Developing Intelligence Theory

New Challenges and Competing Perspectives

Peter Gill, Stephen Marrin, Mark Phythian, Peter Gill, Stephen Marrin, Mark Phythian

• Publisher: Routledge
• Year: 2020
• Print ISBN: 9780367138431
• eBook ISBN: 9780429647468
• Edition: 1
• Topic: Storia
• Subtopic: Storia militare e marittima

Developing Intelligence Theory analyses the current state of intelligence theorisation, provides a guide to a range of approaches and perspectives, and points towards future research agendas in this field. Key questions discussed include the role of intelligence theory in organising the study of intelligence, how (and how far) explanations of intelligence have progressed in the last decade, and how intelligence theory should develop from here.

Significant changes have occurred in the security intelligence environment in recent years—including transformative information technologies, the advent of 'new' terrorism, and the emergence of hybrid warfare—making this an opportune moment to take stock and consider how we explain what intelligence does and how. The material made available via the 2013 Edward Snowden leaks and subsequent national debates has contributed much to our understanding of contemporary intelligence processes and has significant implications for future theorisation, for example, in relation to the concept of 'surveillance'.

The contributors are leading figures in Intelligence Studies who represent a range of different approaches to conceptual thinking about intelligence. As such, their contributions provide a clear statement of the current parameters of debates in intelligence theory, while also pointing to ways in which the study of intelligence continues to develop. This book was originally published as a special issue of Intelligence and National Security.

Theory and practice

Gregory F. Treverton

ABSTRACT

A sometime practitioner reflects on how theory might help address the challenges of doing intelligence: balancing strategic and tactical; knowing when ‘stories’ have been overtaken; understanding intelligence–policy relations in ways more subtle than ‘politicization’ adapting to big data; rethinking the canonical, and no longer helpful ‘intelligence cycle’; collaborating more with the private sector; and perhaps hardest, dealing with ‘false facts’.

Slightly over a decade ago, the RAND Corporation convened a workshop on intelligence theory for the Director of National Intelligence.¹ When we gathered some of the same experts a year ago on the same subject for the International Studies Association – the panel that is the backdrop for this volume – we agreed that most of the questions from that workshop remained open. In between those two gatherings, I had the chance to ask myself those questions from inside government, as the chair of the National Intelligence Council.

For me, the questions about theory were, and are:

• Is the subject empirical (how does intelligence work and why) or normative (how should intelligence work)?
• Can theory help us with the age-old but increasingly pressing question of how to balance tactical or immediate intelligence with more strategic work, what Willmore Kendall seventy years ago called ‘the big job – the carving out of United States destiny in the world as a whole’.²
• A related question: if intelligence is about story-telling, as I think it is, could theory help us know when those stories have been overtaken – what is usually the root of ‘intelligence failures’?
• How can theory help explain the relationship between intelligence and policy – speaking ‘truth’ to power – in a way more subtle than concerns over ‘politicization’?³
• Ditto for the impact of the age of internet, social media, and ‘big data’ on the intelligence process?
• What is the impact on intelligence process of increased involvement of private sector and (in some places) para-states?⁴
• Finally, what is the most helpful focus of theory – process, institutions, or functions?

Purpose and process

My reflections on these questions about theory from the inside begin with the first and last. And the answers are relatively easy for me. Indeed, my language in framing the questions frames my answer to the first. As a (too) frequent practitioner of intelligence inside government, my interest always was more practical, if not normative: how could theory help us do better? To that end, though, it is important to build some empirical theory: how does intelligence work, especially across different examples and countries? Since my focus always has been analysis, not operations, fewer truly normative questions arise. Those that do mostly arise from collection, especially how to deal with datasets, including ones not collected by intelligence agencies, that raise issues of privacy.

From the inside, almost everything is about process. Indeed, the challenge in managing intelligence, as other realms of public policy, is to keep your eye on what you want to accomplish, and not be consumed by the details and frustrations of process. From my perspective on the inside, a number of issues of process arose, for which comparisons if not theory would have been helpful. Of those, the most paradigmatic, if not theoretical, was the canonical notion of the ‘intelligence cycle’, as portrayed in Figure 1.

Figure 1 The canonical intelligence cycle.

Note: This, the oldest I could find, is from the U.S. Army, 1948, courtesy of Sir David Omand.

The traditional cycle begins with ‘direction’ or requirements, then to collection, then processing, then dissemination. That cycle has been criticized often, usually on the argument that things don’t really work that way.⁵ And that is true. ‘Raw’ intelligence often goes to policy officials, collection and analysis often are simultaneous, not a sequence, collection often starts before requirements are clear, and dissemination covers a host of forms and methods.

Yet from my perspective on the inside, the cycle enshrines a whole series of assumptions that are unhelpful and unwise, and for that reason I had become a fan of ‘activity-based intelligence’ (ABI) before I went to the NIC.⁶ ABI provided impressive successes in unraveling terror networks and identifying bad guys in Iraq and Afghanistan. Identifying Osama bin Laden’s driver was one of its successes. It amassed information from many sources around particular locations, then used correlations to develop ‘patterns of life’ that would distinguish potential terrorists from ordinary pious Muslim at pray. However, I was attracted as much by its spirit as its method. In contrast to the cycle, ABI doesn’t assume we know what we’re looking for. The cycle also assumes linearity. Sure, steps get skipped, but the basic model is linear, almost industrial. Even analysis had a certain industrial quality about it: a friend who was an NSA Soviet analyst recalls starting the day with a large stack of ‘her take’, the overnight SIGINT collection relevant to her account. And what is disseminated is a ‘product’, which tends to turn intelligence into a commodity.

