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ICM · Research & Decision Studio · decision-lab · Model 1.1.0 · Illustrative inputs

Decision science

Decision lab

Discover whether an imperfect test changes the preferred action enough to pay for itself.

Model 1.1.0 · Illustrative inputs

Method and assumptions
Decision / uncertaintyIllustrative composition
An illustrative branching decision structure. Branch lengths and positions do not encode probabilities or expected values.

The interactive model requires JavaScript. This saved default illustration remains readable if the runtime is unavailable. Values are computed from the canonical model at build time; they describe fictional inputs, with full calculation precision.

ICM · Decision lab · Model 1.1.0 · Default illustrative record

Default illustrative result

Stage the commitment has the highest expected payoff before new evidence. The supplied test adds net expected value after its cost. The information-value ceiling is 178.25 USDk for perfect state knowledge.

Default result metrics
MetricValueUnitCalculation boundary
Best value before information55.75USDk
Gross value of this test94.99USDkMaximum justified test price under these payoffs.
Net information value76.99USDkGross information value less test cost.
Value of perfect information178.25USDkUpper bound for any information about these two states.
Action payoffs, value and regret
ActionSuccess payoffUSDkFailure payoffUSDkExpected payoffUSDkSuccess regretUSDkFailure regretUSDkExpected regretUSDk
Commit capital520-340470340187
Stage the commitment240-9555.7528095178.25
Wait0005200234
Bayes test branches
SignalBranch probabilityPosterior success probabilityPreferred actionConditional expected payoffUSDkWeighted contributionUSDk
Positive test0.490.753061224489796Commit capital307.6326530612245150.74
Negative test0.510.1588235294117647Wait00
Prior probability policy sweep
Prior success%Preferred actionBest expected payoffUSDk
0Wait0
5Wait0
10Wait0
15Wait0
20Wait0
25Wait0
30Stage the commitment5.5
35Stage the commitment22.25
40Stage the commitment39
45Stage the commitment55.75
50Commit capital90
55Commit capital133
60Commit capital176
65Commit capital219
70Commit capital262
75Commit capital305
80Commit capital348
85Commit capital391
90Commit capital434
95Commit capital477
100Commit capital520
Pairwise policy intersections
Action AAction BPrior at equal value%Best of all actions at intersection
Commit capitalStage the commitment46.666666666666664Commit capital
Commit capitalWait39.53488372093023Stage the commitment
Stage the commitmentWait28.35820895522388Stage the commitment

Inputs behind this result

Current general assumptions
Assumption and unitExact supplied value
Prior probability of success (%)45
Test sensitivity (%)82
Test specificity (%)78
Information cost (USDk)18
Available actions
ActionPayoff if successUSDkPayoff if failureUSDk
Commit capital520-340
Stage the commitment240-95
Wait00
Download exact default inputs and output

Transparent method

  1. Expected value = prior × success payoff + (1 − prior) × failure payoff.
  2. Bayes updates the prior separately after a positive and negative test. Branches of probability zero have no posterior.
  3. EVSI = expected best payoff after the imperfect test − best payoff before it. EVPI is the analogous value of perfect state knowledge.
  4. Net information value = EVSI − test cost. Payoffs are assumed to exclude this test cost.
  5. Joint signal-state masses, action rankings, EVSI, EVPI and the net-value sign use exact rational arithmetic for canonical decimal inputs. Only an exact zero is labelled break-even.
  6. Tied action values use literal name order independent of browser language for consistent display; this ordering does not establish a strict payoff advantage.

Limits

  • Binary states, a single test, user-calibrated probabilities and risk-neutral expected monetary value.
  • No repeated sampling, behavioural utility, ambiguity aversion or causal proof.
  • Charts and headline values are numeric displays of exact rational results. Decimal input values are assumptions; risk-neutral economic comparison does not establish acceptable action or calibrated probability.
  • Numerical input precision: use zero or absolute values of at least 0.000000001. Smaller nonzero inputs are rejected explicitly.

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