A single-number valuation of a case hides more than it reveals. It collapses dozens of uncertain inputs, liability probability, comparative fault, the exact damages a jury might award, into one figure that looks precise but is really just one guess among thousands of plausible combinations. A Monte Carlo simulation replaces that single guess with a distribution: it samples thousands of random draws across every uncertain input, runs each draw through the same valuation logic, and reports back the full range of outcomes those draws produced, along with how often each range of results actually showed up.

This is a modeling exercise, not a forecast of what a specific jury will do. The simulation only knows what you tell it about the plausible range and shape of each input; it does not know the future, and it should never be described as predicting one. Juricratic runs this process as a seeded simulation, meaning every run can be reproduced exactly from the same inputs, so the distribution you present is an auditable artifact rather than a black box, and any assumption in it can be traced, questioned, and rerun.

Start by listing the variables that actually drive the case's value and are not yet resolved: the probability the jury finds liability, the percentage of comparative fault likely to be assigned, the range of damages a jury might award for each category of harm, and any procedural contingency like a pending motion that could change the case's posture entirely. Resist the urge to simulate variables you have already effectively resolved through hard evidence; the simulation is only useful where real uncertainty exists, and padding it with settled facts just slows the model without adding insight.

The core discipline of a Monte Carlo model is refusing to describe an uncertain input as a single number. Instead of saying damages will be two hundred thousand dollars, describe the plausible range and the shape of that range: whether outcomes cluster tightly around a likely figure, spread evenly across a band, or carry a long tail toward a catastrophic or unusually favorable result. The shape you choose is itself an assumption, so document why a given input got a triangular, normal, or beta distribution rather than another shape, since that choice measurably changes the simulation's output.

With every uncertain input assigned a distribution, the simulation draws one random value from each distribution, runs that specific combination through the case's valuation logic exactly as a single deterministic scenario would, and records the resulting outcome. It then repeats that process thousands of times, each draw an independent combination of assumptions pulled from the distributions you defined. No single draw is meant to represent the truth; the value comes entirely from looking at the full set of draws together once the run is complete.

The output of a Monte Carlo run is a distribution of outcomes, and the way to read it is through percentiles rather than a single average. The median tells you the outcome half of all draws fell below; the tenth and ninetieth percentiles show the realistic downside and upside case; the mean can be pulled higher or lower than the median by a skewed tail and should never be quoted alone. Present the range alongside the median so a decision-maker can see both the central tendency and how wide the uncertainty actually is before relying on any single figure.

A random seed fixes the sequence of random draws so the same inputs produce the exact same simulated distribution every time the model is rerun. Without a documented seed, two runs of the identical model can produce slightly different output purely from randomness, which undermines the model's credibility as an analytical artifact rather than a one-off exercise. Record the seed alongside the inputs and the resulting distribution so that anyone, including opposing counsel or a court, can rerun the identical simulation and verify the output rather than take it on faith.

Once the baseline distribution exists, rerun the simulation while holding every input fixed except one, tightening or widening that single input's distribution to see how much the overall output shifts. Inputs that move the resulting distribution substantially deserve the most scrutiny and the most evidentiary support; inputs that barely move it can be described more loosely without materially weakening the analysis. This sensitivity pass turns the simulation from a static output into a map of where additional investigation or evidence would actually change the case's value.