This paper uses axiomatic and Bayesian methods to model information and decisions in a hierarchical judicial system. We use a set of axioms to summarize decision-making at the trial level, where the axioms represent the desirable properties of judicial decisions that rules of evidence, procedure, precedent and higher court review impose. A family of one-parameter functions provides the unique continuous solution to the axioms. The parameter is then interpreted as the type space for the appeals stage. In this stage the appellant (the losing defendant from the trial) and the appeals court each receive private signals of a yet superior court's value of the parameter. The defendant chooses whether or not to appeal the lower court's decision and the appeals court is able to use that action to improve its estimate of the superior court's preferred value of the parameter. The equilibrium involves a cutoff value of the defendant's signal, such that only those with signals at or below the cutoff would choose to appeal their loss. Higher awards at trial, or higher costs of appeal, affect the overall equilibrium by changing the cutoff value used to make the decision to appeal (higher awards increase the cutoff while higher costs decrease it). An increased evidentiary standard also increases the cutoff, resulting in an increased caseload for the appeals court. These changes not only affect the marginal type, but the inframarginal type, too. Finally, if a decrease in appeals court resources results in a reduction in the informativeness of the appeals court's signal, this can lead to decreased informativeness of the appellant's decision (as increased reliance by the appeals court on this information source induces adverse strategic behavior), with the result that the equilibrium relies only on the prior distribution of the superior court's preferred value of the parameter.
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