Choose which uncertainty is worth measuring next
Focus research on information that could change a real business decision.
How this works
Research is valuable when its result can change a consequential choice. Start with the decision alternatives and their outcomes, then identify which uncertain assumptions actually affect the preferred option. A variable that is easy to measure may have little decision value if every plausible result leads to the same action.
Use a transparent decision model with ranges or probabilities supplied and reviewed by the decision owner. AI can organize assumptions and candidate studies; deterministic calculations should evaluate sensitivity and information value. Do not invent precise probabilities merely to produce a numerical ranking.
Before you start
Decision and deadline
Name the actual alternatives, decision owner, commitment deadline and outcomes that matter.
Comparable value scale
Use consistent units for costs and consequences, or explicitly keep multiple criteria separate.
Uncertainty estimates
Obtain defensible ranges or elicited probabilities and label their provenance and limitations.
Prepare the inputs
- alternatives
- Required
Feasible options with outcome logic
Include the current or do-nothing option where relevant.
- uncertain_parameters
- Required
Ranges or distributions with evidence and owner
Do not treat guesses as measured frequencies.
- decision_model
- Required
Transparent formulas or a documented scoring rule
Verify units, dependencies and what constitutes a preferred option.
- measurement_options
- Required
Study method, cost, delay and expected precision
A study provides imperfect information; perfect knowledge is only an upper-bound scenario.
Implementation, step by step
Model the actual choice
- Describe outcomes under each alternative using the agreed value scale and constraints. Compute the current preferred option with uncertainty visible.
- Check formulas and assumptions with the owner before prioritizing research; a faulty model produces a faulty research ranking.
Checkpoint: The baseline decision can be reproduced from stated assumptions.
Find decision-sensitive uncertainties
- Vary assumptions across plausible ranges and identify where the preferred option changes. Consider important interactions rather than only one variable at a time.
- Separate parameters that change the magnitude of outcomes from those that can change the choice itself.
Checkpoint: A sensitivity map identifies research questions with potential decision impact.
Estimate information value cautiously
- Where probabilities and values are defensible, compare expected value with current information to a perfect-information upper bound.
- For actual studies, account for imperfect accuracy, delay and implementation costs. Use qualitative ranking when numerical inputs are not credible.
Checkpoint: Each estimate is labeled by method and assumption quality.
Compare feasible measurements
- Describe the smallest practical study that could resolve the important uncertainty before the deadline. Identify what result would trigger each action.
- Compare cost and delay with plausible decision value; do not prioritize a study solely because it produces an impressive dataset.
Checkpoint: The ranked plan links each measurement to a contingent decision.
Hand off and update after evidence
- Deliver the priority, study design, assumptions and stopping rule. Keep the decision owner responsible for commitment and acceptable tradeoffs.
- When results arrive, update the model and record whether the decision changed. Preserve the original forecast to assess the usefulness of the measurement.
Checkpoint: The review can distinguish information gained from decisions actually improved.
Decisions along the way
All plausible values lead to the same option
Lower the measurement priority unless another documented objective justifies it.
Probabilities are unsupported
Use ranges and sensitivity analysis, and state that expected-value calculations are not established.
Research arrives after the commitment deadline
Treat its value for this decision as limited and consider a faster proxy or a reversible choice.
What you will produce
decision-measurement-plan.csv
CSVAn actionable research plan linked to choices.
Include: uncertainty · decision_switch · measurement · cost · delay · expected_precision · priority
decision-model-note.md
MarkdownReproducible reasoning summaries and calculation references.
Include: alternatives · assumptions · sensitivity · information_value · limits · owner_decisions
Check the result
- Verify consistent units and distinguish gross benefit from net benefit after study cost.
- Check that a claimed information value is nonnegative before costs and uses the same alternatives as the baseline.
- Confirm dependencies among uncertain inputs are not silently ignored.
- State what study result would change the action and whether it can arrive in time.
When something goes wrong
The decision model has no agreed objective
Clarify the tradeoff criteria and deliver a decision framing note before ranking studies.
The perfect-information bound is mistaken for a study forecast
Relabel it as an upper bound and model realistic study accuracy or refrain from a numeric benefit claim.
A key assumption changes during research
Version the model and reassess whether the original measurement still serves the decision.
A worked example
SYNTHETIC EXAMPLE · NOT A REPORTED BUSINESS RESULT
Request
Decide whether a study is worth buying for this choice.
Inputs
Synthetic payoffs in thousands: option A gives 100 in state H and 0 in state L; B gives 40 in either state. The owner assigns H and L probability 0.5 each. A proposed study costs 25.
Expected result
Current best expected payoff is 50 from A. Perfect information would give 0.5×100 + 0.5×40 = 70, an upper-bound gain of 20. Under these assumptions and this objective, a study costing 25 cannot be justified by decision information alone, even if perfect.
Run a small pilot
Start small
Apply the method to one near-term decision with a few uncertain assumptions.
Record the baseline
Record the original choice, confidence, proposed research cost and decision deadline.
Agree success criteria
Evaluate whether research priorities change and whether later evidence affects the decision as anticipated.
Stop or revise
Stop numerical ranking when values, probabilities or feasibility are unsupported.
Keep the scope clear
- The analysis informs a business decision; it does not approve expenditure or make investment commitments.
- Report model assumptions and uncertainty rather than presenting an expected-value calculation as a guarantee.
Check before using
Check probability estimates and decision sensitivity; easy-to-measure variables may have little value.
Business activities
- Research customers and markets
- Plan products and business changes
FROM A RECIPE TO YOUR WORKFLOW
Bring the company context together.
This workflow needs reliable inputs before it needs a better prompt. Moat8 Vault brings working information into a readable archive for the AI tools you use.
For this recipe: Alternatives · Uncertain assumptions · Research costs
Start with one recurring task, agree which sources to use, and review the first result with your team.
Discuss this workflow