Quantitative Approaches to Representing the Value of Information within the Intelligence Cycle

Quantitative Approaches to Representing the Value of Information within the Intelligence Cycle

Christopher M. Smith (TRADOC Analysis Center, Monterey, CA, USA), William T. Scherer (Department of Systems and Information Engineering, University of Virginia Charlottesville, VA, USA), Andrew Todd (Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA, USA) and Daniel T. Maxwell (KaDSci, LLC, Springfield, VA, USA)
Copyright: © 2015 |Pages: 21
DOI: 10.4018/IJSDS.2015100101
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Abstract

The authors propose that valuation of information metrics developed near the end of the intelligence cycle are appropriate supplemental metrics for national security intelligence. Existing information and decision theoretic frameworks are often either inapplicable in the context of national security intelligence or they capture affects from inputs aside from just the information or intelligence. Applied information theory looks at the syntactic transmission of information rather than assigning it a quantitative value. Information economics determines the market value of information, which is also inapplicable in a national security intelligence context. Decision analysis can use the value of information to show the expected value of perfect information (EVPI) and the expected value of imperfect information (EVII) and although this method can be used with utility theory and not just monetary objectives, it has been shown that decision makers within the intelligence community (IC) have difficulty agreeing upon how to value objectives within analysis. Additionally, it is difficult to determine how decision makers use intelligence in the decision-making process, which makes existing decision theoretic methods problematic, and might include inputs from variables besides just the intelligence.
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1. Introduction

The U.S. Intelligence Community (IC) harvests over a billion pieces of data a day, but often lacks the ability to analyze that data and produce valuable intelligence (McConnell, 2007). Efforts to improve intelligence often focus on collection rather than analysis. According to the 9-11 Commission’s report, the intelligence community had information that may have either helped decision makers mitigate or even stop the 9-11 attack. Even though the IC had enough information to determine that “the system was blinking red,” according to a Central Intelligence Agency (CIA) supervisor, “no one looked at the bigger picture; no analytic work foresaw the lightning that could connect the thundercloud to the ground” (9/11 Commission Report, 2004).

Collection management within the IC is a practice that focuses on knowing the needs of the intelligence users and determining how best to allocate limited collection resources (Clark, 2011). Collection managers also review existing allocation strategies by determining if intelligence collected is valuable and relevant. Existing methods for evaluating intelligence, however, have focused on their value to the decision maker.

Since decisions, especially at the national level, are subject to conflicting priorities and politics, it is difficult to determine what role intelligence plays in decisions. The current value of information methods, however, rely crucially on whether or not information is used in a decision and since collection managers can never truly determine how intelligence is used, existing methods are often inapplicable (see Figure 1).

Figure 1.

Intelligence collection management within the intelligence cycle

Collection managers in the IC will benefit from an assessment methodology that allows a quantitative measure of the impact certain intelligence has on an analysis. A robust method of this type will allow managers to objectively measure “good” or effective intelligence vs. “bad” or ineffective intelligence. A quantitative measure applied across the different classes of analyses would provide a relative scoring of the intelligence that different sources produce thereby allowing collection managers to more effectively manage expensive collection resources.

The structure of the paper is as follows. The first section introduces the concept of valuing intelligence within the intelligence cycle. The second section defines national security intelligence for the purposes of this paper. The third section describes the variety of methods or theories currently used to “value” information both in the government and in private sector and also outlines why these methods fall short when applied to national security intelligence. The fourth section discusses research on how the valuation of intelligence has been attempted to date and their limitations. The fifth section makes the suggestion of two possibilities for measuring the supplemental value of intelligence within the intelligence cycle. Finally, in the sixth section we offer a conclusion and suggestions for future work.

2. Definition Of Intelligence

A useful definition for intelligence given by Jennifer Sims is “the collection, analysis, and dissemination of information for decision-makers engaged in a competitive enterprise” (Sims, 2008). Specifically for the purposes of this paper, we will be discussing strategic level intelligence, but we feel that this work will be effective at all levels of decision making using intelligence. Understanding the definition of intelligence is the first step in measuring of it. Although various authors have put forth a number of definitions of intelligence, one of the main distinctions of the various definitions, and most important to the measurement of intelligence, is that some authors suggest that covert actions taken by the government should be included in the definition of intelligence (Godson, 2001; Warner, 2008; Betts, 2008). This would turn the term intelligence into something that not only described information, but also described actions taken by the government. The definition of intelligence for this paper will not include covert action.

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