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On Modeling ECDO Theory

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The value of a model lies less in its predictions than in its capacity for differential revision.
End states are largely accidental. Dynamics are exceedingly difficult to get right.

Box’s Axiom: “All models are wrong, but some are useful.”

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Since the release of the first of three Exothermic Core-Mantle Decoupling — Dzhanibekov Oscillation (ECDO) Hypothesis articles on 16 February 2020, a number of modelers have stepped forward to offer their own perspectives on how Earth’s geomechanics might fit into an ECDO framework. To date, six individuals or groups have undertaken the task of developing simulations of varying complexity to test the fit between Earth’s geophysical and geohistorical record and an ECDO paradigm. This is a fantastic evolutionary process and I am very appreciative and supportive of all these efforts to date.

The purpose of this article is not to dictate how such efforts should proceed, but rather to offer some guidelines.

My own experience bears the patina of five decades spent developing, applying, and testing models in the real world. I am admittedly less versed in the vast array of modern simulation packages and AI-assisted tools that have become available in recent years. Development of the research is far more important to me at this juncture, than the task of learning a new modeling/simulation package or API.

Nonetheless, a few fundamentals of modeling do not change, no matter how sophisticated the tools become. A scratch golfer remains a scratch golfer whether playing with an old set of Ben Hogan irons or the latest Cobra 3DP X clubs. The inexperienced player, by contrast, often speaks at length about equipment and metrics as a substitute for skill. This can become a distraction.

The same thing occurs in modeling.

Sophisticated tools frequently conceal overly simple assumptions, and may well expertly communicate an entirely invalid or incoherent outcome.

Most ECDO models I have seen fail not because of inadequate software, but because they attempt to force a highly complex geophysical system into a framework which is too simple to reproduce the observational set.

Before continuing to mature ECDO modeling itself, therefore, it is worthwhile to establish a few principles regarding models in general.

On Modeling and Simulation

Be cautious – understanding begins with courtship, not conquest. When the unknowns constitute the greater set, the premature application of known measurements, mathematical solutions, or model/simulations may narrow our thinking more than uncertainty may warrant, leaving us in a state of highly precise ignorance.

Applying rigorous math too early in the prosecution of a daunting challenge, is not a first principle. Math usually comes near the end. Early on, it can usually only offer an illusion of confidence.

“I have an idea — let’s throw some Cox proportional hazards analysis at it.” ~ Often the first indication that statistical technique is being substituted for investigative prowess and patience.

Remember, the first requirement is to be an investigator or scientist, not a lab tech. We have lab techs out the wazoo… they rarely find anything salient, but generate a LOT of reports.

The first concern I usually emphasize to young engineers developing tracking, management, or forecasting systems is what I call Observer Reflexive Distortion Effects:

Observer Reflexive Distortion Effects – When an observer engages a system with heightened attention, whether through direct observation, measurement, or test-perturbation – the observer’s cognitive frame undergoes distortion proportional to the novelty, salience, or paradigm conflict encountered. This distortion can alter both the observer’s interpretation of the system, and the system’s behavior in response to being observed.

  1. Temporal distortion of risk – slow-moving or gradual changes appear more dangerous than fast ones because they increase causal ambiguity and reduce signal-to-noise, heightening the observer’s sense of uncertainty.
  2. Observation perturbation – a system under observation will exhibit changes—behavioral, statistical, or structural—simply because it is being observed or measured (Hawthorne effect, for example).
  3. Novelty escalation – early or first-time observations cause the observer to overestimate exceptionality, instability, or risk.
  4. Focus anxiety – persistent or obsessive observation inflates perceived threat, variance, or urgency within the system.
  5. Selective exclusion – when observations contradict the observer’s governing paradigm, they are filtered, minimized, or ignored—not because they are weak, but because they destabilize the observer’s cognitive model.

At its core, this is a warning against overfitting or overinterpreting early model behavior. I’ve fallen into that trap myself more than once — white-knuckling through the first results of a client simulation or the first hours of operating a steam plant, where every fluctuation seems to demand an explanation. Experience teaches that early system behavior often reflects the observer’s expectations or fears as much as the system itself.

As well, my experience running thousands of (old school) models applied to resolve ACAN problems in the real world is outlined below (with corresponding reference to Stephen Wolfram, A New Kind of Science (Champaign, IL: Wolfram Media, Inc., 2002).1

The mental and semantic discipline of the philosophically grounded modeler—and even Stephen Wolfram occasionally stumbles here, useful though his work remains—is to constantly distinguish between simplicity and straightforwardness, and between complexity and complicatedness.

