Category: Psychology

Psychology of Intelligence Analysis by Richards J. Heuer, Jr.

Introduction

Improving Intelligence Analysis at CIA: Dick Heuer’s Contribution to Intelligence Analysis

By

Jack Davis served with the Directorate of Intelligence (DI), the National Intelligence Council, and the Office of Training during his CIA career.

Dick Heuer’s ideas on how to improve analysis focus on helping analysts compensate for the human mind’s limitations in dealing with complex problems that typically involve ambiguous information, multiple players, and fluid circumstances. Such multi-faceted estimative challenges have proliferated in the turbulent post-Cold War world.

Leading Contributors to Quality of Analysis

Intelligence analysts, in seeking to make sound judgments, are always under challenge from the complexities of the issues they address and from the demands made on them for timeliness and volume of production. Four Agency individuals over the decades stand out for having made major contributions on how to deal with these challenges to the quality of analysis.

My short list of the people who have had the greatest positive impact on CIA analysis consists of Sherman Kent, Robert Gates, Douglas MacEachin, and Richards Heuer.

Sherman Kent

Sherman Kent’s pathbreaking contributions to analysis cannot be done justice in a couple of paragraphs

Kent’s greatest contribution to the quality of analysis was to define an honorable place for the analyst–the thoughtful individual “applying the instruments of reason and the scientific method”–in an intelligence world then as now dominated by collectors and operators.

In a second (1965) edition of Strategic Intelligence, Kent took account of the coming computer age as well as human and technical collectors in proclaiming the centrality of the analyst:

Whatever the complexities of the puzzles we strive to solve and whatever the sophisticated techniques we may use to collect the pieces and store them, there can never be a time when the thoughtful man can be supplanted as the intelligence device supreme.

More specifically, Kent advocated application of the techniques of “scientific” study of the past to analysis of complex ongoing situations and estimates of likely future events. Just as rigorous “impartial” analysis could cut through the gaps and ambiguities of information on events long past and point to the most probable explanation, he contended, the powers of the critical mind could turn to events that had not yet transpired to determine the most probable developments

To this end, Kent developed the concept of the analytic pyramid, featuring a wide base of factual information and sides comprised of sound assumptions, which pointed to the most likely future scenario at the apex.

Robert Gates

Bob Gates served as Deputy Director of Central Intelligence (1986-1989) and as DCI (1991-1993). But his greatest impact on the quality of CIA analysis came during his 1982-1986 stint as Deputy Director for Intelligence (DDI).

Gates’s ideas for overcoming what he saw as insular, flabby, and incoherent argumentation featured the importance of distinguishing between what analysts know and what they believe–that is, to make clear what is “fact” (or reliably reported information) and what is the analyst’s opinion (which had to be persuasively supported with evidence). Among his other tenets were the need to seek the views of non-CIA experts, including academic specialists and policy officials, and to present alternate future scenarios.

Using his authority as DDI, he reviewed critically almost all in-depth assessments and current intelligence articles prior to publication. With help from his deputy and two rotating assistants from the ranks of rising junior managers, Gates raised the standards for DDI review dramatically–in essence, from “looks good to me” to “show me your evidence.”

As the many drafts Gates rejected were sent back to managers who had approved them–accompanied by the DDI’s comments about inconsistency, lack of clarity, substantive bias, and poorly supported judgments–the whole chain of review became much more rigorous. Analysts and their managers raised their standards to avoid the pain of DDI rejection. Both career advancement and ego were at stake.

The rapid and sharp increase in attention paid by analysts and managers to the underpinnings for their substantive judgments probably was without precedent in the Agency’s history. The longer term benefits of the intensified review process were more limited, however, because insufficient attention was given to clarifying tradecraft practices that would promote analytic soundness. More than one participant in the process observed that a lack of guidelines for meeting Gates’s standards led to a large amount of “wheel-spinning.”

Douglas MacEachin

Doug MacEachin, DDI from 1993 to 1996, sought to provide an essential ingredient for ensuring implementation of sound analytic standards: corporate tradecraft standards for analysts. This new tradecraft was aimed in particular at ensuring that sufficient attention would be paid to cognitive challenges in assessing complex issues.

MacEachin’s university major was economics, but he also showed great interest in philosophy. His Agency career–like Gates’–included an extended assignment to a policymaking office. He came away from this experience with new insights on what constitutes “value-added” intelligence usable by policymakers. Subsequently, as CIA’s senior manager on arms control issues, he dealt regularly with a cadre of tough- minded policy officials who let him know in blunt terms what worked as effective policy support and what did not.

MacEachin advocated an approach to structured argumentation called “linchpin analysis,” to which he contributed muscular terms designed to overcome many CIA professionals’ distaste for academic nomenclature. The standard academic term “key variables” became drivers. “Hypotheses” concerning drivers became linchpins— assumptions underlying the argument–and these had to be explicitly spelled out. MacEachin also urged that greater attention be paid to analytical processes for alerting policymakers to changes in circumstances that would increase the likelihood of alternative scenarios.

MacEachin thus worked to put in place systematic and transparent standards for determining whether analysts had met their responsibilities for critical thinking. To spread understanding and application of the standards, he mandated creation of workshops on linchpin analysis for managers and production of a series of notes on analytical tradecraft. He also directed that the DI’s performance on tradecraft standards be tracked and that recognition be given to exemplary assessments. Perhaps most ambitious, he saw to it that instruction on standards for analysis was incorporated into a new training course, “Tradecraft 2000.” Nearly all DI managers and analysts attended this course during 1996-97.

Richards Heuer

Dick Heuer was–and is–much less well known within the CIA than Kent, Gates, and MacEachin. He has not received the wide acclaim that Kent enjoyed as the father of professional analysis, and he has lacked the bureaucratic powers that Gates and MacEachin could wield as DDIs. But his impact on the quality of Agency analysis arguably has been at least as important as theirs.

Heuer’s Central Ideas

Dick Heuer’s writings make three fundamental points about the cognitive challenges intelligence analysts face:

• The mind is poorly “wired” to deal effectively with both inherent uncertainty (the natural fog surrounding complex, indeterminate intelligence issues) and induced uncertainty (the man-made fog fabricated by denial and deception operations).
• Even increased awareness of cognitive and other “unmotivated” biases, such as the tendency to see information confirming an already-held judgment more vividly than one sees “disconfirming” information, does little by itself to help analysts deal effectively with uncertainty.
• Tools and techniques that gear the analyst’s mind to apply higher levels of critical thinking can substantially improve analysis on complex issues on which information is incomplete, ambiguous, and often deliberately distorted. Key examples of such intellectual devices include techniques for structuring information, challenging assumptions, and exploring alternative interpretations.

Given the difficulties inherent in the human processing of complex information, a prudent management system should:

• Encourage products that (a) clearly delineate their assumptions and chains of inference and (b) specify the degree and source of the uncertainty involved in the conclusions.
• Emphasize procedures that expose and elaborate alternative points of view–analytic debates, devil’s advocates, interdisciplinary brainstorming, competitive analysis, intra-office peer review of production, and elicitation of outside expertise.

Heuer emphasizes both the value and the dangers of mental models, or mind-sets. In the book’s opening chapter, entitled “Thinking About Thinking,” he notes that:

[Analysts] construct their own version of “reality” on the basis of information provided by the senses, but this sensory input is mediated by complex mental processes that determine which information is attended to, how it is organized, and the meaning attributed to it. What people perceive, how readily they perceive it, and how they process this information after receiving it are all strongly influenced by past experience, education, cultural values, role requirements, and organizational norms, as well as by the specifics of the information received.

This process may be visualized as perceiving the world through a lens or screen that channels and focuses and thereby may distort the images that are seen. To achieve the clearest possible image…analysts need more than information…They also need to understand the lenses through which this information passes. These lenses are known by many terms–mental models, mind-sets, biases, or analytic assumptions.

In essence, Heuer sees reliance on mental models to simplify and interpret reality as an unavoidable conceptual mechanism for intelligence analysts–often useful, but at times hazardous. What is required of analysts, in his view, is a commitment to challenge, refine, and challenge again their own working mental models, precisely because these steps are central to sound interpretation of complex and ambiguous issues.

Throughout the book, Heuer is critical of the orthodox prescription of “more and better information” to remedy unsatisfactory analytic performance. He urges that greater attention be paid instead to more intensive exploitation of information already on hand, and that in so doing, analysts continuously challenge and revise their mental models.

Heuer sees mirror-imaging as an example of an unavoidable cognitive trap. No matter how much expertise an analyst applies to interpreting the value systems of foreign entities, when the hard evidence runs out the tendency to project the analyst’s own mind-set takes over. In Chapter 4, Heuer observes:

To see the options faced by foreign leaders as these leaders see them, one must understand their values and assumptions and even their misperceptions and misunderstandings. Without such insight, interpreting foreign leaders’ decisions or forecasting future decisions is often nothing more than partially informed speculation. Too frequently, foreign behavior appears “irrational” or “not in their own best interest.” Such conclusions often indicate analysts have projected American values and conceptual frameworks onto the foreign leaders and societies, rather than understanding the logic of the situation as it appears to them.

Recommendations

Heuer’s advice to Agency leaders, managers, and analysts is pointed: To ensure sustained improvement in assessing complex issues, analysis must be treated as more than a substantive and organizational process. Attention also must be paid to techniques and tools for coping with the inherent limitations on analysts’ mental machinery. He urges that Agency leaders take steps to:

• Establish an organizational environment that promotes and rewards the kind of critical thinking he advocates–or example, analysis on difficult issues that considers in depth a series of plausible hypotheses rather than allowing the first credible hypothesis to suffice.
• Expand funding for research on the role such mental processes play in shaping analytical judgments. An Agency that relies on sharp cognitive performance by its analysts must stay abreast of studies on how the mind works–i.e., on how analysts reach judgments.
• Foster development of tools to assist analysts in assessing information. On tough issues, they need help in improving their mental models and in deriving incisive findings from information they already have; they need such help at least as much as they need more information.

