In This Article The False Precision of Deterministic Models What Stochastic Models Do Differently Monte Carlo Valuation in Python Reading the Confidence Interval Output Practical Application in Deal Contexts When Deterministic Models Are Still Useful The False Precision of Deterministic Models A standard DCF or EBITDA-multiple valuation produces a single number. That number gets presented in a board deck with two decimal places, anchors a negotiation, and shapes a capital decision worth millions of dollars. The problem is that the number is not a prediction — it is a calculation that depends on input assumptions that are, themselves, uncertain. Changing the revenue growth assumption by two percentage points or the EBITDA multiple by half a turn can move the output by 20–40%. Deterministic models handle this by running three scenarios: base, upside, and downside. This approach has two critical weaknesses.…