Reconciling Asymmetric Information and Divergent Expectations Theories of Litigation*

Both asymmetric information (AI) and divergent expectations (DE) theories offer possible explanations of the litigation puzzle. Under DE, cases proceed to trial when, by chance, the plaintiff is more optimistic than the defendant. As the fraction of cases tried (T) declines, this leads to a tendency toward 50 percent plaintiff win rates at trial (P), regardless of the fraction of plaintiff winners in the filed population. Under AI, by contrast, informed parties proceed to trial only when they expect to win. Hence, as the fraction of cases tried declines, plaintiff win rates at trial tends toward either 0 or 1. I present evidence that the relationship between T and P generated by the litigation process is consistent with DE and not AI. I also offer evidence of the presence of AI early in litigation in the form of one‐sided plaintiff win rates in cases adjudicated prior to trial. I reconcile these two findings with evidence that pretrial adjudication and settlement culls both likely plaintiff winners and likely plaintiff losers from the filed pool, causing a tendency toward central rather than extreme plaintiff win rates at trial.