I offer some concluding observations and recommendations, rooted in Heuer’s findings and taking into account the tough tradeoffs facing intelligence professionals:

•  Commit to a uniform set of tradecraft standards based on the insights in this book. Leaders need to know if analysts have done their cognitive homework before taking corporate responsibility for their judgments. Although every analytical issue can be seen as one of a kind, I suspect that nearly all such topics fit into about a dozen recurring patterns of challenge based largely on variations in substantive uncertainty and policy sensitivity. Corporate standards need to be established for each such category. And the burden should be put on managers to explain why a given analytical assignment requires deviation from the standards. I am convinced that if tradecraft standards are made uniform and transparent, the time saved by curtailing personalistic review of quick-turnaround analysis (e.g., “It reads better to me this way”) could be “re-invested” in doing battle more effectively against cognitive pitfalls. (“Regarding point 3, let’s talk about your assumptions.”)
•  Pay more honor to “doubt.” Intelligence leaders and policymakers should, in recognition of the cognitive impediments to sound analysis, establish ground rules that enable analysts, after doing their best to clarify an issue, to express doubts more openly. They should be encouraged to list gaps in information and other obstacles to confident judgment. Such conclusions as “We do not know” or “There are several potentially valid ways to assess this issue” should be regarded as badges of sound analysis, not as dereliction of analytic duty.

Find a couple of successors to Dick Heuer. Fund their research. Heed their findings.

PART ONE–OUR MENTAL MACHINERY Chapter 1
Thinking About Thinking

Of the diverse problems that impede accurate intelligence analysis, those inherent in human mental processes are surely among the most important and most difficult to deal with. Intelligence analysis is fundamentally a mental process, but understanding this process is hindered by the lack of conscious awareness of the workings of our own minds.

A basic finding of cognitive psychology is that people have no conscious experience of most of what happens in the human mind. Many functions associated with perception, memory, and information processing are conducted prior to and independently of any conscious direction. What appears spontaneously in consciousness is the result of thinking, not the process of thinking.

Weaknesses and biases inherent in human thinking processes can be demonstrated through carefully designed experiments. They can be alleviated by conscious application of tools and techniques that should be in the analytical tradecraft toolkit of all intelligence analysts.

Thinking analytically is a skill like carpentry or driving a car. It can be taught, it can be learned, and it can improve with practice. But like many other skills, such as riding a bike, it is not learned by sitting in a classroom and being told how to do it. Analysts learn by doing. Most people achieve at least a minimally acceptable level of analytical performance with little conscious effort beyond completing their education. With much effort and hard work, however, analysts can achieve a level of excellence beyond what comes naturally.

expert guidance may be required to modify long-established analytical habits to achieve an optimal level of analytical excellence. An analytical coaching staff to help young analysts hone their analytical tradecraft would be a valuable supplement to classroom instruction.

One key to successful learning is motivation. Some of CIA’s best analysts developed their skills as a consequence of experiencing analytical failure early in their careers. Failure motivated them to be more self-conscious about how they do analysis and to sharpen their thinking process.

Part I identifies some limitations inherent in human mental processes. Part II discusses analytical tradecraft–simple tools and approaches for overcoming these limitations and thinking more systematically. Chapter 8, “Analysis of Competing Hypotheses,” is arguably the most important single chapter. Part III presents information about cognitive biases–the technical term for predictable mental errors caused by simplified information processing strategies. A final chapter presents a checklist for analysts and recommendations for how managers of intelligence analysis can help create an environment in which analytical excellence flourishes.

Herbert Simon first advanced the concept of “bounded” or limited rationality.

Because of limits in human mental capacity, he argued, the mind cannot cope directly with the complexity of the world. Rather, we construct a simplified mental model of reality and then work with this model. We behave rationally within the confines of our mental model, but this model is not always well adapted to the requirements of the real world. The concept of bounded rationality has come to be recognized widely, though not universally, both as an accurate portrayal of human judgment and choice and as a sensible adjustment to the limitations inherent in how the human mind functions.

Much psychological research on perception, memory, attention span, and reasoning capacity documents the limitations in our “mental machinery” identified by Simon.

Many scholars have applied these psychological insights to the study of international political behavior. A similar psychological perspective underlies some writings on intelligence failure and strategic surprise.

This book differs from those works in two respects. It analyzes problems from the perspective of intelligence analysts rather than policymakers. And it documents the impact of mental processes largely through experiments in cognitive psychology rather than through examples from diplomatic and military history.

A central focus of this book is to illuminate the role of the observer in determining what is observed and how it is interpreted. People construct their own version of “reality” on the basis of information provided by the senses, but this sensory input is mediated by complex mental processes that determine which information is attended to, how it is organized, and the meaning attributed to it. What people perceive, how readily they perceive it, and how they process this information after receiving it are all strongly influenced by past experience, education, cultural values, role requirements, and organizational norms, as well as by the specifics of the information received.

In this book, the terms mental model and mind-set are used more or less interchangeably, although a mental model is likely to be better developed and articulated than a mind-set. An analytical assumption is one part of a mental model or mind-set. The biases discussed in this book result from how the mind works and are independent of any substantive mental model or mind-set.

Intelligence analysts must understand themselves before they can understand others. Training is needed to (a) increase self-awareness concerning generic problems in how people perceive and make analytical judgments concerning foreign events, and (b) provide guidance and practice in overcoming these problems.

The disadvantage of a mind-set is that it can color and control our perception to the extent that an experienced specialist may be among the last to see what is really happening when events take a new and unexpected turn. When faced with a major paradigm shift, analysts who know the most about a subject have the most to unlearn.

The advantage of mind-sets is that they help analysts get the production out on time and keep things going effectively between those watershed events that become chapter headings in the history books.

What analysts need is more truly useful information–mostly reliable HUMINT from knowledgeable insiders–to help them make good decisions. Or they need a more accurate mental model and better analytical tools to help them sort through, make sense of, and get the most out of the available ambiguous and conflicting information.

Psychological research also offers to intelligence analysts additional insights that are beyond the scope of this book. Problems are not limited to how analysts perceive and process information. Intelligence analysts often work in small groups and always within the context of a large, bureaucratic organization. Problems are inherent in the processes that occur at all three levels–individual, small group, and organization. This book focuses on problems inherent in analysts’ mental processes, inasmuch as these are probably the most insidious. Analysts can observe and get a feel for these problems in small-group and organizational processes, but it is very difficult, at best, to be self-conscious about the workings of one’s own mind.

Chapter 2

Perception: Why Can’t We See What Is There To Be Seen?

The process of perception links people to their environment and is critical to accurate understanding of the world about us. Accurate intelligence analysis obviously requires accurate perception. Yet research into human perception demonstrates that the process is beset by many pitfalls. Moreover, the circumstances under which intelligence analysis is conducted are precisely the circumstances in which accurate perception tends to be most difficult. This chapter discusses perception in general, then applies this information to illuminate some of the difficulties of intelligence analysis.

We tend to perceive what we expect to perceive.

A corollary of this principle is that it takes more information, and more unambiguous information, to recognize an unexpected phenomenon than an expected one.

patterns of expectation become so deeply embedded that they continue to influence perceptions even when people are alerted to and try to take account of the existence of data that do not fit their preconceptions. Trying to be objective does not ensure accurate perception.

Patterns of expectations tell analysts, subconsciously, what to look for, what is important, and how to interpret what is seen. These patterns form a mind-set that predisposes analysts to think in certain ways. A mind-set is akin to a screen or lens through which one perceives the world.

There is a tendency to think of a mind-set as something bad, to be avoided. According to this line of argument, one should have an open mind and be influenced only by the facts rather than by preconceived notions! That is an unreachable ideal. There is no such thing as “the facts of the case.” There is only a very selective subset of the overall mass of data to which one has been subjected that one takes as facts and judges to be relevant to the question at issue.

Actually, mind-sets are neither good nor bad; they are unavoidable. People have no conceivable way of coping with the volume of stimuli that impinge upon their senses, or with the volume and complexity of the data they have to analyze, without some kind of simplifying preconceptions about what to expect, what is important, and what is related to what. “There is a grain of truth in the otherwise pernicious maxim that an open mind is an empty mind.” Analysts do no achieve objective analysis by avoiding preconceptions; that would be ignorance or self-delusion. Objectivity is achieved by making basic assumptions and reasoning as explicit as possible so that they can be challenged by others and analysts can, themselves, examine their validity.

One of the most important characteristics of mind-sets is: Mind-sets tend to be quick to form but resistant to change.

Once an observer has formed an image–that is, once he or she has developed a mind- set or expectation concerning the phenomenon being observed–this conditions future perceptions of that phenomenon.

This is the basis for another general principle of perception: New information is assimilated to existing images.

This principle explains why gradual, evolutionary change often goes unnoticed. It also explains the phenomenon that an intelligence analyst assigned to work on a topic or country for the first time may generate accurate insights that have been overlooked by experienced analysts who have worked on the same problem for 10 years. A fresh perspective is sometimes useful; past experience can handicap as well as aid analysis.

This tendency to assimilate new data into pre-existing images is greater “the more ambiguous the information, the more confident the actor is of the validity of his image, and the greater his commitment to the established view.”

One of the more difficult mental feats is to take a familiar body of data and reorganize it visually or mentally to perceive it from a different perspective. Yet this is what intelligence analysts are constantly required to do. In order to understand international interactions, analysts must understand the situation as it appears to each of the opposing forces, and constantly shift back and forth from one perspective to the other as they try to fathom how each side interprets an ongoing series of interactions. Trying to perceive an adversary’s interpretations of international events, as well as US interpretations of those same events, is comparable to seeing both the old and young woman in Figure 3.

A related point concerns the impact of substandard conditions of perception. The basic principle is:

Initial exposure to blurred or ambiguous stimuli interferes with accurate perception even after more and better information becomes available.

What happened in this experiment is what presumably happens in real life; despite ambiguous stimuli, people form some sort of tentative hypothesis about what they see. The longer they are exposed to this blurred image, the greater confidence they develop in this initial and perhaps erroneous impression, so the greater the impact this initial impression has on subsequent perceptions. For a time, as the picture becomes clearer, there is no obvious contradiction; the new data are assimilated into the previous image, and the initial interpretation is maintained until the contradiction becomes so obvious that it forces itself upon our consciousness.