Just as there exists a critical distinction between inductive and deductive evidence, or between variables and constraints, so too is this discipline essential to the sound modeling mind.

Never allow your modeling tools to conceal this circumstance from you or your audience.

This serves to introduce what I call, The Black Box Paradox.

TES’s Black Box Paradox

black wood rectangular box carved

If you don’t understand why a model produces its answer, should you trust it?

And if you do understand why it produces its answer, why did you need the model in the first place?

The above, of course, is not a mathematical paradox, but a philosophical one. Nor does this tension serve to invalidate efforts to create models and simulations. Rather, its purpose is to compel the modeler to confront two questions:

What has actually driven the answer I have obtained?

And,

Am I using a sophisticated tool merely to repackage and conceal a simple a priori assumption?

These are the questions every model developer must keep firmly in mind while applying their tools.

Corollary I — The Convergence Corollary

Models generally produce one of three outcomes.

Multiple Optimal Outcomes (Wolfram’s “Equifinality”)

A model yields several competing solutions or equilibria.

This indicates either that the problem remains underdetermined, or that assumptions require further discrimination—or simply that a business or judgment call must be made.

Incoherence (Wolfram’s “Principle of Computational Equivalence”)

The model fails to reconcile and/or produces contradictory results.

Such failure may indicate ignorance, incompatible assumptions, changing conditions, or an apples-and-oranges comparison.

The unappreciated irony, however, resides in this: The incoherence itself is often informative.

A Single Optimal Outcome (Wolfram’s “Simple Rules Can Produce Immense Complexity”)

The model converges upon one answer.

Such convergence may indeed represent a true optimum.

Or it may merely reflect unsound assumptions, hidden biases, or sensitivity blindness.

Paradoxically, the most suspect outcome is often the appearance of a single optimal solution.

Corollary II — The Failure Corollary (Wolfram’s “Failure Is Informative”)

Because all models are wrong (see Box’s Axiom), failure does not imply lack of utility.

Indeed:

The catastrophic failure of a model may be more informative than the model itself.

Corollary III — The Adaptation Corollary (Wolfram’s “Principle of Computational Equivalence”)

A model’s utility is measured less by its end-state accuracy than by its ability to survive contact with contradictory observations and constraints.

Or:

The value of a model lies less in its predictions than in its capacity for differential revision.

Conformance to reality is found primarily in a model’s dynamics—not in any particular end state.

End states are largely accidental. Dynamics are exceedingly difficult to get right.

Corollary IV — The Complexity Corollary (Wolfram’s “Irreducibility”)

If you use too few variables to characterize a phenomenon, you have little hope of capturing its full complexity.

If you use too many, you can forecast virtually any future you desire.

Toward an ECDO Modeling Framework (Wolfram’s “Classification Before Mathematics”)

Reality does not reward the most sophisticated model. It rewards the model which disciplines its priors and gets the dynamics right. The equations are merely the notation.

The purpose of this step in the systems analysis process is to move the discussion away from

“Here is my model.”

and toward

“Here is the language within which competing models may be expressed, leveraged, and compared.”

That is what systems groups do before attempting to develop model fabric.

This is the proper role of dynamic modeling—and at present there are at least six highly motivated groups pursuing that effort. I am impressed with each and every effort.

My own role is not to be the modeler. It is to provide coherence to the effort. As managing partner in a model-development practice, and president of a large systems integration corporation, this has been my job for some time.

After defining the ECDO problem (Cunningham, Inversion, “Define the Problem—Then Seek Falsification,” ch. 3, p. 15.),2 a Wittgensteinian system description should precede any attempt to formulate constraints or differential equations.

Before constraining equations come logical or variable objects – never provide an answer to an undefined question.

Before modes come solutions; before solutions come states – never fine tune a wrong answer, never attempt to answer a question which does not bear coherence.

Before simulations come coupling coefficients and dynamic variables – always understand the chess piece rules before attempting to play chess.

These things must first be identified and assigned coherent meanings. Otherwise, one is left with a collection of ECDO Inertial Interchange True Polar Wander (ECDO-IITPW) models whose variables, assumptions, and state definitions differ in ways that preclude meaningful comparison.

