valuation_simulation

io.github.simonmak-ascent/intangible-valuation · Verify this server

Uncertainty analysis: Monte Carlo valuation, Monte Carlo sensitivity ranking, decision-tree expected values, and one-at-a-time sensitivity analysis. Method selects the formula. Use to quantify and stress the uncertainty around a point valuation; monte_carlo simulates all listed inputs, monte_carlo_sensitivity ranks the drivers. For a single deterministic point value use the relevant valuation tool; sensitivity_analysis varies one parameter of a core function only. Per method: monte_carlo needs input_distributions (optional: iterations, seed); monte_carlo_sensitivity needs base_params + distributions (optional: iterations, seed); decision_tree needs tree; sensitivity_analysis needs function_name + parameter_name + parameter_range + fixed_parameters. monte_carlo_sensitivity requires iterations between 1000 and 100000. Only method is required; all other parameters are method-dependent — supply those the selected method names and omit the rest (defaults apply where defined). Rates and premiums are decimals (0.10 = 10%). Pure arithmetic: no I/O and no external calls, rounded to 2 decimals; parameters belonging to other methods are accepted and ignored. An unknown method, or a missing method-required parameter, returns an error instead of a value.