The early but incorrect impression tends to persist because the amount of information necessary to invalidate a hypothesis is considerably greater than the amount of information required to make an initial interpretation. The problem is not that there is any inherent difficulty in grasping new perceptions or new ideas, but that established perceptions are so difficult to change. People form impressions on the basis of very little information, but once formed, they do not reject or change them unless they obtain rather solid evidence. Analysts might seek to limit the adverse impact of this tendency by suspending judgment for as long as possible as new information is being received.

Implications for Intelligence Analysis

Comprehending the nature of perception has significant implications for understanding the nature and limitations of intelligence analysis. The circumstances under which accurate perception is most difficult are exactly the circumstances under which intelligence analysis is generally conducted–dealing with highly ambiguous situations on the basis of information that is processed incrementally under pressure for early judgment. This is a recipe for inaccurate perception.

Intelligence seeks to illuminate the unknown. Almost by definition, intelligence analysis deals with highly ambiguous situations. As previously noted, the greater the ambiguity of the stimuli, the greater the impact of expectations and pre-existing images on the perception of that stimuli. Thus, despite maximum striving for objectivity, the intelligence analyst’s own preconceptions are likely to exert a greater impact on the analytical product than in other fields where an analyst is working with less ambiguous and less discordant information.

Moreover, the intelligence analyst is among the first to look at new problems at an early stage when the evidence is very fuzzy indeed. The analyst then follows a problem as additional increments of evidence are received and the picture gradually clarifies–as happened with test subjects in the experiment demonstrating that initial exposure to blurred stimuli interferes with accurate perception even after more and better information becomes available. If the results of this experiment can be generalized to apply to intelligence analysts, the experiment suggests that an analyst who starts observing a potential problem situation at an early and unclear stage is at a disadvantage as compared with others, such as policymakers, whose first exposure may come at a later stage when more and better information is available.

The receipt of information in small increments over time also facilitates assimilation of this information into the analyst’s existing views. No one item of information may be sufficient to prompt the analyst to change a previous view. The cumulative message inherent in many pieces of information may be significant but is attenuated when this information is not examined as a whole. The Intelligence Community’s review of its performance before the 1973 Arab-Israeli War noted:

The problem of incremental analysis–especially as it applies to the current intelligence process–was also at work in the period preceding hostilities. Analysts, according to their own accounts, were often proceeding on the basis of the day’s take, hastily comparing it with material received the previous day. They then produced in ‘assembly line fashion’ items which may have reflected perceptive intuition but which [did not] accrue from a systematic consideration of an accumulated body of integrated evidence.

And finally, the intelligence analyst operates in an environment that exerts strong pressures for what psychologists call premature closure. Customer demand for interpretive analysis is greatest within two or three days after an event occurs. The system requires the intelligence analyst to come up with an almost instant diagnosis before sufficient hard information, and the broader background information that may be needed to gain perspective, become available to make possible a well-grounded judgment. This diagnosis can only be based upon the analyst’s preconceptions concerning how and why events normally transpire in a given society.

The problems outlined here have implications for the management as well as the conduct of analysis. Given the difficulties inherent in the human processing of complex information, a prudent management system should:

• Encourage products that clearly delineate their assumptions and chains of inference and that specify the degree and source of uncertainty involved in the conclusions.
• Support analyses that periodically re-examine key problems from the ground up in order to avoid the pitfalls of the incremental approach.
• Emphasize procedures that expose and elaborate alternative points of view.
• Educate consumers about the limitations as well as the capabilities of intelligence analysis; define a set of realistic expectations as a standard against which to judge analytical performance.

Chapter 3
Memory: How Do We Remember What We Know?

Differences between stronger and weaker analytical performance are attributable in large measure to differences in the organization of data and experience in analysts’ long-term memory. The contents of memory form a continuous input into the analytical process, and anything that influences what information is remembered or retrieved from memory also influences the outcome of analysis.

This chapter discusses the capabilities and limitations of several components of the memory system. Sensory information storage and short-term memory are beset by severe limitations of capacity, while long-term memory, for all practical purposes, has a virtually infinite capacity. With long-term memory, the problems concern getting information into it and retrieving information once it is there, not physical limits on the amount of information that may be stored. Understanding how memory works provides insight into several analytical strengths and weaknesses.

Components of the Memory System

What is commonly called memory is not a single, simple function. It is an extraordinarily complex system of diverse components and processes. There are at least three, and very likely more, distinct memory processes. The most important from the standpoint of this discussion and best documented by scientific research are sensory information storage (SIS), short-term memory (STM), and long-term memory (LTM). Each differs with respect to function, the form of information held, the length of time information is retained, and the amount of information-handling capacity. Memory researchers also posit the existence of an interpretive mechanism and an overall memory monitor or control mechanism that guides interaction among various elements of the memory system.

Sensory Information Storage

Sensory information storage holds sensory images for several tenths of a second after they are received by the sensory organs. The functioning of SIS may be observed if you close your eyes, then open and close them again as rapidly as possible.

Short-Term Memory

Information passes from SIS into short-term memory, where again it is held for only a short period of time–a few seconds or minutes. Whereas SIS holds the complete image, STM stores only the interpretation of the image. If a sentence is spoken, SIS retains the sounds, while STM holds the words formed by these sounds.

Retrieval of information from STM is direct and immediate because the information has never left the conscious mind. Information can be maintained in STM indefinitely by a process of “rehearsal”–repeating it over and over again. But while rehearsing some items to retain them in STM, people cannot simultaneously add new items.

Long-Term Memory

Some information retained in STM is processed into long-term memory. This information on past experiences is filed away in the recesses of the mind and must be retrieved before it can be used. In contrast to the immediate recall of current experience from STM, retrieval of information from LTM is indirect and sometimes laborious.

Loss of detail as sensory stimuli are interpreted and passed from SIS into STM and then into LTM is the basis for the phenomenon of selective perception discussed in the previous chapter. It imposes limits on subsequent stages of analysis, inasmuch as the lost data can never be retrieved. People can never take their mind back to what was actually there in sensory information storage or short-term memory. They can only retrieve their interpretation of what they thought was there as stored in LTM.

There are no practical limits to the amount of information that may be stored in LTM. The limitations of LTM are the difficulty of processing information into it and retrieving information from it.

Despite much research on memory, little agreement exists on many critical points. What is presented here is probably the lowest common denominator on which most researchers would agree.

Organization of Information in Long-Term Memory.

analysts’ needs are best served by a very simple image of the structure of memory.

Imagine memory as a massive, multidimensional spider web. This image captures what is, for the purposes of this book, perhaps the most important property of information stored in memory–its interconnectedness. One thought leads to another. It is possible to start at any one point in memory and follow a perhaps labyrinthine path to reach any other point. Information is retrieved by tracing through the network of interconnections to the place where it is stored.

Retrievability is influenced by the number of locations in which information is stored and the number and strength of pathways from this information to other concepts that might be activated by incoming information. The more frequently a path is followed, the stronger that path becomes and the more readily available the information located along that path. If one has not thought of a subject for some time, it may be difficult to recall details. After thinking our way back into the appropriate context and finding the general location in our memory, the interconnections become more readily available. We begin to remember names, places, and events that had seemed to be forgotten.

Once people have started thinking about a problem one way, the same mental circuits or pathways get activated and strengthened each time they think about it. This facilitates the retrieval of information. These same pathways, however, also become the mental ruts that make it difficult to reorganize the information mentally so as to see it from a different perspective.

One useful concept of memory organization is what some cognitive psychologists call a “schema.” A schema is any pattern of relationships among data stored in memory. It is any set of nodes and links between them in the spider web of memory that hang together so strongly that they can be retrieved and used more or less as a single unit.

For example, a person may have a schema for a bar that when activated immediately makes available in memory knowledge of the properties of a bar and what distinguishes a bar, say, from a tavern. It brings back memories of specific bars that may in turn stimulate memories of thirst, guilt, or other feelings or circumstances. People also have schemata (plural for schema) for abstract concepts such as a socialist economic system and what distinguishes it from a capitalist or communist system. Schemata for phenomena such as success or failure in making an accurate intelligence estimate will include links to those elements of memory that explain typical causes and implications of success or failure. There must also be schemata for processes that link memories of the various steps involved in long division, regression analysis, or simply making inferences from evidence and writing an intelligence report.

Any given point in memory may be connected to many different overlapping schemata. This system is highly complex and not well understood.

It serves the purpose of emphasizing that memory does have structure. It also shows that how knowledge is connected in memory is critically important in determining what information is retrieved in response to any stimulus and how that information is used in reasoning.

Concepts and schemata stored in memory exercise a powerful influence on the formation of perceptions from sensory data.

If information does not fit into what people know, or think they know, they have great difficulty processing it.

The content of schemata in memory is a principal factor distinguishing stronger from weaker analytical ability. This is aptly illustrated by an experiment with chess players. When chess grandmasters and masters and ordinary chess players were given five to 10 seconds to note the position of 20 to 25 chess pieces placed randomly on a chess board, the masters and ordinary players were alike in being able to remember the places of only about six pieces. If the positions of the pieces were taken from an actual game (unknown to the test subjects), however, the grandmasters and masters were usually able to reproduce almost all the positions without error, while the ordinary players were still able to place correctly only a half-dozen pieces.

That the unique ability of the chess masters did not result from a pure feat of memory is indicated by the masters’ inability to perform better than ordinary players in remembering randomly placed positions. Their exceptional performance in remembering positions from actual games stems from their ability to immediately perceive patterns that enable them to process many bits of information together as a single chunk or schema. The chess master has available in long-term memory many schemata that connect individual positions together in coherent patterns. When the position of chess pieces on the board corresponds to a recognized schema, it is very easy for the master to remember not only the positions of the pieces, but the outcomes of previous games in which the pieces were in these positions. Similarly, the unique abilities of the master analyst are attributable to the schemata in long-term memory that enable the analyst to perceive patterns in data that pass undetected by the average observer.

Getting Information Into and Out of Long-Term Memory. It used to be that how well a person learned something was thought to depend upon how long it was kept in short-term memory or the number of times they repeated it to themselves. Research evidence now suggests that neither of these factors plays the critical role. Continuous repetition does not necessarily guarantee that something will be remembered. The key factor in transferring information from short-term to long-term memory is the development of associations between the new information and schemata already available in memory. This, in turn, depends upon two variables: the extent to which the information to be learned relates to an already existing schema, and the level of processing given to the new information.

