Precedent-based legal reasoning and knowledge acquisition in contract law: A process model

ABSTRACT

In the law, decisions in previous cases play a significant role in the presentation, understanding, and outcome of new cases. This is particularly true in the area of contract law where few statutes (explicit legal rules) exist. When presented with a new case, a lawyer must be able to identify important issues and make some predictions about how the case might be decided. The lawyer will often recall past cases which bear similarities to the current case and reason analogically to make these predictions. In order to perform these tasks, a lawyer must be able to remember past cases, organize them in memory so that cases that are conceptually similar are stored together (a lawyer normally won't be reminded of an irrelevant case), and make analogies between cases. Thus the organization and representation of knowledge in memory is crucial in building a model of lawyer's cognitive processes. This paper describes a process model, implemented in a computer program called STARE, which addresses these issues in the context of first-year law students learning contract law.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.1 Applications and Expert Systems
          Subjects: Law

Additional Classification:
  H. Information Systems
  H.1 MODELS AND PRINCIPLES
      H.1.2 User/Machine Systems
          Subjects: Human information processing

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.3 Deduction and Theorem Proving
          Subjects: Deduction (e.g., natural, rule-based)
      I.2.6 Learning
          Subjects: Language acquisition

General Terms:
Design, Human Factors