Platform Demo · Lab 03

Monte Carlo, on demand.

The engine's loss model draws claim frequency and severity from normal distributions and multiplies them per trial. The shipped workbook carries 400 pre-seeded trials it cannot regenerate. Here you can replay those exact draws — or change the parameters and re-simulate as many times as you like.

Sample Platform Output - Anonymized Carrier SPC-01 - Demonstration Only

Simulation parameters

Loss per trial = max(N(μf, σf), 0) × max(N(μs, σs), 0). Seeded draws make every run reproducible — same seed, same answer, audit-grade.

Mean loss
Median loss
P95 loss
ES95 (tail mean)
Trials

Loss distribution

Allocation objective

Decision weights against sleeve return coefficients · objective = Σ weight × coefficient

Objective value

recomputes as you edit weights

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