Depth of processing is the second important variable in determining how well information is retained. Depth of processing refers to the amount of effort and cognitive capacity employed to process information, and the number and strength of associations that are thereby forged between the data to be learned and knowledge already in memory. In experiments to test how well people remember a list of words, test subjects might be asked to perform different tasks that reflect different levels of processing. The following illustrative tasks are listed in order of the depth of mental processing required: say how many letters there are in each word on the list, give a word that rhymes with each word, make a mental image of each word, make up a story that incorporates each word.

It turns out that the greater the depth of processing, the greater the ability to recall words on a list. This result holds true regardless of whether the test subjects are informed in advance that the purpose of the experiment is to test them on their memory. Advising test subjects to expect a test makes almost no difference in their performance, presumably because it only leads them to rehearse the information in short-term memory, which is ineffective as compared with other forms of processing.

There are three ways in which information may be learned or committed to memory: by rote, assimilation, or use of a mnemonic device.

By Rote. Material to be learned is repeated verbally with sufficient frequency that it can later be repeated from memory without use of any memory aids. When information is learned by rote, it forms a separate schema not closely interwoven with previously held knowledge. That is, the mental processing adds little by way of elaboration to the new information, and the new information adds little to the elaboration of existing schemata. Learning by rote is a brute force technique. It seems to be the least efficient way of remembering.

By Assimilation. Information is learned by assimilation when the structure or substance of the information fits into some memory schema already possessed by the learner. The new information is assimilated to or linked to the existing schema and can be retrieved readily by first accessing the existing schema and then reconstructing the new information. Assimilation involves learning by comprehension and is, therefore, a desirable method, but it can only be used to learn information that is somehow related to our previous experience.

By Using A Mnemonic Device. A mnemonic device is any means of organizing or encoding information for the purpose of making it easier to remember. A high school student cramming for a geography test might use the acronym “HOMES” as a device for remembering the first letter of each of the Great Lakes–Huron, Ontario, etc.

Memory and Intelligence Analysis

An analyst’s memory provides continuous input into the analytical process. This input is of two types–additional factual information on historical background and context, and schemata the analyst uses to determine the meaning of newly acquired information. Information from memory may force itself on the analyst’s awareness without any deliberate effort by the analyst to remember; or, recall of the information may require considerable time and strain. In either case, anything that influences what information is remembered or retrieved from memory also influences intelligence analysis.

Judgment is the joint product of the available information and what the analyst brings to the analysis of this information.

Substantive knowledge and analytical experience determine the store of memories and schemata the analyst draws upon to generate and evaluate hypotheses. The key is not a simple ability to recall facts, but the ability to recall patterns that relate facts to each other and to broader concepts–and to employ procedures that facilitate this process.

Stretching the Limits of Working Memory

Limited information is available on what is commonly thought of as “working memory”–the collection of information that an analyst holds in the forefront of the mind as he or she does analysis. The general concept of working memory seems clear from personal introspection.

In writing this chapter, I am very conscious of the constraints on my ability to keep many pieces of information in mind while experimenting with ways to organize this information and seeking words to express my thoughts. To help offset these limits on my working memory, I have accumulated a large number of written notes containing ideas and half-written paragraphs. Only by using such external memory aids am I able to cope with the volume and complexity of the information I want to use.

The recommended technique for coping with this limitation of working memory is called externalizing the problem–getting it out of one’s head and down on paper in some simplified form that shows the main elements of the problem and how they relate to each other.

breaking down a problem into its component parts and then preparing a simple “model” that shows how the parts relate to the whole. When working on a small part of the problem, the model keeps one from losing sight of the whole.

A simple model of an analytical problem facilitates the assimilation of new information into long-term memory; it provides a structure to which bits and pieces of information can be related. The model defines the categories for filing information in memory and retrieving it on demand. In other words, it serves as a mnemonic device that provides the hooks on which to hang information so that it can be found when needed.

“Hardening of the Categories”. Memory processes tend to work with generalized categories. If people do not have an appropriate category for something, they are unlikely to perceive it, store it in memory, or be able to retrieve it from memory later. If categories are drawn incorrectly, people are likely to perceive and remember things inaccurately. When information about phenomena that are different in important respects nonetheless gets stored in memory under a single concept, errors of analysis may result.

“Hardening of the categories” is a common analytical weakness. Fine distinctions among categories and tolerance for ambiguity contribute to more effective analysis.

Things That Influence What Is Remembered. Factors that influence how information is stored in memory and that affect future retrievability include: being the first-stored information on a given topic, the amount of attention focused on the information, the credibility of the information, and the importance attributed to the information at the moment of storage. By influencing the content of memory, all of these factors also influence the outcome of intelligence analysis.

Memory Rarely Changes Retroactively. Analysts often receive new information that should, logically, cause them to reevaluate the credibility or significance of previous information. Ideally, the earlier information should then become either more salient and readily available in memory, or less so. But it does not work that way. Unfortunately, memories are seldom reassessed or reorganized retroactively in response to new information. For example, information that is dismissed as unimportant or irrelevant because it did not fit an analyst’s expectations does not become more memorable even if the analyst changes his or her thinking to the point where the same information, received today, would be recognized as very significant.

Memory Can Handicap as Well as Help

Understanding how memory works provides some insight into the nature of creativity, openness to new information, and breaking mind-sets. All involve spinning new links in the spider web of memory–links among facts, concepts, and schemata that previously were not connected or only weakly connected.

There is, however, a crucial difference between the chess master and the master intelligence analyst. Although the chess master faces a different opponent in each match, the environment in which each contest takes place remains stable and unchanging: the permissible moves of the diverse pieces are rigidly determined, and the rules cannot be changed without the master’s knowledge. Once the chess master develops an accurate schema, there is no need to change it. The intelligence analyst, however, must cope with a rapidly changing world. Many countries that previously were US adversaries are now our formal or de facto allies. The American and Russian governments and societies are not the same today as they were 20 or even 10 or five years ago. Schemata that were valid yesterday may no longer be functional tomorrow.

Learning new schemata often requires the unlearning of existing ones, and this is exceedingly difficult. It is always easier to learn a new habit than to unlearn an old one.

PART II–TOOLS FOR THINKING

Chapter 4

Strategies for Analytical Judgment: Transcending the Limits of Incomplete Information

When intelligence analysts make thoughtful analytical judgments, how do they do it? In seeking answers to this question, this chapter discusses the strengths and limitations of situational logic, theory, comparison, and simple immersion in the data as strategies for the generation and evaluation of hypotheses. The final section discusses alternative strategies for choosing among hypotheses. One strategy too often used by intelligence analysts is described as “satisficing”–choosing the first hypothesis that appears good enough rather than carefully identifying all possible hypotheses and determining which is most consistent with the evidence.

Intelligence analysts should be self-conscious about their reasoning process. They should think about how they make judgments and reach conclusions, not just about the judgments and conclusions themselves.

Judgment is what analysts use to fill gaps in their knowledge. It entails going beyond the available information and is the principal means of coping with uncertainty. It always involves an analytical leap, from the known into the uncertain.

Judgment is an integral part of all intelligence analysis. While the optimal goal of intelligence collection is complete knowledge, this goal is seldom reached in practice. Almost by definition of the intelligence mission, intelligence issues involve considerable uncertainty.

Analytical strategies are important because they influence the data one attends to. They determine where the analyst shines his or her searchlight, and this inevitably affects the outcome of the analytical process.

Strategies for Generating and Evaluating Hypotheses

the goal is to understand the several kinds of careful, conscientious analysis one would hope and expect to find among a cadre of intelligence analysts dealing with highly complex issues.

Situational Logic

This is the most common operating mode for intelligence analysts. Generation and analysis of hypotheses start with consideration of concrete elements of the current situation, rather than with broad generalizations that encompass many similar cases. The situation is regarded as one-of-a-kind, so that it must be understood in terms of its own unique logic, rather than as one example of a broad class of comparable events.

Starting with the known facts of the current situation and an understanding of the unique forces at work at that particular time and place, the analyst seeks to identify
the logical antecedents or consequences of this situation. A scenario is developed that hangs together as a plausible narrative. The analyst may work backwards to explain the origins or causes of the current situation or forward to estimate the future outcome.

Situational logic commonly focuses on tracing cause-effect relationships or, when dealing with purposive behavior, means-ends relationships. The analyst identifies the goals being pursued and explains why the foreign actor(s) believe certain means will achieve those goals.

Particular strengths of situational logic are its wide applicability and ability to integrate a large volume of relevant detail. Any situation, however unique, may be analyzed in this manner.

Situational logic as an analytical strategy also has two principal weaknesses. One is that it is so difficult to understand the mental and bureaucratic processes of foreign leaders and governments. To see the options faced by foreign leaders as these leaders see them, one must understand their values and assumptions and even their misperceptions and misunderstandings. Without such insight, interpreting foreign leaders’ decisions or forecasting future decisions is often little more than partially informed speculation. Too frequently, foreign behavior appears “irrational” or “not in their own best interest.” Such conclusions often indicate analysts have projected American values and conceptual frameworks onto the foreign leaders and societies, rather than understanding the logic of the situation as it appears to them.

The second weakness is that situational logic fails to exploit the theoretical knowledge derived from study of similar phenomena in other countries and other time periods. The subject of national separatist movements illustrates the point. Nationalism is a centuries-old problem, but most Western industrial democracies have been considered well-integrated national communities.

Analyzing many examples of a similar phenomenon, as discussed below, enables one to probe more fundamental causes than those normally considered in logic-of-the- situation analysis. The proximate causes identified by situational logic appear, from the broader perspective of theoretical analysis, to be but symptoms indicating the presence of more fundamental causal factors. A better understanding of these fundamental causes is critical to effective forecasting, especially over the longer range.

Applying Theory

Theory is an academic term not much in vogue in the Intelligence Community, but it is unavoidable in any discussion of analytical judgment. In one popular meaning of the term, “theoretical” is associated with the terms “impractical” and “unrealistic”. Needless to say, it is used here in a quite different sense.

A theory is a generalization based on the study of many examples of some phenomenon. It specifies that when a given set of conditions arises, certain other conditions will follow either with certainty or with some degree of probability. In other words, conclusions are judged to follow from a set of conditions and a finding that these conditions apply in the specific case being analyzed.