Accordingly, the following two panels (below) constitute a proposed system description for ECDO States 1 and 2 and their transitions. They are not intended to be equations. Nor are they intended to represent a complete model themselves.

Rather, they are meant to define the principal entities, coupling factors, state variables, and dynamic parameters which should be considered before any mathematical treatment or simulation is attempted.

The objective is simple:

To establish a common language within which competing ECDO models may be constructed, tested, falsified, and compared.

Thoughts are appreciated.

Modeling Factors for ECDO Exothermic Core-Mantle Decoupling (Indigo and Tau)

What permits the ECDO mediated Dzhanibekov rotation event?

Panel 1 – Modeling Factors for ECDO Exothermic Core-Mantle Decoupling (Indigo and Tau)
What permits the ECDO mediated Dzhanibekov rotation event?

Modeling Factors for ECDO States 1 and 2 – Along with Their Transitions (Post-Tau)

Given permission, what are the ECDO system rotation dynamics?

Panel 2 – Modeling Factors for ECDO States 1 and 2 – Along with Their Transitions (Post-Tau)
Given permission, what are the ECDO system rotation dynamics? 3

Simulation Success Touchpoints (Lakatos Touchpoints)

End states are largely accidental. Dynamics are exceedingly difficult to get right.

Hungarian-born philosopher of mathematics and science, Imre Lakatos, argued that research programmes should be judged not by a single prediction but by their ability to explain an expanding set of observations without resorting to ad hoc fixes. Forty geohistorical touchpoints are offered as benchmarks against which competing ECDO models may be evaluated.4

The underlying premise is simple: the greater the number of touchpoints a model successfully reproduces — without ad hoc tweaks — the more confidence one may place in its validity within an ECDO context. This does not render the model synonymous with truth. Rather, it identifies those models whose dynamics happen to emulate the complex dynamics of Earth with greater fidelity.

No single observation proves a model. Credibility emerges instead through the coherent reproduction of an increasingly broad set of independent geophysical and geohistorical observations.

Accordingly, competing ECDO models should be judged not by isolated predictions, but by their ability to progressively account for an expanding set of independent geophysical and geohistorical touchpoints without resorting to ad hoc manipulations or special pleading.

Below are my suggested touch points for model evaluation: (Formation Name – Expected ECDO Observation – (Comment) – Latitude Longitude)

Anchoring Touchpoints (Vital – State 1 to State 2 Primary Rotation)

