Monte Carlo simulation values a case by running it many times. Instead of plugging single best-guess numbers into a formula, you specify each uncertain input as a range or distribution, then draw random values thousands of times and record the outcome each time. The result is not one number but a distribution: the average outcome, the spread around it, and the shape of the tails where the best and worst results live. It is the natural companion to a decision tree once you accept that the inputs are uncertain.

The method's strength is that it captures uncertainty and interaction that point estimates hide. A case might have a comfortable average value but a fat left tail where a small chance of a catastrophic verdict dominates the real risk. Monte Carlo surfaces that tail directly, and it lets correlated inputs move together, so you are not pretending that every uncertain factor is independent. The output is a picture of what could happen, expressed in probabilities you can act on.

Its honesty depends entirely on its inputs: garbage distributions produce a confident-looking garbage histogram, so the ranges must reflect real judgment. Juricratic runs these simulations under an explicit seed so every run is reproducible and auditable, and it presents the spread rather than collapsing it to a single figure. That keeps a simulation what it is meant to be, a structured exploration of possibility, not a dressed-up prediction.

Litigation teams and funders use Monte Carlo analysis to set reserves that account for tail risk, to price a portfolio of cases, and to show a client the full range of outcomes rather than a single point. It is especially useful where damages are wide-ranging or where several uncertain events compound. Presenting a distribution, with its percentiles, tends to lead to more disciplined settlement decisions than a single average that hides the downside.