What academics refer to as theory is really only a more explicit version of what intelligence analysts think of as their basic understanding of how individuals, institutions, and political systems normally behave.

Theoretical propositions frequently fail to specify the time frame within which developments might be anticipated to occur.

Further elaboration of the theory relating economic development and foreign ideas to political instability in feudal societies would identify early warning indicators that analysts might look for. Such indicators would guide both intelligence collection and analysis of sociopolitical and socioeconomic data and lead to hypotheses concerning when or under what circumstances such an event might occur.

But if theory enables the analyst to transcend the limits of available data, it may also provide the basis for ignoring evidence that is truly indicative of future events.

Figure 4 below illustrates graphically the difference between theory and situational logic. Situational logic looks at the evidence within a single country on multiple interrelated issues, as shown by the column highlighted in gray. This is a typical area studies approach. Theoretical analysis looks at the evidence related to a single issue in multiple countries, as shown by the row highlighted in gray. This is a typical social science approach.

The distinction between theory and situational logic is not as clear as it may seem from this graphic, however. Logic-of-the-situation analysis also draws heavily on theoretical assumptions. How does the analyst select the most significant elements to describe the current situation, or identify the causes or consequences of these elements, without some implicit theory that relates the likelihood of certain outcomes to certain antecedent conditions?

Comparison with Historical Situations

A third approach for going beyond the available information is comparison. An analyst seeks understanding of current events by comparing them with historical precedents in the same country, or with similar events in other countries. Analogy is one form of comparison. When an historical situation is deemed comparable to current circumstances, analysts use their understanding of the historical precedent to fill gaps in their understanding of the current situation. Unknown elements of the present are assumed to be the same as known elements of the historical precedent. Thus, analysts reason that the same forces are at work, that the outcome of the present situation is likely to be similar to the outcome of the historical situation, or that a certain policy is required in order to avoid the same outcome as in the past.

Comparison differs from situational logic in that the present situation is interpreted in the light of a more or less explicit conceptual model that is created by looking at similar situations in other times or places. It differs from theoretical analysis in that this conceptual model is based on a single case or only a few cases, rather than on many similar cases. Comparison may also be used to generate theory, but this is a more narrow kind of theorizing that cannot be validated nearly as well as generalizations inferred from many comparable cases.

Reasoning by comparison is a convenient shortcut, one chosen when neither data nor theory are available for the other analytical strategies, or simply because it is easier and less time-consuming than a more detailed analysis. A careful comparative analysis starts by specifying key elements of the present situation. The analyst then seeks out one or more historical precedents that may shed light on the present. Frequently, however, a historical precedent may be so vivid and powerful that it imposes itself upon a person’s thinking from the outset, conditioning them to perceive the present primarily in terms of its similarity to the past. This is reasoning by analogy. As Robert Jervis noted, “historical analogies often precede, rather than follow, a careful analysis of a situation.”

The tendency to relate contemporary events to earlier events as a guide to understanding is a powerful one. Comparison helps achieve understanding by reducing the unfamiliar to the familiar. In the absence of data required for a full understanding of the current situation, reasoning by comparison may be the only alternative. Anyone taking this approach, however, should be aware of the significant potential for error. This course is an implicit admission of the lack of sufficient information to understand the present situation in its own right, and lack of relevant theory to relate the present situation to many other comparable situations.

In a short book that ought to be familiar to all intelligence analysts, Ernest May traced the impact of historical analogies on U.S. Foreign Policy. He found that because of reasoning by analogy, US policymakers tend to be one generation behind, determined to avoid the mistakes of the previous generation. They pursue the policies that would have been most appropriate in the historical situation but are not necessarily well adapted to the current one.

Communist aggression after World War II was seen as analogous to Nazi aggression, leading to a policy of containment that could have prevented World War II.

May argues that policymakers often perceive problems in terms of analogies with the past, but that they ordinarily use history badly:

When resorting to an analogy, they tend to seize upon the first that comes to mind. They do not research more widely. Nor do they pause to analyze the case, test its fitness, or even ask in what ways it might be misleading.

As compared with policymakers, intelligence analysts have more time available to “analyze rather than analogize.” Intelligence analysts tend to be good historians, with a large number of historical precedents available for recall. The greater the number of potential analogues an analyst has at his or her disposal, the greater the likelihood of selecting an appropriate one. The greater the depth of an analyst’s knowledge, the greater the chances the analyst will perceive the differences as well as the similarities between two situations. Even under the best of circumstances, however, inferences based on comparison with a single analogous situation probably are more prone to error than most other forms of inference.

The most productive uses of comparative analysis are to suggest hypotheses and to highlight differences, not to draw conclusions. Comparison can suggest the presence or the influence of variables that are not readily apparent in the current situation, or stimulate the imagination to conceive explanations or possible outcomes that might not otherwise occur to the analyst. In short, comparison can generate hypotheses that then guide the search for additional information to confirm or refute these hypotheses. It should not, however, form the basis for conclusions unless thorough analysis of both situations has confirmed they are indeed comparable.

Data Immersion

Analysts sometimes describe their work procedure as immersing themselves in the data without fitting the data into any preconceived pattern. At some point an apparent pattern (or answer or explanation) emerges spontaneously, and the analyst then goes back to the data to check how well the data support this judgment. According to this view, objectivity requires the analyst to suppress any personal opinions or preconceptions, so as to be guided only by the “facts” of the case.

To think of analysis in this way overlooks the fact that information cannot speak for itself. The significance of information is always a joint function of the nature of the information and the context in which it is interpreted. The context is provided by the analyst in the form of a set of assumptions and expectations concerning human and organizational behavior. These preconceptions are critical determinants of which information is considered relevant and how it is interpreted.

Analysis begins when the analyst consciously inserts himself or herself into the process to select, sort, and organize information. This selection and organization can only be accomplished according to conscious or subconscious assumptions and preconceptions.

In research to determine how physicians make medical diagnoses, the doctors who comprised the test subjects were asked to describe their analytical strategies. Those who stressed thorough collection of data as

their principal analytical method were significantly less accurate in their diagnoses than those who described themselves as following other analytical strategies such as identifying and testing hypotheses.

Relationships Among Strategies

No one strategy is necessarily better than the others. In order to generate all relevant hypotheses and make maximum use of all potentially relevant information, it would be desirable to employ all three strategies at the early hypothesis generation phase of a research project. Unfortunately, analysts commonly lack the inclination or time to do so.

Differences in analytical strategy may cause fundamental differences in perspective between intelligence analysts and some of the policymakers for whom they write. Higher level officials who are not experts on the subject at issue use far more theory and comparison and less situational logic than intelligence analysts. Any policymaker or other senior manager who lacks the knowledge base of the specialist and does not have time for detail must, of necessity, deal with broad generalizations. Many decisions must be made, with much less time to consider each of them than is available to the intelligence analyst. This requires the policymaker to take a more conceptual approach, to think in terms of theories, models, or analogies that summarize large amounts of detail. Whether this represents sophistication or oversimplification depends upon the individual case and, perhaps, whether one agrees lead to increased diagnostic accuracy.

or disagrees with the judgments made. In any event, intelligence analysts would do well to take this phenomenon into account when writing for their consumers.

Strategies for Choice Among Hypotheses

A systematic analytical process requires selection among alternative hypotheses, and it is here that analytical practice often diverges significantly from the ideal and from the canons of scientific method. The ideal is to generate a full set of hypotheses, systematically evaluate each hypothesis, and then identify the hypothesis that provides the best fit to the data.

In practice, other strategies are commonly employed. Alexander George has identified a number of less-than-optimal strategies for making decisions in the face of incomplete information and multiple, competing values and goals. While George conceived of these strategies as applicable to how decisionmakers choose among alternative policies, most also apply to how intelligence analysts might decide among alternative analytical hypotheses.

The relevant strategies George identified are:

• “Satisficing”–selecting the first identified alternative that appears “good enough” rather than examining all alternatives to determine which is “best.”
• Incrementalism–focusing on a narrow range of alternatives representing marginal change, without considering the need for dramatic change from an existing position.
• Consensus–opting for the alternative that will elicit the greatest agreement and support. Simply telling the boss what he or she wants to hear is one version of this.
• Reasoning by analogy–choosing the alternative that appears most likely to avoid some previous error or to duplicate a previous success.
• Relying on a set of principles or maxims that distinguish a “good” from a “bad” alternative.

“Satisficing”

I would suggest, based on personal experience and discussions with analysts, that most analysis is conducted in a manner very similar to the satisficing mode (selecting the first identified alternative that appears “good enough”). The analyst identifies what appears to be the most likely hypothesis–that is, the tentative estimate, explanation, or description of the situation that appears most accurate.

This approach has three weaknesses: the selective perception that results from focus on a single hypothesis, failure to generate a complete set of competing hypotheses, and a focus on evidence that confirms rather than disconfirms hypotheses. Each of these is discussed below.

Selective Perception. Tentative hypotheses serve a useful function in helping analysts select, organize, and manage information. They narrow the scope of the problem so that the analyst can focus efficiently on data that are most relevant and important. The hypotheses serve as organizing frameworks in working memory and thus facilitate retrieval of information from memory. In short, they are essential elements of the analytical process. But their functional utility also entails some cost, because a hypothesis functions as a perceptual filter. Analysts, like people in general, tend to see what they are looking for and to overlook that which is not specifically included in their search strategy. They tend to limit the processed information to that which is relevant to the current hypothesis. If the hypothesis is incorrect, information may be lost that would suggest a new or modified hypothesis.

This difficulty can be overcome by the simultaneous consideration of multiple hypotheses.

It has the advantage of focusing attention on those few items of evidence that have the greatest diagnostic value in distinguishing among the validity of competing hypotheses. Most evidence is consistent with several different hypotheses, and this fact is easily overlooked when analysts focus on only one hypothesis at a time–especially if their focus is on seeking to confirm rather than disprove what appears to be the most likely answer.

Failure To Generate Appropriate Hypotheses. If tentative hypotheses determine the criteria for searching for information and judging its relevance, it follows that one may overlook the proper answer if it is not encompassed within the several hypotheses being considered. Research on hypothesis generation suggests that performance on this task is woefully inadequate.

