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The physics layer makes claims about the business: a forecast claims what a measure will do next, a declared lag claims how long a cause takes to show its effect, and a driver edge claims that one measure moves another. Some of those claims are accounting identities, such as gross_profit = revenue - cogs, and are exact by definition. Most are empirical hypotheses whose parameters nobody knows in advance. The calibration layer owns those parameters. The physics layer declares a relationship, calibration estimates it, and every new observation or intervention revises the estimate. The control layer then makes its next decision on the updated physics.

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Why calibration is its own layer

Any application can fit a model. What a separate layer adds is that the fitting, scoring, and retiring of estimates happens once, against the shared World Model, instead of inside each application that needs a forecast or a driver coefficient. Because the physics states each relationship explicitly (which measure, which drivers, at which grain), calibration has a common interface for three recurring jobs:
  • Comparing predictions with observations for every component, using the same semantic definitions the rest of the platform queries.
  • Monitoring drift, so that a relationship that held last quarter but no longer does is caught before a control acts on it.
  • Updating the model that subsequent decisions use, so that forecasts, scenarios, anomaly detection, and controls all read the same current estimate rather than each keeping a private copy.
Keeping this separate from both the physics and the controls means a model assumption, a policy threshold, and a retraining rule are never tangled together in one application-specific function.

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What gets calibrated

  • Forecasts. Seasonal models refit continuously as new history arrives, and each forecast carries an envelope that states its own uncertainty.
  • Driver coefficients. Where the physics declares that a measure depends on its drivers, such as sales on ad spend, price, and availability, calibration holds the estimated strength of each dependency and revises it incrementally as evidence accumulates. The estimator can be any online procedure suited to the relationship, from recursive least squares to stochastic gradient updates to Bayesian filtering.
  • Lags and structural claims. A declared lead time or a claimed causal direction is checked against the realized relationship as data accumulates.
  • Functional form, where warranted. When a component’s error stays high however its parameters are tuned, the relationship itself is likely wrong, and calibration flags it for a human to revise rather than refitting it forever.

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Prediction error is the signal

Every claim the physics layer makes is eventually scored against what actually happened, and calibration tracks that prediction error for each component. A forecast that keeps landing outside its envelope, or an estimate whose predictions drift away from outcomes, is flagged for a refit or for human revision, while components with healthy error keep their standing. This is what keeps the exact, estimated, and refused labels truthful, since an estimate is only worth its label if something is checking it.

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Learning is not causal proof

Online learning is not causal inference. A coefficient fitted from history absorbs everything that moved alongside its driver: seasonality, a price change, a selection effect, or the feedback of the business’s own past decisions. Calibration therefore keeps track of where each estimate came from:
  • An observational estimate, fitted from passive history, stays labeled as weak. It can inform a forecast, but it is retired rather than trusted once its predictions drift.
  • An interventional estimate, measured by evaluation from the actions of a control, identifies the driver’s effect from deliberate changes and supersedes any observational fit on the same edge.
The architectural claim is that explicitly declared drivers and interventions make calibration and evaluation reusable across every application, not that a declared graph establishes causality on its own.

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Evaluation feeds calibration

Evaluation is the richest source of calibration signal. When a control change’s measured impact disagrees with what the model implied, the gap points at a specific physical claim, whether a lead time, a demand response, or a claimed causal link. Because interventions probe the physics in a way that passive observation cannot, every evaluated change leaves the model a little sharper than it was.

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Next steps

Evaluation

Measure what controls do, with results feeding back here

Metric Tree

The driver edges calibration estimates and keeps honest

Controls

The decision rules that run on calibrated physics

World Model

The three layers and how they fit together