Reason-driven decision making in the US law enforcement context 1

• Published in: Police journal (Chichester) Vol. 90; no. 4; pp. 289 - 317
• Main Authors: Loomis, David, Loomis, Steven
• Format: Journal Article
• Language: English
• Published: 01.12.2017
• ISSN: 0032-258X; 1740-5599
• DOI: 10.1177/0032258X16678422

This paper argues that the complex good of justice in the free and civil society requires that policing actively seek optimal information conditions for decision making. Optimal information conditions in policing include two broad categories of information: explicit and tacit. The aim of information optimality is best achieved when the varieties of decision making are reason-based and normatively ethical, which place limits against radical imbalances between explicit and tacit information. The authors’ methods are interdisciplinary, using two approaches from philosophy (ontology of institutions, regulative epistemology) and two from economics (information and institutional economics) in order to examine the information environment of US law enforcement. Our general method is theoretical, deductive and non-technical, which allows us to raise a number of important questions and develop hypotheses for future research. Among the hypotheses revealed are that (i) institutional scale and integration appear to be decreasing the price of explicit information, while raising costs against tacit information; (ii) this cost differential has led or will lead to information loss, thus hindering reasoned, ethical decision making in the LE context; (iii) perhaps the most serious consequence of a decoupling of data from reason is the threat of redefining reason on terms internal not external to the data; and (iv) an information disequilibrium threatens to disrupt law enforcement’s mission and role to ensure an ethically robust, objective form of justice. The authors conclude with a set of reason-based principles that may assist LE researchers, leaders and practitioners from falling into the traps of information division and loss posed by the various data-driven movements.