Where Models Break

Notes on marine systems, model limits, and false precision

This is a small collection of essays about a recurring problem in marine navigation: not bad data, but data that continues to look confident after the assumptions behind it have weakened.

Most modern marine tools rely on models — tides, weather, currents, routing, ETAs. These models are often excellent within the conditions they were designed for.

The trouble begins when we keep using them past those conditions, without being told that anything has changed.

The pieces here are not critiques of technology. They are attempts to describe where validity quietly decays, why that decay is confusing in practice, and how experienced operators learn to compensate for it.

These ideas are explored at length in Dead Reckoning.

What You’ll Find Here

  • Explanations of real offshore confusion — not abstract theory
  • Why certain numbers stop lining up even when nothing appears “wrong”
  • Where models remain useful, and where they should be treated cautiously
  • How interface design often hides uncertainty instead of revealing it

How These Essays Connect

The pieces in Where Models Break are meant to be read as a progression. Each one isolates a different place where confidence quietly outlives validity.

  1. Why Tide Predictions Degrade Offshore
    A concrete physical example of model decay in practice.
  2. When Marine Models Stop Being Valid
    Generalizes the problem beyond tides into a broader modeling pattern.
  3. Designing Honest Marine Interfaces
    Explains why these failures persist at the interface layer.
  4. Why ETAs Drift Offshore
    A common operational failure caused by hidden assumptions.
  5. Why Experienced Sailors Trust Trends More Than Numbers
    How operators adapt when tools don’t expose uncertainty.

How to Read This

These pieces are meant to be read slowly. They are not reference material or how-to guides.

If you’ve ever felt that the numbers looked fine but the situation didn’t, you’re the intended reader.

Nothing here argues against using models. It argues for understanding where they stop being trustworthy — and why our tools rarely say so.