b2KIT

Expected Value / Decision Tree Calculator

Build decision trees with probability-weighted outcomes, expected monetary value, and sensitivity analysis for business decisions.

Tested tool guide Tested browser tools Checked August 16, 2026

What Expected Value / Decision Tree Calculator does, with a checked example

Enter each decision alternative, its chance branches, branch probabilities, and monetary outcomes. The calculator multiplies each terminal outcome by its path probability, rolls those weighted values up the tree, and compares alternatives by expected monetary value. Its sensitivity analysis tests how changes to an assumption can change that ranking. The common surprise is that EMV is not the payoff most likely to occur in a single decision; it is a probability-weighted average based on the entered scenarios.

Worked example

A concrete input and expected output from the current implementation.

Input

Decision: Launch
Success | probability 0.60 | outcome $50,000
Failure | probability 0.40 | outcome -$20,000
Decision: Hold
Certain | probability 1.00 | outcome $5,000

Expected output

Launch EMV: $22,000
Hold EMV: $5,000

Launch has an EMV of (0.60 x $50,000) + (0.40 x -$20,000) = $22,000. Hold has an EMV of 1.00 x $5,000 = $5,000, so Launch leads by $17,000 under these assumptions.

How the result is produced

1

Rolling up the tree

For each terminal branch, multiply its monetary outcome by the probability of reaching it. In a nested tree, the path probability is the product of the chance probabilities encountered along that path. Add terminal contributions beneath a chance node, then compare the resulting EMVs at the decision node. Costs already included in terminal outcomes should not be added again.

2

Testing sensitivity

Sensitivity analysis changes an uncertain probability or payoff and recalculates the competing alternatives. The useful result is the break-even value where their EMVs are equal; on either side, the preferred alternative can switch. This analysis does not establish whether the tested range is credible. It only shows how strongly the ranking depends on assumptions supplied by the user.

Good uses

  • Compare bidding on a contract with declining when winning, losing, preparation costs, and resulting profits have estimated probabilities.
  • Evaluate a product launch against a pilot or delay using separate demand outcomes, launch costs, and downside losses.
  • Compare suppliers by assigning monetary consequences to on-time delivery, delay, quality failure, and replacement scenarios.

Limits and checks

  • Probabilities must be coherent within each chance node. If branches overlap or omit a possible outcome, the resulting EMV does not represent the intended decision.
  • A high EMV can hide an unacceptable worst-case loss. EMV alone does not reflect liquidity limits, risk appetite, or a nonlinear preference for money.
  • Mixing future nominal cash with present dollars, or counting a shared cost in both a parent decision and terminal outcomes, can distort the comparison.

Common questions

Should branch probabilities add to 100%?

Yes, for mutually exclusive and collectively exhaustive outcomes leaving the same chance node. Probabilities from different nodes do not need to total 100% together. In a nested tree, the probability of a terminal outcome is the product along its path. If outcomes overlap, reorganize the branches before interpreting the EMV.

Does the highest EMV identify the decision I should take?

No. It identifies the largest probability-weighted monetary result under the values entered. A one-time decision with severe downside, binding cash constraints, nonfinancial objectives, or poorly supported probabilities may justify another choice. Treat the ranking as one decision criterion, and inspect downside cases and sensitivity before using it as a recommendation.

References and verification

The example and behavioral notes were checked against the browser implementation. Standards and primary references below define the relevant format, formula, or platform behavior.

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