Value of Information Assessment Worksheet

Purpose: Quickly estimate whether running an analysis or experiment is likely to be worth the cost. This worksheet helps you prioritize analytic work by focusing on expected decision value rather than raw curiosity.

How it works: Enter simple monetary estimates and a probability that new information will change your decision. Use the formulas below to judge whether the expected benefit of being able to change the decision exceeds the cost of collecting the information.

Key formulas (use the same currency for all money fields):

• Difference in expected payoff if decision changes = Alternative EV − Current EV
• Expected value of changing decision = (Alternative EV − Current EV) × (Probability change as a fraction)
• Net expected value of information ≈ Expected value of changing decision − Cost of information − (Probability change × Implementation cost if changed)
• Simple break-even probability (ignoring implementation cost) = Cost of information / (Alternative EV − Current EV). If the required probability is lower than your estimate of chance that the analysis will change the decision, the analysis is likely worth running.

Worked example — pricing decision:

• Current EV = 10,000
• Alternative EV = 12,000
• Difference = 2,000
• Estimated probability the experiment/analysis will change the decision = 30% (0.30)
• Cost of analysis = 200
• Implementation cost if changed = 0
• Expected value of changing decision = 2,000 × 0.30 = 600
• Net expected value of information = 600 − 200 = 400 → positive, so running the analysis appears warranted.
• Break-even probability = 200 / 2,000 = 0.10 (10%)

Notes: These estimates are only as good as the inputs. Use conservative probability estimates when uncertainty is high. If implementation constraints, timing, stakeholder incentives, or decision rules are unclear, treat the result as exploratory rather than definitive.