failure Index — cadence breakdowns, filings missed, metrics blurred
• EED: Entropy Export Deficit — innovation that fails to scale or simplify

GTESI Background and Provenance

GTESI (General Theory of Evolutionary Systems and Information) is a systems-level diagnostic framework rooted in thermodynamics, information theory, economics, and physics. It explains why some systems persist, evolve, or collapse—and how symbolic structure interacts with motion and entropy. It’s grounded in foundational work by:

• Claude Shannon (information compression)
• David Ricardo (comparative advantage and exchange)
• Albert Einstein (energy and curvature)
• Richard Feynman (entropy and systems modeling)

The full model is detailed in the forthcoming book Everything in Motion. But you don’t need to be a physicist to use it. You just need to ask: Why is this thing still standing? Or why is it about to fall?

GTESI will help you see the answer before others do.

What GTESI does not do

GTESI does not coerce. It invites clarity. It invites systems (and people) to act in accordance with the entropy they feel. To seek persistence not through rigidity, but coherence. It aims to provide conceptual rigor and practical fluency for decision-makers who have seen GTESI in action. The framework looks forwards as well as backwards: there are symbolic stress watchlists, sector-wide trend forecasts, or pre-collapse patterns alerts. GTESI is not hostile analysis. It’s adaptive pattern recognition. It doesn’t scold leaders — it explains why some leaders feel stuck. It doesn’t shame organizations — it gives them a mirror for earlier, cleaner course correction. This is about systemic narratives, not failures, for readers who have come to believe that there is merit in considering thermodynamic persistence is a unifying principle for systems in motion.

At first, there’s the “I don’t get it” moment, followed by the “Sounds fancy. Who’s using it?” challenge. That’s understandable.

GTESI is dense, cross-disciplinary, and emergent. It is not backed by a “school,” movement, or institution. There’s no standard onboarding, no fixed authorship tradition. New ideas feel suspicious. So, this GTESI framework must create real impact in practice.