Taming Complexity: Rationality, the Law of Evidence, and the Nature of the Legal System

This essay explores the implications of complexity for understanding both the law of evidence and the nature of the legal system. Among the propositions critically analyzed is that one significant way to understand the general problem of the meaning of rationality is that it has involved a multivariate search for tools to understand and regulate a hostile environment. The law of evidence is conceptualized as a subset of this effort, at least in part, as involving a search for tools to regulate the almost infinitely complex domain of potentially relevant evidence and at the same time to accommodate policy demands. The proposition is then considered that the legal system of which the evidentiary system is a part has emergent properties that may not be deducible from its component parts and that suggest that it may be, or at least has properties highly analogous to, a complex adaptive system. One implication of this analysis is that the tools of standard academic research that rely heavily on the isolation and reduction of analytical problems to manageable units to permit them to be subjected to standard deductive methodologies may need to be supplemented with analytical tools that facilitate the regulation of complex natural phenomena such as fluid dynamics. This has direct implications for such things as the conception of law as rules, and thus for the Hart/Dworkin debate that has dominated jurisprudence for 50 years. That debate may have mis-characterized the object of its inquiry, and thus the Dworkinian solution to the difficulties of positivism is inapplicable. Even if that is wrong, it can be shown that the Dworkinian solution is not achievable and cannot rationally be approximated. Solutions to legal problems within the legal system as a whole (as compared to any particular node within the legal system) are arrived at through a process of inference to the best explanation that occurs within a highly interconnected set of nodes that has similarities to a neural network.