We examine a model of case-by-case learning by judges and litigants. A judge hearing cases learns partial information about the best legal rule, gradually par-titioning the case space. The evolution of doctrine is path dependent but dis-plays strong limit properties, converging to the best legal rule. Litigant behavior strongly affects the speed of convergence. If existing case law induces litigants to modify their behavior, convergence is faster because more cases bring new in-formation. Also, if processing information about cases is costly, the judge will optimally stop learning before convergence, leaving residual uncertainty in the law and some cases wrongly decided.
|Number of pages
|Journal of Institutional and Theoretical Economics
|Published - 2023
All Science Journal Classification (ASJC) codes
- Economics and Econometrics
- judicial learning