Alternative Views of Argument Construction from a Mass of Evidence
David A. Schum · Studies in fuzziness and soft computing · 2002
Work is now progressing on attempts to close a very important technology gap. We are still far better at gathering, transmitting, storing, and retrieving information than we are at drawing defensible conclusions from this information. This gap is as apparent in law as in any other context in which conclusions are reached based on masses of evidence. Some of the work being done to close this gap involves study of what are now called “inference networks.” Such networks are representations for complex probabilistic reasoning tasks often based on masses of evidence. However, this approach is not at all new. Some of it dates from the early twentieth century in the work of John H. Wigmore, whose name is certainly well known among legal scholars and practitioners on this side of the Atlantic. It happens that Wigmore was the very first person to study complex inference networks in any systematic way. Until quite recently, Wigmore’s work on inference networks was not taken seriously within his own field of law, and it has been almost entirely unnoticed by persons in other disciplines in which there is equal concern about complex probabilistic reasoning tasks based on masses of evidence. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.