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Philipps, Lothar (1991): Distribution of Damages in Car Accidents throught the Use of Neural Networks. In: Cardozo Law Review, Vol. 13: pp. 987-1000 [PDF, 1MB]

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After a traffic accident the damage has to be fairly divided among the parties involved, and a ratio has to be determined. There are many precedents for this, and judges have developed catalogues suggesting ratios for common types of accidents. The problem that "every case is different," however, remains. Many cases have familiar aspects, but also unfamiliar ones. Even if a case is composed of several familiar aspects with established ratios, the question remains as to how these are to be figured into one ratio. The first thought would be to invent a mathematical formula, but such formulae are rigid and speculative. The body of law has grown organically and must not be forced into a sleek system. The distant consequences of using a mathematical formula cannot be foreseen; they might well be grossly unjust. I suggest using a neural network instead. Precedents may be fed into the network directly as learning patterns. This has the advantage that court rulings can be transferred directly and not via a formula. Future modifications in court rulings also can be adopted by the network. As far as the effect of the learning patterns on new cases is concerned, a relatively safe assumption is that they will fit in harmoniously with the precedents. This is due to the network's structure—a number of simple decisional units, which are interconnected, tune their activity to each other, thus achieving a state of equilibrium. When the conditions of such an equilibrium are translated back into the terms of the case, the solution can hardly be totally unjust.

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