How it works

HTMA Measure identifies the quantity, gathers evidence, estimates component ranges, and compares the result with a decision threshold when one exists. A missing threshold can prevent an action recommendation without preventing a useful estimate.

1. Make the question precise

The agent identifies:

• the decision;
• the quantity of interest;
• the unit;
• the time horizon;
• the decision threshold; and
• the cost of being wrong.

If a required identifier, period, jurisdiction, current source, or private fact is missing, the workflow stops and names the blocker. A missing threshold alone does not stop an otherwise responsible range; it leaves the action comparison unavailable.

2. Choose the estimate mode

The same noun can refer to different quantities. “Cost” might mean:

• a vendor’s public market price;
• a budget allowance;
• the amount likely paid;
• an official fee;
• a statutory rate; or
• an ambiguous target that needs clarification.

The mode determines which evidence and adjustments are legitimate.

3. Build and rank evidence

The agent starts with available local context, then uses direct sources, reference classes, and comparable cases. The agent assesses each source for quality, freshness, and relevance to the target.

Confirmed facts stay separate from assumptions and inference.

4. Decompose and calibrate

The agent breaks the quantity into components with low, central, and high values. It widens bounds when evidence is weak.

Monte Carlo simulation is optional and only follows credible, calibrated inputs.

5. Connect the range to the decision

When a threshold exists, the agent compares the final interval with it and explains what action follows. Without one, the threshold stays null and the memo makes no approve/reject claim.

6. Measure only what matters next

The value-of-information section ranks remaining uncertainties by their ability to change the decision. It names the next useful measurement and the point at which further research stops mattering.

7. Return prose and structured data

The result is a readable memo plus an HTMA_RESULT JSON appendix for scoring, automation, or later calibration review.