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Reasoning with dimensions and magnitudes

  • John HortyEmail author
Original Research
  • 79 Downloads

Abstract

This paper shows how two models of precedential constraint can be broadened to include legal information represented through dimensions. I begin by describing a standard representation of legal cases based on boolean factors alone, and then reviewing two models of constraint developed within this standard setting. The first is the “result model”, supporting only a fortiori reasoning. The second is the “reason model”, supporting a richer notion of constraint, since it allows the reasons behind a court’s decisions to be taken into account. I then show how the initial representation can be modified to incorporate dimensional information and how the result and reason models can be adapted to this new dimensional setting. As it turns out, these two models of constraint, which are distinct in the standard setting, coincide once they are transposed to the new dimensional setting, yielding exactly the same patterns of constraint. I therefore explore two ways of refining the reason model of constraint so that, even in the dimensional setting, it can still be separated from the result model.

Keywords

Precedent Constraint Dimensions 

Notes

Acknowledgements

This paper is a light revision and expansion of an earlier version (Horty 2017) that was presented at ICAIL 2017, where it won the Carole Hafner Best Paper Award. This recognition was especially meaningful to me since I have fond memories of working with Carole to organize ICAIL 1995. It was in helping Carole organize that conference that I first learned about the field of AI and Law, and how interesting it could be. I am deeply indebted to Trevor Bench-Capon for raising the issue that motivated this paper in the first place, for his enthusiasm, and for his generous comments on earlier drafts. Apart from providing some additional examples and explanations, along with formal proofs of observations, this revision differs from the earlier version appearing in ICAIL 2017 in only two ways. First, the method of interpreting standard information into dimensional information described here, in the Appendix, differs from the interpretation described in Sect. 4 of that earlier version—both interpretations work, and and they provide different insights. Second, this version of the paper includes a discussion of the first refinement of the reason model, found here in Definition 13, which was mentioned in my ICAIL 2017 presentation but not included in the earlier version for reasons of space. Since my intention in this paper is simply to present in a more careful and systematic way the material from the earlier ICAIL 2017 version, I have not considered work that has appeared since then, including Rigoni (2018) and Bench-Capon and Atkinson (2017). I hope to be able to discuss this more recent work in the future.

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.University of Maryland, College ParkMarylandUSA

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