A Dynamic Logic for Information Evaluation in Intelligence

TL;DR

This paper challenges the conventional 6×6 Admiralty System by showing that credibility, not reliability, dominates intelligence evaluation and that facts-versus-interpretations are not cleanly separable in practice. It introduces L_{(intel)}, a dynamic belief revision framework based on doxastic plausibility models, in which credibility is the primary evaluative dimension and reliability updates credibility post-hoc. The framework yields a numerical, six-degree credibility scale C^1–C^6 that maps onto doctrinal labels, and couples with six reliability updates R^{A}–R^{F} to produce posterior credibility scores; it also aligns with Icard (2023a)’s 3×3 descriptive taxonomy when the scale is grouped. The approach bridges qualitative and quantitative methods and demonstrates that credibility-driven evaluation can recover descriptive message types, offering a rigorous, adaptable tool for classifying intelligence messages and guiding future information-processing tasks.

Abstract

In the field of human intelligence, officers use an alphanumeric scale, known as the Admiralty System, to rate the credibility of messages and the reliability of their sources (NATO AJP-2.1, 2016). During this evaluation, they are expected to estimate the credibility and reliability dimensions independently of each other (NATO STANAG, 2003). However, empirical results show that officers perceive these dimensions as strongly correlated (Baker et al., 1968). More precisely, they consider credibility as playing the leading role over reliability, the importance of which is only secondary (Samet, 1975). In this paper, we present a formal evaluative procedure, called L(intel), in line with these findings. We adapt dynamic belief revision to make credibility the main dimension of evaluation and introduce dynamic operators to update credibility ratings with the source's reliability. In addition to being empirically sound, we show that L(intel) provides an effective procedure to classify intelligence messages along the descriptive taxonomy presented in Icard (2023).