Analysts need to take more time to develop a full set of competing hypotheses, using all three of the previously discussed strategies–theory, situational logic, and comparison.

Failure To Consider Diagnosticity of Evidence. In the absence of a complete set of alternative hypotheses, it is not possible to evaluate the “diagnosticity” of evidence. Unfortunately, many analysts are unfamiliar with the concept of diagnosticity of evidence. It refers to the extent to which any item of evidence helps the analyst determine the relative likelihood of alternative hypotheses.

Evidence is diagnostic when it influences an analyst’s judgment on the relative likelihood of the various hypotheses. If an item of evidence seems consistent with all the hypotheses, it may have no diagnostic value at all. It is a common experience to discover that most available evidence really is not very helpful, as it can be reconciled with all the hypotheses.

Failure To Reject Hypotheses

Scientific method is based on the principle of rejecting hypotheses, while tentatively accepting only those hypotheses that cannot be refuted. Intuitive analysis, by comparison, generally concentrates on confirming a hypothesis and commonly accords more weight to evidence supporting a hypothesis than to evidence that weakens it. Ideally, the reverse would be true. While analysts usually cannot apply the statistical procedures of scientific methodology to test their hypotheses, they can and should adopt the conceptual strategy of seeking to refute rather than confirm hypotheses.

Failure To Reject Hypotheses

Scientific method is based on the principle of rejecting hypotheses, while tentatively accepting only those hypotheses that cannot be refuted. Intuitive analysis, by comparison, generally concentrates on confirming a hypothesis and commonly accords more weight to evidence supporting a hypothesis than to evidence that weakens it. Ideally, the reverse would be true. While analysts usually cannot apply the statistical procedures of scientific methodology to test their hypotheses, they can and should adopt the conceptual strategy of seeking to refute rather than confirm hypotheses.

There are two aspects to this problem: people do not naturally seek disconfirming evidence, and when such evidence is received it tends to be discounted. If there is any question about the former, consider how often people test their political and religious beliefs by reading newspapers and books representing an opposing viewpoint.

Apart from the psychological pitfalls involved in seeking confirmatory evidence, an important logical point also needs to be considered. The logical reasoning underlying the scientific method of rejecting hypotheses is that “…no confirming instance of a law is a verifying instance, but that any disconfirming instance is a falsifying instance”.

In other words, a hypothesis can never be proved by the enumeration of even a large body of evidence consistent with that hypothesis, because the same body of evidence may also be consistent with other hypotheses. A hypothesis may be disproved, however, by citing a single item of evidence that is incompatible with it.

the validity of a hypothesis can only be tested by seeking to disprove it rather than confirm it.

Consider lists of early warning indicators, for example. They are designed to be indicative of an impending attack. Very many of them, however, are also consistent with the hypothesis that military movements are a bluff to exert diplomatic pressure and that no military action will be forthcoming. When analysts seize upon only one of these hypotheses and seek evidence to confirm it, they will often be led astray.

The evidence available to the intelligence analyst is in one important sense different from the evidence available to test subjects asked to infer the number sequence rule. The intelligence analyst commonly deals with problems in which the evidence has only a probabilistic relationship to the hypotheses being considered. Thus it is seldom possible to eliminate any hypothesis entirely, because the most one can say is that a given hypothesis is unlikely given the nature of the evidence, not that it is impossible.

This weakens the conclusions that can be drawn from a strategy aimed at eliminating hypotheses, but it does not in any way justify a strategy aimed at confirming them.

Conclusion

There are many detailed assessments of intelligence failures, but few comparable descriptions of intelligence successes. In reviewing the literature on intelligence successes, Frank Stech found many examples of success but only three accounts that provide sufficient methodological details to shed light on the intellectual processes and methods that contributed to the successes.

Chapter 5
Do You Really Need More Information?

The difficulties associated with intelligence analysis are often attributed to the inadequacy of available information. Thus the US Intelligence Community invests heavily in improved intelligence collection systems while managers of analysis lament the comparatively small sums devoted to enhancing analytical resources, improving analytical methods, or gaining better understanding of the cognitive processes involved in making analytical judgments. This chapter questions the often- implicit assumption that lack of information is the principal obstacle to accurate intelligence judgements.

Using experts in a variety of fields as test subjects, experimental psychologists have examined the relationship between the amount of information available to the experts, the accuracy of judgments they make based on this information, and the experts’

intelligence judgments.

confidence in the accuracy of these judgments. The word “information,” as used in this context, refers to the totality of material an analyst has available to work with in making a judgment.

Key findings from this research are:

• Once an experienced analyst has the minimum information necessary to make an informed judgment, obtaining additional information generally does not improve the accuracy of his or her estimates. Additional information does, however, lead the analyst to become more confident in the judgment, to the point of overconfidence.
• Experienced analysts have an imperfect understanding of what information they actually use in making judgments. They are unaware of the extent to which their judgments are determined by a few dominant factors, rather than by the systematic integration of all available information. Analysts actually use much less of the available information than they think they do.

To interpret the disturbing but not surprising findings from these experiments, it is necessary to consider four different types of information and discuss their relative value in contributing to the accuracy of analytical judgments. It is also helpful to distinguish analysis in which results are driven by the data from analysis that is driven by the conceptual framework employed to interpret the data.

Understanding the complex relationship between amount of information and accuracy of judgment has implications for both the management and conduct of intelligence analysis. Such an understanding suggests analytical procedures and management initiatives that may indeed contribute to more accurate analytical judgments. It also suggests that resources needed to attain a better understanding of the entire analytical process might profitably be diverted from some of the more costly intelligence collection programs.

Modeling Expert Judgment

Another significant question concerns the extent to which analysts possess an accurate understanding of their own mental processes. How good is their insight into how they actually weight evidence in making judgments? For each situation to be analyzed, they have an implicit “mental model” consisting of beliefs and assumptions as to which variables are most important and how they are related to each other. If analysts have good insight into their own mental model, they should be able to identify and describe the variables they have considered most important in making judgments.

There is strong experimental evidence, however, that such self-insight is usually faulty. The expert perceives his or her own judgmental process, including the number of different kinds of information taken into account, as being considerably more complex than is in fact the case. Experts overestimate the importance of factors that have only a minor impact on their judgment and underestimate the extent to which their decisions are based on a few major variables. In short, people’s mental models are simpler than they think, and the analyst is typically unaware not only of which variables should have the greatest influence, but also which variables actually are having the greatest influence.

When Does New Information Affect Our Judgment?

To evaluate the relevance and significance of these experimental findings in the context of intelligence analysts’ experiences, it is necessary to distinguish four types of additional information that an analyst might receive:

• Additional detail about variables already included in the analysis: Much raw intelligence reporting falls into this category. One would not expect such supplementary information to affect the overall accuracy of the analyst’s judgment, and it is readily understandable that further detail that is consistent with previous information increases the analyst’s confidence. Analyses for which considerable depth of detail is available to support the conclusions tend to be more persuasive to their authors as well as to their readers.
• Identification of additional variables: Information on additional variables permits the analyst to take into account other factors that may affect the situation. This is the kind of additional information used in the horserace handicapper experiment. Other experiments have employed some combination of additional variables and additional detail on the same variables. The finding that judgments are based on a few critical variables rather than on the entire spectrum of evidence helps to explain why information on additional variables does not normally improve predictive accuracy. Occasionally, in situations when there are known gaps in an analyst’s understanding, a single report concerning some new and previously unconsidered factor–for example, an authoritative report on a policy decision or planned coup d’etat–will have a major impact on the analyst’s judgment. Such a report would fall into one of the next two categories of new information.
• Information concerning the value attributed to variables already included in the analysis: An example of such information would be the horserace handicapper learning that a horse he thought would carry 110 pounds will actually carry only 106. Current intelligence reporting tends to deal with this kind of information; for example, an analyst may learn that a dissident group is stronger than had been anticipated. New facts affect the accuracy of judgments when they deal with changes in variables that are critical to the estimates. Analysts’ confidence in judgments based on such information is influenced by their confidence in the accuracy of the information as well as by the amount of information.
• Information concerning which variables are most important and how they relate to each other: Knowledge and assumptions as to which variables are most important and how they are interrelated comprise the mental model that tells the analyst how to analyze the data received. Explicit investigation of such relationships is one factor that distinguishes systematic research from current intelligence reporting and raw intelligence. In the context of the horserace handicapper experiment, for example, handicappers had to select which variables to include in their analysis. Is weight carried by a horse more, or less, important than several other variables that affect a horse’s performance? Any information that affects this judgment influences how the handicapper analyzes the available data; that is, it affects his mental model.

The accuracy of an analyst’s judgment depends upon both the accuracy of our mental model (the fourth type of information discussed above) and the accuracy of the values attributed to the key variables in the model (the third type of information discussed above). Additional detail on variables already in the analyst’s mental model and information on other variables that do not in fact have a significant influence on our judgment (the first and second types of information) have a negligible impact on accuracy, but form the bulk of the raw material analysts work with. These kinds of information increase confidence because the conclusions seem to be supported by such a large body of data.

This discussion of types of new information is the basis for distinguishing two types of analysis- data-driven analysis and conceptually-driven analysis.

Data-Driven Analysis

In this type of analysis, accuracy depends primarily upon the accuracy and completeness of the available data. If one makes the reasonable assumption that the analytical model is correct and the further assumption that the analyst properly applies this model to the data, then the accuracy of the analytical judgment depends entirely upon the accuracy and completeness of the data.

Analyzing the combat readiness of a military division is an example of data-driven analysis. In analyzing combat readiness, the rules and procedures to be followed are relatively well established.

Most elements of the mental model can be made explicit so that other analysts may be taught to understand and follow the same analytical procedures and arrive at the same or similar results. There is broad, though not necessarily universal, agreement on what the appropriate model is. There are relatively objective standards for judging the quality of analysis, inasmuch as the conclusions follow logically from the application of the agreed-upon model to the available data.

Conceptually Driven Analysis

Conceptually driven analysis is at the opposite end of the spectrum from data-driven analysis. The questions to be answered do not have neat boundaries, and there are many unknowns. The number of potentially relevant variables and the diverse and imperfectly understood relationships among these variables involve the analyst in enormous complexity and uncertainty.

