Predicting civil litigation outcomes and the evolution of case complexity and settlement dynamics

Legal disputes unfold through sequences of filings in which parties update their positions and may settle at any stage. Most computational studies of legal prediction, however, focus on adjudicated outcomes and treat cases as static objects observed only at the end of litigation. Here we develop a temporally structured framework for predicting outcomes in civil litigation using 835,190 court filings between 1996 and 2022. We represent each case as a sequence of documents and model litigation as a three-outcome process: plaintiff win, plaintiff loss, or settlement. Documents are encoded using structured legal features, text embeddings, and information about judges and law firms, and a classifier estimates outcome probabilities at each stage of the case. The model achieves class-specific AUC values between 0.74 and 0.81, and reaches up to 97% accuracy for high-confidence plaintiff-win predictions. To study heterogeneity in predictability, we define case complexity as the entropy of the predicted outcome distribution. Richer factual and relational information improves prediction primarily in low-complexity cases, whereas its marginal contribution declines as complexity increases, suggesting that some disputes remain difficult not because information is missing, but because outcomes are less determinate. Consistent with this interpretation, complexity increases over the course of litigation, indicating that additional filings can amplify uncertainty rather than resolve it. Settlement rates follow an inverted U-shape with respect to complexity, peaking at intermediate levels of predictive uncertainty and declining at both low and high levels of complexity. These findings suggest that predictive uncertainty is not merely model error, but an empirical signal of legal complexity, litigation dynamics, and the conditions under which disputes are resolved through adjudication or settlement.