Karl refers to as a legal warrant, i.e., âa proposition expressing the conditions under which a legal ... ing the English case of Bourhill v.Young, A.C. 92 ... not owe a duty of reasonable care to the plaintiff, and one in which liability was denied ...
A Reduction-Graph Model of Ratio Decidendi L. Karl Branting 1993 There are two main ideas in this 1993 paper by Karl Branting, and both are mentioned in the title: A “reduction-graph model” is a concept from the field of computer science, and “ratio decidendi” is a concept from the field of jurisprudence. The paper is significant because it combines these two ideas to support a claim about the adequacy of a pure exemplar-based theory of legal reasoning. Computer scientists, especially those familiar with logic programming, will recognize Karl’s reduction-graph model as a variant of an SLD-derivation. (For example, compare Figure 2 in Karl’s paper with Figure 1 in [7]) What Karl refers to as a legal warrant, i.e., “a proposition expressing the conditions under which a legal predicate is satisfied [p. 42],” is actually a definite clause in Horn clause logic. What Karl refers to as exemplars, i.e., “collections of facts, expressed in a concrete case-description language [p. 43],” are actually sets of unit clauses in Horn clause logic. Finally, a reduction operator [p. 43] corresponds to a single step in a resolution proof, in which an atom in the body of one clause is resolved with the head of another clause. String a sequence of these reduction operators together, and you have an SLDderivation, or an SLD-tree, and Karl argues that this tree should be viewed as the “justification” of the legal decision. “A justification for the conclusion that a predicate applies to a case therefore consists of a warrant for the predicate together with all reductions necessary to match the antecedents of the warrant to the facts of the case [p. 43].” Now, what does this have to do with the jurisprudential concept of ratio decidendi ? In traditional jurisprudence, identifying the ratio of a case was a way to specify which components of a precedent should be taken as authoritative in subsequent cases. Although the literature on this subject was immense, Karl identified four characteristics of the ratio that most legal 1
philosophers would agree upon: (1) it should include the propositions of law that are necessary for the decision, as opposed to mere dictum, which is a proposition of law that could be negated without changing the result; (2) it only rarely consists of a single proposition, but usually includes a range of propositions, at various levels of abstraction; (3) it should be “grounded in the specific facts of the case [p. 41]”; and (4) it should include “not only the precedent’s material facts and decision, but also the theory under which the material facts lead to the decision [p. 42].” Karl then argues that the entire reduction-graph, and all of its components, i.e., the entire SLD-tree that constitutes the “justification” of the legal decision, should be taken as the ratio decidendi of the case. Actually, the fourth characteristic in the preceding list was, at one time, somewhat controversial. Arthur Goodhart had argued that the ratio decidendi should include only the material facts and the outcome of a case, and not the reasons given by the judge for his decision [5]. Others, such as Rupert Cross [4], had argued that the theory under which a case was decided was equally important in its role as a precedent in subsequent cases. Using the English case of Bourhill v.Young, A.C. 92 (1943), as an example, Cross showed that there were two theories under which that case could have been decided, one in which liability was denied because the defendant did not owe a duty of reasonable care to the plaintiff, and one in which liability was denied because the defendant’s conduct was not a proximate cause of the plaintiff’s harm, and it would make a difference in future hypothetical cases which of these two theories was employed. In Figure 3 and Figure 4 of his paper, Karl illustrates these two theories of Bourhill v.Young using his reduction-graph model, and shows that the complete SLD-trees are needed to capture Cross’s distinction, since the material facts of Bourhill v.Young are exactly the same in both trees. In the final section of the paper, Karl takes this argument one step further, and applies it to several contemporary examples of case-based legal reasoning. His main point here is that a “pure exemplar-based” theory of precedent would have to be consistent with the Goodhart view of ratio decidendi, and therefore subject to the critique of Cross in a case such as Bourhill v. Young. Four approaches to exemplar-based reasoning are considered: (i) structural similarity [6]; (2) dimensional analysis [1]; (3) the nearest neighbor classification rule [8, 10]; and (4) my own prototype-plus-deformation model [9]. For each approach, Karl argues that the authors of these studies have adopted the Goodhart view, implicitly and unavoidably, thus making it im2
possible to represent the distinction that Cross wants to make in Bourhill v.Young. Karl Branting published another article1 on his reduction-graph model in a special issue of the journal Artificial Intelligence in 2003 [3], but the extensive jurisprudential debate was not included. There is a short discussion of ratio decidendi, one citation in the text to Rupert Cross, and an illustration of the alternative theories in Bourhill v.Young, but there is no mention of Arthur Goodhart, and no evaluation of the reduction-graph model in terms of the Goodhart/Cross debate. Curiously, there is not even a citation to Karl’s previous ICAIL 1993 paper! Perhaps Karl had decided by then that the Goodhart/Cross debate was ancient history in jurisprudence, and no longer relevant. He remarks that the concept of ratio decidendi was part of “the orthodox view of precedent,” and notes that “many legal scholars would argue that the orthodox view is a drastic simplification of the actual use of precedents in legal discourse and problem solving.” But even if our jurisprudential theories are much more sophisticated today, our AI models are simple enough that they can benefit from the criticism of previous generations of legal scholars. Evaluating a computational model using the standards of a jurisprudential theory sets a good precedent, in my opinion, for future research.
References [1] Kevin D. Ashley. Modeling legal argument: Reasoning with cases and hypotheticals. MIT Press, 1990. [2] Karl Branting. A computational model of ratio decidendi. Artificial Intelligence and Law, 2(1):1–31, 1993. [3] Karl Branting. A reduction-graph model of precedent in legal analysis. Artificial Intelligence, 150(1-2):59–95, 2003. [4] Rupert Cross. Precedent in English Law, 3rd Edition. Oxford University Press, 1979. 1
An expanded version of the ICAIL 1993 paper was also published in Artficial Intelligence and Law [2]
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[5] Arthur Goodhart. Determining the ratio decidendi of a case. Yale Law Journal, 40(2):161–183, 1930. [6] Keith J. Holyoak and Paul Thagard. Analogical mapping by constraint satisfaction. Cognitive Science, 13:295–355, 1989. [7] John W. Lloyd. Springer, 1987.
Foundations of Logic Programming, 2nd Edition.
[8] Ejan Mackaay and Pierre Robillard. Predicting judicial decisions: The nearest neighbour rule and visual representation of case patterns. Datenverarbeitung im Recht, 3(3-4):302–331, 1974. [9] L. Thorne McCarty. Invited address: On the role of prototypes in appellate legal argument. In Proceedings of the 3rd international conference on Artificial Intelligence and Law, ICAIL ’91, pages 185–190, 1991. [10] Alan Tyree. Expert systems in law. Prentice Hall, 1989.
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