Investment decision analysis
Decision-analysis tree evaluating build-vs-buy vs hybrid for a platform choice — chance nodes with probabilities, automatic expected-value rollback.
For the CTO evaluating vendors
Scenario
A CTO is deciding how to stand up a new data-platform layer: build internally, buy managed SaaS, or take a hybrid. Each path has outcomes with probabilities and net-value estimates. The decision-analysis tree rolls the expected value back to the decision node so the optimal branch is identified automatically.
Annotation key
decision "…"— actor chooses a branchchance "…"— nature chooses;prob Non each child (must sum to 1)end "…" payoff=N— terminal payoffchoice "label"— label on an outgoing decision branch- EV rollback: chance = probability-weighted sum; decision = max child EV
How to read
Evaluate each branch's expected value. Build in-house: 0.6 × 900k + 0.4 × 150k = 600k. Managed SaaS: flat 500k. Hybrid: 0.5 × 700k + 0.5 × 300k = 500k. Under these estimates the optimal branch is Build in-house — the parser flags it on render. The chart's real value is in forcing stakeholders to state probabilities explicitly; sensitivity to them is where the interesting argument happens.