Fraction of iterations (strictly in the range [0.01, 0.98]) where the buyerFixed scenario
had the highest winVariable at this checkpoint. Win rates are clipped/smoothed to reflect
unmodeled tail risks and prevent deterministic misinterpretation (e.g., [1.0, 0.0, 0.0] becomes [0.98, 0.01, 0.01]).
Fraction of iterations (strictly in the range [0.01, 0.98]) where the buyerVariable scenario
had the highest winVariable at this checkpoint. Win rates are clipped/smoothed to reflect
unmodeled tail risks and prevent deterministic misinterpretation (e.g., [1.0, 0.0, 0.0] becomes [0.98, 0.01, 0.01]).
Fraction of iterations (strictly in the range [0.01, 0.98]) where the renter scenario
had the highest winVariable at this checkpoint. Win rates are clipped/smoothed to reflect
unmodeled tail risks and prevent deterministic misinterpretation (e.g., [1.0, 0.0, 0.0] becomes [0.98, 0.01, 0.01]).
Median winVariable amount for each category at each snapshot.
Month indices of each snapshot (e.g. [59, 119, 179, 239, 299] for a 25-year sim, i.e. end of years 5, 10, 15, 20, 25).
Decodes a
winnerSnapshotsresult into a flat record array.Each record represents one 5-year checkpoint (strictly before
numberOfYears).renter,buyerFixed, andbuyerVariableare win rates strictly in the range [0.01, 0.98] (smoothed to [0.01, 0.01, 0.98] in extreme scenarios) and sum to 1. The median fields hold the medianwinVariableamount for that category at the checkpoint.