In the absence of any agreed-upon analytical schema, analysts are left to their own devices. They interpret information with the aid of mental models that are largely implicit rather than explicit. Assumptions concerning political forces and processes in the subject country may not be apparent even to the analyst. Such models are not representative of an analytical consensus. Other analysts examining the same data may well reach different conclusions, or reach the same conclusions but for different reasons. This analysis is conceptually driven, because the outcome depends at least as much upon the conceptual framework employed to analyze the data as it does upon the data itself.

To illustrate further the distinction between data-driven and conceptually driven analysis, it is useful to consider the function of the analyst responsible for current intelligence, especially current political intelligence as distinct from longer term research. The daily routine is driven by the incoming wire service news, embassy cables, and clandestine-source reporting from overseas that must be interpreted for dissemination to consumers throughout the Intelligence Community. Although current intelligence reporting is driven by incoming information, this is not what is meant by data-driven analysis. On the contrary, the current intelligence analyst’s task is often extremely concept-driven. The analyst must provide immediate interpretation of the latest, often unexpected events. Apart from his or her store of background information, the analyst may have no data other than the initial, usually incomplete report. Under these circumstances, interpretation is based upon an implicit mental model of how and why events normally transpire in the country for which the analyst is responsible. Accuracy of judgment depends almost exclusively upon accuracy of the mental model, for there is little other basis for judgment.

Partly because of the nature of human perception and information-processing, beliefs of all types tend to resist change. This is especially true of the implicit assumptions and supposedly self-evident truths that play an important role in forming mental models. Analysts are often surprised to learn that what are to them self-evident truths are by no means self-evident to others, or that self-evident truth at one point in time may be commonly regarded as uninformed assumption 10 years later.

Information that is consistent with an existing mind-set is perceived and processed easily and reinforces existing beliefs. Because the mind strives instinctively for consistency, information that is inconsistent with an existing mental image tends to be overlooked, perceived in a distorted manner, or rationalized to fit existing assumptions and beliefs.

Mosaic Theory of Analysis

Understanding of the analytic process has been distorted by the mosaic metaphor commonly used to describe it. According to the mosaic theory of intelligence, small pieces of information are collected that, when put together like a mosaic or jigsaw puzzle, eventually enable analysts to perceive a clear picture of reality. The analogy suggests that accurate estimates depend primarily upon having all the pieces, that is, upon accurate and relatively complete information. It is important to collect and store the small pieces of information, as these are the raw material from which the picture is made; one never knows when it will be possible for an astute analyst to fit a piece into the puzzle. Part of the rationale for large technical intelligence collection systems is rooted in this mosaic theory.

Insights from cognitive psychology suggest that intelligence analysts do not work this way and that the most difficult analytical tasks cannot be approached in this manner. Analysts commonly find pieces that appear to fit many different pictures. Instead of a picture emerging from putting all the pieces together, analysts typically form a picture first and then select the pieces to fit. Accurate estimates depend at least as much upon the mental model used in forming the picture as upon the number of pieces of the puzzle that have been collected.

While analysis and collection are both important, the medical analogy attributes more value to analysis and less to collection than the mosaic metaphor.

Conclusions

To the leaders and managers of intelligence who seek an improved intelligence product, these findings offer a reminder that this goal can be achieved by improving analysis as well as collection. There appear to be inherent practical limits on how much can be gained by efforts to improve collection. By contrast, an open and fertile field exists for imaginative efforts to improve analysis.

These efforts should focus on improving the mental models employed by analysts to interpret information and the analytical processes used to evaluate it. While this will be difficult to achieve, it is so critical to effective intelligence analysis that even small improvements could have large benefits. Specific recommendations are included the next three chapters and in Chapter 14, “Improving Intelligence Analysis.”

Chapter 6

Keeping an Open Mind

Minds are like parachutes. They only function when they are open. After reviewing how and why thinking gets channeled into mental ruts, this chapter looks at mental tools to help analysts keep an open mind, question assumptions, see different perspectives, develop new ideas, and recognize when it is time to change their minds.

A new idea is the beginning, not the end, of the creative process. It must jump over many hurdles before being embraced as an organizational product or solution. The organizational climate plays a crucial role in determining whether new ideas bubble to the surface or are suppressed.

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Major intelligence failures are usually caused by failures of analysis, not failures of collection. Relevant information is discounted, misinterpreted, ignored, rejected, or overlooked because it fails to fit a prevailing mental model or mind-set. The “signals” are lost in the “noise.”

A mind-set is neither good nor bad. It is unavoidable. It is, in essence, a distillation of all that analysts think they know about a subject. It forms a lens through which they perceive the world, and once formed, it resists change.

Understanding Mental Ruts

Chapter 3 on memory suggested thinking of information in memory as somehow interconnected like a massive, multidimensional spider web. It is possible to connect any point within this web to any other point. When analysts connect the same points frequently, they form a path that makes it easier to take that route in the future. Once they start thinking along certain channels, they tend to continue thinking the same way and the path may become a rut.

Talking about breaking mind-sets, or creativity, or even just openness to new information is really talking about spinning new links and new paths through the web of memory. These are links among facts and concepts, or between schemata for organizing facts or concepts, that were not directly connected or only weakly connected before.

Problem-Solving Exercise

intelligence analysis is too often limited by similar, unconscious, self-imposed constraints or “cages of the mind.”

You do not need to be constrained by conventional wisdom. It is often wrong. You do not necessarily need to be constrained by existing policies. They can sometimes be changed if you show a good reason for doing so. You do not necessarily need to be constrained by the specific analytical requirement you were given. The policymaker who originated the requirement may not have thought through his or her needs or the requirement may be somewhat garbled as it passes down through several echelons to you to do the work. You may have a better understanding than the policymaker of what he or she needs, or should have, or what is possible to do. You should not hesitate to go back up the chain of command with a suggestion for doing something a little different than what was asked for.

Mental Tools

People use various physical tools such as a hammer and saw to enhance their capacity to perform various physical tasks. People can also use simple mental tools to enhance their ability to perform mental tasks. These tools help overcome limitations in human mental machinery for perception, memory, and inference.

Questioning Assumptions

It is a truism that analysts need to question their assumptions. Experience tells us that when analytical judgments turn out to be wrong, it usually was not because the information was wrong. It was because an analyst made one or more faulty assumptions that went unchallenged.

Sensitivity Analysis. One approach is to do an informal sensitivity analysis. How sensitive is the ultimate judgment to changes in any of the major variables or driving forces in the analysis? Those linchpin assumptions that drive the analysis are the ones that need to be questioned. Analysts should ask themselves what could happen to make any of these assumptions out of date, and how they can know this has not already happened. They should try to disprove their assumptions rather than confirm them. If an analyst cannot think of anything that would cause a change of mind, his or her mind-set may be so deeply entrenched that the analyst cannot see the conflicting evidence. One advantage of the competing hypotheses approach discussed in Chapter 8 is that it helps identify the linchpin assumptions that swing a conclusion in one direction or another.

Identify Alternative Models. Analysts should try to identify alternative models, conceptual frameworks, or interpretations of the data by seeking out individuals who disagree with them rather than those who agree. Most people do not do that very often. It is much more comfortable to talk with people in one’s own office who share the same basic mind-set. There are a few things that can be done as a matter of policy, and that have been done in some offices in the past, to help overcome this tendency.

At least one Directorate of Intelligence component, for example, has had a peer review process in which none of the reviewers was from the branch that produced the report. The rationale for this was that an analyst’s immediate colleagues and supervisor(s) are likely to share a common mind-set. Hence these are the individuals least likely to raise fundamental issues challenging the validity of the analysis. To

avoid this mind-set problem, each research report was reviewed by a committee of three analysts from other branches handling other countries or issues. None of them had specialized knowledge of the subject. They were, however, highly accomplished analysts. Precisely because they had not been immersed in the issue in question, they were better able to identify hidden assumptions and other alternatives, and to judge whether the analysis adequately supported the conclusions.

Be Wary of Mirror Images. One kind of assumption an analyst should always recognize and question is mirror-imaging–filling gaps in the analyst’s own knowledge by assuming that the other side is likely to act in a certain way because that is how the US would act under similar circumstances. To say, “if I were a Russian intelligence officer …” or “if I were running the Indian Government …” is mirror-imaging. Analysts may have to do that when they do not know how the Russian intelligence officer or the Indian Government is really thinking. But mirror-imaging leads to dangerous assumptions, because people in other cultures do not think the way we do.

Failure to understand that others perceive their national interests differently from the way we perceive those interests is a constant source of problems in intelligence analysis.

Seeing Different Perspectives

Another problem area is looking at familiar data from a different perspective. If you play chess, you know you can see your own options pretty well. It is much more difficult to see all the pieces on the board as your opponent sees them, and to anticipate how your opponent will react to your move. That is the situation analysts are in when they try to see how the US Government’s actions look from another country’s perspective. Analysts constantly have to move back and forth, first seeing the situation from the US perspective and then from the other country’s perspective. This is difficult to do

Thinking Backwards. One technique for exploring new ground is thinking backwards. As an intellectual exercise, start with an assumption that some event you did not expect has actually occurred. Then, put yourself into the future, looking back to explain how this could have happened. Think what must have happened six months or a year earlier to set the stage for that outcome, what must have happened six months or a year before that to prepare the way, and so on back to the present.

Crystal Ball. The crystal ball approach works in much the same way as thinking 71

backwards. Imagine that a “perfect” intelligence source (such as a crystal ball) has told you a certain assumption is wrong. You must then develop a scenario to explain how this could be true. If you can develop a plausible scenario, this suggests your assumption is open to some question.

Role playing. Role playing is commonly used to overcome constraints and inhibitions that limit the range of one’s thinking. Playing a role changes “where you sit.” It also gives one license to think and act differently. Simply trying to imagine how another leader or country will think and react, which analysts do frequently, is not role playing. One must actually act out the role and become, in a sense, the person whose role is assumed. It is only “living” the role that breaks an analyst’s normal mental set and permits him or her to relate facts and ideas to each other in ways that differ from habitual patterns. An analyst cannot be expected to do this alone; some group interaction is required, with different analysts playing different roles, usually in the context of an organized simulation or game.

