Decision Frameworks & Value of Information Calculator

What this calculator helps you do

Use this worksheet to focus analytics and experiments where they change decisions. The approach estimates how much better your decisions could be if you had new information, subtracts the cost of getting that information, and produces a simple priority signal that blends net value, time-to-deliver, and implementation feasibility.

When to use it

• Before committing scarce analyst time to an experiment, model, or deep analysis.
• When you must choose which experiments or analyses to run this quarter.
• To create a short-list of high-impact opportunities for rapid testing or data collection.

Key inputs (and how to estimate them)

• States of the world: List the plausible outcome states that matter for the decision (for example, high demand vs low demand). For each state give a probability (your current belief).
• Actions: List the candidate decisions or policies you might choose (for example: order 100 units, order 50 units).
• Consequence matrix: For each action-state pair estimate the monetary (or utility) payoff. Use expected net value (revenue minus variable & fixed costs, lost sales, holding costs, penalties, etc.).
• Cost of the analysis/experiment: All-in cost to run the work (people, tools, incentives, incremental hardware, ad spend, etc.).
• Time-to-deliver: Calendar time until the result is usable (weeks or days). Faster results raise priority.
• Implementation feasibility: An estimate between 0 and 1 of the probability the recommended decision (if the analysis favors it) can actually be implemented within constraints and incentives.

What the calculator computes

At a conceptual level the calculator compares three numbers:

1. Expected value of the best action right now, given current uncertainty.
2. Expected value if you had additional information (perfect information is an upper bound; a specific experiment gives an expected posterior value).
3. Net expected value = (value of additional information) − (cost of getting it).

Step-by-step method (matrix form)

Use the following approach for a finite set of discrete states and actions.

1. Current expected value (no new information)

For each action a, compute EV(a) = sum over states s of P(s) × payoff(a, s). The best current expected value is CurrentEV = max_a EV(a).

2. Expected value with perfect information (upper bound)

For each state s, find BestPayoff(s) = max_a payoff(a, s). Then ExpectedPerfectPayoff = sum_s P(s) × BestPayoff(s).

Expected Value of Perfect Information (EVPI) = ExpectedPerfectPayoff − CurrentEV.

3. Expected value of a real experiment or analysis (EVSI, approximate)

If your proposed experiment can produce specific outcomes o with probabilities P(o) and each outcome yields updated posterior probabilities P(s | o), then for each outcome compute PosteriorBest(o) = max_a sum_s P(s | o) × payoff(a, s). The ExpectedPosteriorBest = sum_o P(o) × PosteriorBest(o).

Expected Value of Sample Information (EVSI) = ExpectedPosteriorBest − CurrentEV.

When detailed posterior scenarios are hard to enumerate, use a simple approximation: estimate how much the experiment can move the probability of key states (sensitivity bands) and recompute. EVPI is often a useful upper bound to gauge if further precision is worth pursuing.

4. Net expected value and prioritization score

NetEV = EVSI (or EVPI as an upper bound) − Cost. If NetEV <= 0 then the analysis is not worth doing on pure expected-value grounds.

PriorityScore = max(NetEV, 0) × Feasibility / TimeToDeliver (use consistent time units, e.g., dollars per week). Higher scores imply higher priority for allocation of analytics/experiment capacity.

Worked example — Marketing A/B test (simple)

Scenario: decide whether to run a $10,000 campaign (Action = Run) or not run it (Action = Don't). Two states: high response (H) and low response (L). Current belief: P(H)=0.30, P(L)=0.70.

Consequence matrix (net profit):

• Run & H: $300,000
• Run & L: −$50,000
• Don't & H: $0
• Don't & L: $0

Compute EVs:

EV(Run) = 0.30×300,000 + 0.70×(−50,000) = 90,000 − 35,000 = $55,000.

EV(Don't) = 0.

CurrentEV = max = $55,000 (so without more info you would run).

Perfect information payoff:

BestPayoff(H) = 300,000; BestPayoff(L) = 0. ExpectedPerfectPayoff = 0.30×300,000 + 0.70×0 = $90,000.

EVPI = 90,000 − 55,000 = $35,000.

If a preliminary A/B experiment is expected to reduce uncertainty enough that EVSI ≈ $35,000 (optimistic), then NetEV = 35,000 − 10,000 = $25,000. If TimeToDeliver = 4 weeks and Feasibility = 0.9, PriorityScore = 25,000×0.9/4 = $5,625 per week.

Interpretation: A positive net EV and a healthy priority score make the experiment a good candidate relative to low-scoring alternatives.

Worked example — Inventory decision (two-order sizes)

Scenario: choose to order 100 units or 50 units before a season with uncertain demand. States: HighDemand (P=0.4) and LowDemand (P=0.6).

Consequence matrix (net profit after accounting for holding/stockout costs):

• Order 100 & HighDemand: $120,000
• Order 100 & LowDemand: $10,000
• Order 50 & HighDemand: $60,000
• Order 50 & LowDemand: $30,000

EV(Order 100) = 0.4×120,000 + 0.6×10,000 = 48,000 + 6,000 = $54,000.

EV(Order 50) = 0.4×60,000 + 0.6×30,000 = 24,000 + 18,000 = $42,000.

CurrentEV = $54,000 (choose Order 100).

Perfect information payoff: BestPayoff(High) = 120,000; BestPayoff(Low) = 30,000. ExpectedPerfectPayoff = 0.4×120,000 + 0.6×30,000 = 48,000 + 18,000 = $66,000.

EVPI = 66,000 − 54,000 = $12,000. If a demand-forecasting pilot costs $5,000 and reduces uncertainty close to perfect, NetEV ≈ 7,000. If TimeToDeliver = 2 weeks and Feasibility = 0.8, PriorityScore = 7,000×0.8/2 = $2,800/week.

Interpretation guidance and cautions

• EVPI is an upper bound. Real experiments usually deliver less value than perfect information; use EVSI when possible.
• Be explicit about the decision rule you will apply when new information arrives. VoI calculations assume you can act on the result.
• Watch implementation risk: a high EV that cannot be executed (low feasibility) should be deprioritized.
• Time matters: long delivery delays reduce practical value, especially for fast-moving markets and seasonal decisions.
• Avoid overconfidence in precise numbers. Use sensitivity checks: vary key probabilities and payoffs to see how sensitive priority is to your assumptions.

Practical next steps

• Use this worksheet to estimate NetEV and PriorityScore for each candidate analysis or experiment.
• Rank opportunities by PriorityScore and consider analyst availability, strategic importance, and risk appetite.
• For complex problems, create a simple posterior-outcome model for EVSI or run a small simulation to estimate expected posterior gains.

Template checklist

• Have you listed all meaningful states and assigned probabilities?
• Are payoffs expressed in the same units and include all relevant costs?
• Is the cost estimate for the experiment comprehensive?
• Have you estimated feasibility and time-to-deliver realistically?
• Did you perform at least one sensitivity run for key assumptions?

Final note

This worksheet is designed to be practical rather than perfect. Its value is in reallocating scarce analytics effort toward decisions where information changes choices and improves outcomes. Use it as a decision aid, not an oracle.