Decision Tree Analysis (Litigation)
A branching diagram that maps every decision and chance event in a case so you can price each path.
Browse the glossary →A decision tree lays a case out as a sequence of forks. Some forks are choices you control, such as whether to file a motion or accept an offer; others are chance events you do not control, such as how a judge rules or how a jury finds. Each chance branch carries a probability, each endpoint carries a dollar value, and by multiplying and summing back through the tree you can attach an expected value to every branch and to the case as a whole.
The discipline is in the drawing. Building the tree forces you to name every decision point, every uncertain event, and every outcome, rather than carrying a vague sense of the case in your head. Rolling the tree back from its endpoints, a process often called folding back, tells you which choice maximizes expected value at each fork, and it exposes the branches where a small change in a probability would change the recommended move.
Trees are transparent but they simplify. A handful of branches cannot capture the full continuum of damages or the correlations between rulings, and the numbers are only as good as the estimates fed into them. Juricratic uses the same branching logic but lets a path be simulated many times under a seed, so an endpoint becomes a distribution rather than a single pinned value, and the tree stays auditable rather than becoming a black box.
Branch EV = sum over outcomes of P(outcome) x value(outcome); choose the branch with the highest EV
How it actually shows up
Litigators and their clients use decision trees to structure settlement discussions, to justify a reserve or a demand, and to compare a proposed deal against the value of trying the case. Insurers and in-house counsel lean on them to make consistent, defensible decisions across a portfolio. The tree's real value is often the conversation it forces: disagreements about a number become explicit and can be tested instead of argued in the abstract.
- What is a decision tree in litigation?
- It is a diagram that breaks a case into a sequence of decisions you control and chance events you do not, each chance branch weighted by a probability and each endpoint given a value. By working backward from the endpoints you get an expected value for every path and a clear view of which choice looks best at each fork.
- How do you build a litigation decision tree?
- Start at the present decision, then branch forward through each motion, ruling, and verdict the case can reach. Attach probabilities to the chance branches and dollar values to the endpoints, then fold the tree back by multiplying and summing. Keep the branch count small enough to reason about and stress-test the numbers.
- What are the limits of decision tree analysis?
- Trees compress a continuous range of outcomes into a few branches and assume the probabilities you supply are right. They also tend to treat rulings as independent when they are often correlated. They are excellent for structuring thinking, but the output is only as trustworthy as the estimates and the structure behind it.
This page is an educational explainer, not legal advice, and creates no attorney–client relationship. Juricratic is a simulation engine: every probability-like figure is a dial you set, not a calibrated prediction. Verify every rule, deadline, and figure against the authorities and orders that govern your matter.
Turn the concept into a modeled matter.
Juricratic makes every one of these ideas a live dial: model your case as a solvable game, then watch the optimal line and the settlement window move as the assumptions do.
Request access →