Monte Carlo simulation meaning

Monte Carlo simulation is a probabilistic modelling technique used in legal practice to assess valuation and risk by running thousands of model iterations (often a Discounted Cash Flow (dcf) model) with inputs randomly drawn from specified probability distributions. It yields a distribution of outcomes (e.g., valuation, damages, cost overrun), with statistics such as mean, median and percentiles, rather than a single point estimate. It is commonly used in M&A and corporate valuation, fairness opinions, insolvency and restructuring valuations, damages quantification in litigation and arbitration, competition and regulatory price controls, project finance/PPP risk allocation, and derivatives and securities pricing. Key features include: defined inputs and distributions (for example growth, margins, interest/FX/commodity prices), treatment of correlations, number of iterations, and transparent reporting of outputs and confidence levels. Reliability depends on data quality and justifiable assumptions. The term is not defined in legislation or case law; it is a descriptive expression used consistently across England & Wales, Scotland, Northern Ireland and Ireland. Where deployed in expert evidence, courts will expect methodology that is transparent, replicable and within the expert’s competence, with assumptions and sources properly disclosed (e.g., under CPR Part 35 and equivalent rules). Often used alongside scenario and sensitivity analysis.