  1. Thar Desert – ECDO-striated saline/diatomaceous flats – 26°20’59.54″N 71°54’36.67″E
  2. Taklamakan Desert – ECDO-striated saline/diatomaceous flats – 39°05’07.05″N 83°01’16.75″E
  3. Gobi Desert – ECDO-striated saline/diatomaceous flats – 40°51’32.60″N 102°53’15.79″E
  4. Great Victoria Desert – ECDO-striated saline/diatomaceous flats – 25°01’29.68″S 126°55’45.24″E
  5. Russian Black Soils Displacement Range – Broad-faced displacement along ECDO oceanic displacement vector, with coriolis taper and distinct termination line – 58°13’42.41″N 62°27’59.30″E
  6. North American Black Soils Displacement Range – Broad-faced displacement along ECDO oceanic displacement vector, with coriolis taper and distinct termination line – 54°24’35.89″N 120°25’00.69″W
  7. Great Basin Desert (US) – ECDO-striated saline/diatomaceous flats – 37°16’40.60″N 110°41’54.45″W
  8. Vizcaíno Desert (Baja) – ECDO-striated saline/diatomaceous flats – 27°29’21.18″N 113°36’50.98″W
  9. Patagonian Desert – ECDO-striated saline/diatomaceous flats – 43°46’33.68″S 68°16’04.43″W
  10. Salar de Uyuni Salt Flats (Bolivia) – ECDO-striated saline/diatomaceous flats – 20°11’20.17″S 67°34’40.43″W
  11. Bonneville Basin/Salt Lake (Utah) – ECDO-striated saline/diatomaceous flats – 40°32’36.81″N 113°33’26.64″W
  12. Cuban Underwater Formation – Emergence during ECDO State 2 – (Emergence) – 22°07’54.86″N 85°07’41.13″W
  13. Chihuahuan Desert – ECDO-striated saline/diatomaceous flats – 23°37’24.31″N 102°13’55.00″W
  14. Emi Koussi Pass (Sahara Desert) – ECDO-striated saline/diatomaceous flats – 18°16’46.31″N 20°13’02.82″E
  15. Giza Plateau Egypt – Submersion of ~576 ft during State 2 – 29°58’38.86″N 31°08’04.68″E
  16. Sardinia Island – Preservation during initial ECDO State 1 to State 2 rotation – 40°01’21.47″N 9°06’35.35″E
  17. Grand Erg Oriental (Sahara Desert) – ECDO-striated saline/diatomaceous flats – 32°03’41.22″N 6°44’27.64″E
  18. Mauritanian Slide Formation – Exit tail for coriolis-affected oceanic inundation for State 1 to State 2 rotation – 17°54’21.96″N 17°04’07.81″W
  19. Cape Verde Islands – Land emerges during State 2 – (Emergence/Atlantis) 15°11’44.52″N 23°42’09.49″W
  20. Bahama Bank – Land emerges during State 2 – (Emergence) – 24°17’54.59″N 78°11’05.62″W
  21. Galapagos Land Bridge – Land emerges during State 2 – (Emergence) – 1°05’27.42″S 85°55’32.83″W
  22. Easter Island Land Bridge – Land emerges during State 2 – (Emergence) – 25°56’36.91″S 100°34’19.77″W
  23. East Euler Axis Survival Point – Brief or no inundation during either ECDO rotation – 0°00’00.00″S 120°55’35.14″E
  24. West Euler Axis Survival Point – Brief or no inundation during either ECDO rotation – 0°00’00.00″S 59°47’30.39″W
  25. Caspian Turan Depression – ECDO-striated saline/diatomaceous flats – 44°34’04.25″N 59°51’58.29″E
  26. Zealandia – Land emerges during State 2 – (Emergence) – 49°19’25.31″S 170°05’46.64″E
  27. Lemuria – Land emerges during State 2 – (Emergence) – 10°44’56.00″S 61°09’10.24″E
  28. Faiyum Basin (Egypt) – Submerged 225 ft 500 years after rotation from State 2 back to State 1 – 29°22’05.52″N 30°49’13.34″E
  29. Erg Amatlich Desert Striations Mauritania – ECDO-striated saline/diatomaceous flats – 18°55’29.92″N 13°50’00.45″W
  30. Mu Civilization – Land emerges during State 2 – (Emergence) – 11°20’14.58″S 148°48’25.87″W

Conditional/Transitional/Return from State 2 to State 1 Touchpoints (Secondary)

These sites may be the result of secondary/oscillatory rotation effects, may involve a type of transition effect, or may have been created from the rotation from State 2 back to State 1 – they are conditional and should be evaluated for predictive strength only after the primary anchoring touchpoints have been satisfied by the model.

  1. Lake Tuz Salt Flats Anatolia – ECDO-striated saline/diatomaceous flats – 38°41’15.33″N 33°26’59.04″E
  2. Kalahari Desert – ECDO-striated saline/diatomaceous flats – (Return to State 1 potential) – 26°47’03.63″S 19°43’02.41″E
  3. Rub’ al Khali/Arabian Desert – ECDO-striated saline/diatomaceous flats – (Return to State 1 potential) – 21°07’08.80″N 51°25’21.82″E
  4. Syrian Desert – ECDO-striated saline/diatomaceous flats – (Return to State 1 potential) – 32°22’52.39″N 40°42’05.01″E
  5. Ogaden Desert – ECDO-striated saline/diatomaceous flats – 8°21’31.88″N 48°08’42.56″E
  6. Doggerland – Emergence during ECDO State 2 – (Emergence) – 54°41’30.89″N 3°32’10.07″E
  7. Eastern Antarctica Dome C – Milder climate latitude during State 2, maintains elevation – 71°55’34.56″S 63°41’21.62″E
  8. Western Antarctica – Tropical climate latitude during State 2, submersion – 74°59’59.32″S 73°36’13.84″W
  9. Azores Bank – Land emerges during State 2 – (Emergence) – 38°00’47.99″N 29°09’33.71″W
  10. Canary Islands – Land emerges during State 2 – (Emergence) – 28°03’37.00″N 14°40’41.55″W

To all our highly-motivated, intelligent, and creative modelers: “Well done, and I look forward to continuing to work with each of you.”

The Ethical Skeptic, “On Modeling ECDO Theory”; The Ethical Skeptic, WordPress, 20 Jun 2026; Web, https://theethicalskeptic.com/?p=117051