Build a Monte Carlo simulation of our annual incentive plan funding. The plan has [two] metrics: [Revenue, weighted 60%, and Adjusted EBITDA, weighted 40%]. For each metric, attainment against plan is uncertain. Use the attached history of actual results versus plan for the last [five] years to estimate how far attainment typically lands from target and how the two metrics move together. The payout curve pays [0% below 90% attainment, 50% at 90%, 100% at 100%, and 200% at 120% or above], straight-line between. Target funding is [$12,000,000]. Run 100,000 trials. Before reporting results, run two checks and show me they pass: with no uncertainty at all, funding equals the curve at plan exactly; and a straight-line uncapped curve produces expected payout equal to expected attainment. Then report: the distribution of funding as a chart, expected funding in dollars and as a percent of target, the probability funding lands below target, the probability a metric pays zero, the probability of hitting the cap, and the 10th and 90th percentile of funding. Then show me the same results with the threshold cliff removed, so payout is a straight line from zero instead of dropping to nothing below threshold. Then list every assumption you made that I did not specify.

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How to use this: replace the bracketed items with your plan's metrics, weights, curve and target from the intake sheet, and attach a small table of actual versus plan by metric by year (a spreadsheet with columns Year, Metric, Plan, Actual is enough). Everything else the AI needs is in the prompt. Finance accrues at target; this tells you whether expected funding actually sits at target, and which direction it leans.