Instead, ABI doesn’t presume linearity, but rather is ‘sequence neutral’. In intelligence, as in life, we often may solve a puzzle before we realize the puzzle was in our minds. Or in an analogy with medicine, notice how many drugs were discovered by accident when they were being used for something else entirely. The discovery was an accident, hardly the result of a linear production function.

Perhaps most important and least helpful, traditional intelligence and the cycle give pride of place to collection. Collection drives the entire process: there is nothing to analyze if nothing is collected against the requirements that have been identified. When analysis or post mortems reveal an intelligence ‘gap’, the first response is collect more to fill that gap. But the world is full of data. Any ‘collection’ is bound to be very selective and very partial. Perhaps slightly paradoxically, ABI reminds us that data isn’t really the problem. It’s everywhere, and getting more so. China has, by one estimate, one camera for every 43 people. Data is becoming ubiquitous, whether we like it or not. Privacy, in many respects in which it was traditionally conceived, is gone, or going, again for better or worse.

Worse, not only does intelligence privilege collection, it still privileges information it collects, often from its special or secret sources. For ABI, by contrast, all data is neutral, mostly facts. There are no ‘reliable sources’ – or questionable ones. Data only becomes ‘good’ or ‘bad’ when it correlates with other data to produce a target’s location or unravel his network. This neutrality of data is, not surprisingly, perhaps the most uncomfortable challenge of ABI to the traditional paradigm of intelligence, for so much of the cycle has been built around precisely the opposite – evaluating information sources for their reliability. The cycle may have made a certain sense during the Cold War, with one over-arching but secretive foe. It makes no sense now.

Balancing strategic and tactical⁷

This is an enduring challenge; hand-wringing about the primacy of the urgent over the important has characterized all of my years as a student, consumer and sometime practitioner of intelligence. At the NIC, it was on my mind every hour of every working day. It is made worse by the shapelessness of the current world, which means that every crisis has to be approached afresh on its own terms, and, especially, by the nation’s hyper-sensitivity to the terrorism threat. That threat to the United States homeland remains minimal, but that is hardly the way it is perceived by the public – or characterized by politicians. Between 9/11 and 2017, 94 Americans died at the hands of jihadist terrorists; all 12 perpetrators were American citizens or in the U.S. legally.⁸ From my perch at the NIC, I understood the politics but was dismayed by how the acute sensitivity deformed our work. When we looked at Nigeria, there was not much Nigeria: it was Boko Haram. And even when we looked at Boko Haram, there was not much Boko Haram: it was all deciphering networks and targeting bad guys. We all wondered and worried, where do these people come from, and why are they doing what they’re doing? We did what we could at the NIC trying to understand root causes and motivations. But we were only scratching the surface.

In 2016, the NIC produced about 700 pieces of paper, and more than half of those were memorandums from a National Intelligence Officer to the National Security Adviser, her deputy, or another senior National Security Council official. They came directly from the deliberations of the two main policymaking bodies in the administration – the Principals Committee, the relevant cabinet secretaries, or, especially, the Deputies Committee, their deputies and the focal point for assessing options and teeing up decisions. Not all those were purely tactical. Some were the ‘what ifs?’ of the sort that should be the woof and warp of intelligence–policy relations: ‘if we do x, how will Putin respond?’ Because we were at all the policy meetings, we knew what was going on. But my task, every day, was to find time and capacity not just to answer the questions policy officials asked but also to answer the more strategic ones they weren’t asking.

Plainly, no theory could solve the dilemma of how to balance strategic and tactical intelligence. As my boss, Jim Clapper, the Director of National Intelligence, put it: we can’t ignore the demand signal. We weren’t about to tell Susan Rice, the National Security Advisor, that we weren’t going to do a piece she had asked for because we were at work on something more strategic. But if theory starts with what intelligence is, for me, the starting point is strategic analysis. When Harry Truman set up the strategic intelligence function at the end of World War II, he understood that the United States had been thrust into a global role for which it was not prepared.⁹ But he could have anticipated neither how fully and nor how long this new global role would be dominated by the Cold War, which lent a sharp focus – perhaps too sharp – to strategic analysis.

Now, the need for strategic analysis – Kendall’s ‘big job’ – is at least as great as in Truman’s time. Then, the world was hardening into a very definite shape; now it is very shapeless and often frightening for that fact. Then, the fact that the United States would lead was almost a given, and the question was how. Now, the question is not just how the United States will lead, but how much, or even whether it will lead.

Building – and adjusting – ‘stories’ in a shapeless word

This is a kin of the strategic/tactical challenge. I have come to think that intelligence is ultimately about telling stories, and most ‘intelligence – or warning – failures’ derive from holding onto stories that events have outmoded. A story from another realm, Ebola, drives the point home. The medical community had a ‘story’ about Ebola: because death was quick, its period of contagion was brief, thus it would flare up and die out in remote regions. Trouble was that much better transit from rural areas to urban had overtaken the story.

The shapelessness of the world both confounds and demands strategic analysis. If intelligence is story-telling, many of our current stories are suspiciously long in the tooth. In policy terms, for instance, we have been telling ourselves the same story about North Korea for a generation: with just the right combination of carrots and sticks, primarily the la...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Contents
• Citation Information
• Notes on Contributors
• Introduction: Developing intelligence theory
• 1 Theory and practice
• 2 Evaluating intelligence theories: current state of play
• 3 Intelligence in the Socratic philosophers
• 4 Intelligence and the liberal conscience
• 5 Intelligence is as intelligence does
• 6 Intelligence theory from the margins: questions ignored and debates not had
• 7 ‘Quo Vadis?’ A comparatist meets a theorist searching for a grand theory of intelligence
• 8 A theoretical reframing of the intelligence–policy relation
• 9 When everything becomes intelligence: machine learning and the connected world
• 10 The way ahead in explaining intelligence organization and process
• Index