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2 A.I. & L. 1 (1994)

handle is hein.journals/artinl2 and id is 1 raw text is: Artificial Intelligence and Law 2: 1-31, 1994. 1 © 1994 Kluwer Academic Publishers. Printed in the Netherlands. A Computational Model of Ratio Decidendi L. KARL BRANTING Department of Computer Science, University of Wyoming, Laramie, WY 82071-3682, U.S.A. E-mail: karl@eolus.uwyo.edu Key words: explanation, ratio decidendi, reduction graph, warrant Abstract. This paper proposes a model of ratio decidendi as a justification structure consisting of a series of reasoning steps, some of which relate abstract predicates to other abstract predicates and some of which relate abstract predicates to specific facts. This model satisfies an important set of characteristics of ratio decidendi identified from the jurisprudential literature. In particular, the model shows how the theory under which a case is decided controls its precedential effect. By contrast, a purely exemplar-based model of ratio decidendi fails to account for the dependency of precedential effect on the theory of decision. 1. Introduction A central obstacle to the development of automated legal reasoning systems for common- law jurisdictions is the problem of modeling precedent-based legal reasoning. In common-law jurisdictions, reasoning with precedents is an essential part of every effec- tive attorney's repertory of skills. A computer system intended to emulate human problem-solving behavior in such domains must therefore also be capable of reasoning with precedents. Any complete account of precedent-based legal reasoning must include a model of ratio decidendi, the content of a precedent that is authoritative as to subsequent cases. Predicting, advocating, and justifying the binding effect of a precedent on subsequent cases all require identifying the authoritative elements of the precedent and showing how they can apply to subsequent cases involving different facts. Development of an adequate computer model entails three distinct tasks. First, the phe- nomenon to be modeled must be precisely specified. Second, an appropriate computa- tional model must be described. Finally, the ability of the computational model to account for the phenomenon must be demonstrated. The next section addresses the the first of these tasks - describing the phenomenon to be modeled - arguing that the jurisprudential literature on legal precedent provides a set of criteria for the adequacy of models of ratio decidendi. Section three describes a model of ratio decidendi, termed the reduction-graph model, under which the ratio decidendi of a precedent is a justification structure consisting of a series of reasoning steps, some of which relate abstract predicates to other abstract predicates and some of which relate abstract predicates to specific facts. Section four argues that the reduction-graph model

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