Just one notional intelligence report is sufficient to start the action in the game. In my experience, it is possible to have a useful political game in just one day with almost no investment in preparatory work.

Devil’s Advocate. A devil’s advocate is someone who defends a minority point of view. He or she may not necessarily agree with that view, but may choose or be assigned to represent it as strenuously as possible. The goal is to expose conflicting interpretations and show how alternative assumptions and images make the world look different. It often requires time, energy, and commitment to see how the world looks from a different perspective.

Imagine that you are the boss at a US facility overseas and are worried about the possibility of a terrorist attack. A standard staff response would be to review existing measures and judge their adequacy. There might well be pressure–subtle or otherwise–from those responsible for such arrangements to find them satisfactory. An alternative or supplementary approach would be to name an individual or small group as a devil’s advocate assigned to develop actual plans for launching such an attack. The assignment to think like a terrorist liberates the designated person(s) to think unconventionally and be less inhibited about finding weaknesses in the system that might embarrass colleagues, because uncovering any such weaknesses is the assigned task.

Recognizing When To Change Your Mind

As a general rule, people are too slow to change an established view, as opposed to being too willing to change. The human mind is conservative. It resists change. Assumptions that worked well in the past continue to be applied to new situations long after they have become outmoded.

Learning from Surprise. A study of senior managers in industry identified how some successful managers counteract this conservative bent. They do it, according to the study, looks from a different perspective.

By paying attention to their feelings of surprise when a particular fact does not fit their prior understanding, and then by highlighting rather than denying the novelty. Although surprise made them feel uncomfortable, it made them take the cause [of the surprise] seriously and inquire into it….Rather than deny, downplay, or ignore disconfirmation [of their prior view], successful senior managers often treat it as friendly and in a way cherish the discomfort surprise creates. As a result, these managers often perceive novel situations early on and in a frame of mind relatively undistorted by hidebound notions.

Analysts should keep a record of unexpected events and think hard about what they might mean, not disregard them or explain them away. It is important to consider whether these surprises, however small, are consistent with some alternative hypothesis. One unexpected event may be easy to disregard, but a pattern of surprises may be the first clue that your understanding of what is happening requires some adjustment, is at best incomplete, and may be quite wrong.

Strategic Assumptions vs. Tactical Indicators. Abraham Ben-Zvi analyzed five cases of intelligence failure to foresee a surprise attack. He made a useful distinction between estimates based on strategic assumptions and estimates based on tactical indications.

Tactical indicators are specific reports of preparations or intent to initiate hostile action or, in the recent Indian case, reports of preparations for a nuclear test.

tactical indicators should be given increased weight in the decisionmaking process.

At a minimum, the emergence of tactical indicators that contradict our strategic assumption should trigger a higher level of intelligence alert. It may indicate that a bigger surprise is on the way.

Stimulating Creative Thinking

Imagination and creativity play important roles in intelligence analysis as in most other human endeavors. Intelligence judgments require the ability to imagine possible causes and outcomes of a current situation. All possible outcomes are not given. The analyst must think of them by imagining scenarios that explicate how they might come about. Similarly, imagination as well as knowledge is required to reconstruct how a problem appears from the viewpoint of a foreign government. Creativity is required to question things that have long been taken for granted. The fact that apples fall from trees was well known to everyone. Newton’s creative genius was to ask “why?” Intelligence analysts, too, are expected to raise new questions that lead to the identification of previously unrecognized relationships or to possible outcomes that had not previously been foreseen.

A creative analytical product shows a flair for devising imaginative or innovative– but also accurate and effective–ways to fulfill any of the major requirements of analysis: gathering information, analyzing information, documenting evidence, and/or presenting conclusions. Tapping unusual sources of data, asking new questions, applying unusual analytic methods, and developing new types of products or new ways of fitting analysis to the needs of consumers are all examples of creative activity.

The old view that creativity is something one is born with, and that it cannot be taught or developed, is largely untrue. While native talent, per se, is important and may be immutable, it is possible to learn to employ one’s innate talents more productively. With understanding, practice, and conscious effort, analysts can learn to produce more imaginative, innovative, creative work.

There is a large body of literature on creativity and how to stimulate it. At least a half- dozen different methods have been developed for teaching, facilitating, or liberating creative thinking. All the methods for teaching or facilitating creativity are based on the assumption that the process of thinking can be separated from the content of thought. One learns mental strategies that can be applied to any subject.

It is not our purpose here to review commercially available programs for enhancing creativity. Such programmatic approaches can be applied more meaningfully to problems of new product development, advertising, or management than to intelligence analysis. It is relevant, however, to discuss several key principles and techniques that these programs have in common, and that individual intelligence analysts or groups of analysts can apply in their work.

Intelligence analysts must generate ideas concerning potential causes or explanations of events, policies that might be pursued or actions taken by a foreign government, possible outcomes of an existing situation, and variables that will influence which outcome actually comes to pass. Analysts also need help to jog them out of mental ruts, to stimulate their memories and imaginations, and to perceive familiar events from a new perspective.

Deferred Judgment. The principle of deferred judgment is undoubtedly the most important. The idea-generation phase of analysis should be separated from the idea- evaluation phase, with evaluation deferred until all possible ideas have been brought out. This approach runs contrary to the normal procedure of thinking of ideas and evaluating them concurrently. Stimulating the imagination and critical thinking are both important, but they do not mix well. A judgmental attitude dampens the imagination, whether it manifests itself as self-censorship of one’s own ideas or fear of critical evaluation by colleagues or supervisors. Idea generation should be a freewheeling, unconstrained, uncritical process.

New ideas are, by definition, unconventional, and therefore likely to be suppressed, either consciously or unconsciously, unless they are born in a secure and protected environment. Critical judgment should be suspended until after the idea-generation stage of analysis has been completed. A series of ideas should be written down and then evaluated later. This applies to idea searching by individuals as well as brainstorming in a group. Get all the ideas out on the table before evaluating any of them.

Quantity Leads to Quality. A second principle is that quantity of ideas eventually leads to quality. This is based on the assumption that the first ideas that come to mind will be those that are most common or usual. It is necessary to run through these conventional ideas before arriving at original or different ones. People have habitual ways of thinking, ways that they continue to use because they have seemed successful in the past. It may well be that these habitual responses, the ones that come first to mind, are the best responses and that further search is unnecessary. In looking for usable new ideas, however, one should seek to generate as many ideas as possible before evaluating any of them.

No Self-Imposed Constraints. A third principle is that thinking should be allowed– indeed encouraged–to range as freely as possible. It is necessary to free oneself from self-imposed constraints, whether they stem from analytical habit, limited perspective, social norms, emotional blocks, or whatever.

Cross-Fertilization of Ideas. A fourth principle of creative problem-solving is that cross-fertilization of ideas is important and necessary. Ideas should be combined with each other to form more and even better ideas. If creative thinking involves forging new links between previously unrelated or weakly related concepts, then creativity will be stimulated by any activity that brings more concepts into juxtaposition with each other in fresh ways. Interaction with other analysts is one basic mechanism for this. As a general rule, people generate more creative ideas when teamed up with others; they help to build and develop each other’s ideas. Personal interaction stimulates new associations between ideas. It also induces greater effort and helps maintain concentration on the task.

These favorable comments on group processes are not meant to encompass standard committee meetings or coordination processes that force consensus based on the lowest common denominator of agreement. My positive words about group interaction apply primarily to brainstorming sessions aimed at generating new ideas and in which, according to the first principle discussed above, all criticism and evaluation are deferred until after the idea generation stage is completed.

Thinking things out alone also has its advantages: individual thought tends to be more structured and systematic than interaction within a group. Optimal results come from alternating between individual thinking and team effort, using group interaction to generate ideas that supplement individual thought. A diverse group is clearly preferable to a homogeneous one. Some group participants should be analysts who are not close to the problem, inasmuch as their ideas are more likely to reflect different insights.

Idea Evaluation. All creativity techniques are concerned with stimulating the flow of ideas. There are no comparable techniques for determining which ideas are best. The procedures are, therefore, aimed at idea generation rather than idea evaluation. The same procedures do aid in evaluation, however, in the sense that ability to generate more alternatives helps one see more potential consequences, repercussions, and effects that any single idea or action might entail.

Organizational Environment

A new idea is not the end product of the creative process. Rather, it is the beginning of what is sometimes a long and tortuous process of translating an idea into an innovative product. The idea must be developed, evaluated, and communicated to others, and this process is influenced by the organizational setting in which it transpires. The potentially useful new idea must pass over a number of hurdles before it is embraced as an organizational product.

Organizational Environment

A new idea is not the end product of the creative process. Rather, it is the beginning of what is sometimes a long and tortuous process of translating an idea into an innovative product. The idea must be developed, evaluated, and communicated to others, and this process is influenced by the organizational setting in which it transpires. The potentially useful new idea must pass over a number of hurdles before it is embraced as an organizational product.

A panel of judges composed of the leading scientists in the field of medical sociology was asked to evaluate the principal published results from each of the 115 research projects. Judges evaluated the research results on the basis of productivity and innovation.

Productivity was defined as the “extent to which the research represents an addition to knowledge along established lines of research or as extensions of previous theory.”

Innovativeness was defined as “additions to knowledge through new lines of research or the development of new theoretical statements of findings that were not explicit in previous theory. Innovation, in other words, involved raising new questions and developing new approaches to the acquisition of knowledge, as distinct from working productively within an already established framework. This same definition applies to innovation in intelligence analysis.

Andrews found virtually no relationship between the scientists’ creative ability and the innovativeness of their research. (There was also no relationship between level of intelligence and innovativeness.) Those who scored high on tests of creative ability did not necessarily receive high ratings from the judges evaluating the innovativeness of their work. A possible explanation is that either creative ability or innovation, or both, were not measured accurately, but Andrews argues persuasively for another view. Various social and psychological factors have so great an effect on the steps needed to translate creative ability into an innovative research product that there is no measurable effect traceable to creative ability alone. In order to document this conclusion, Andrews analyzed data from the questionnaires in which the scientists described their work environment.

Andrews found that scientists possessing more creative ability produced more innovative work only under the following favorable conditions:

• When the scientist perceived himself or herself as responsible for initiating new activities. The opportunity for innovation, and the encouragement of it, are–not surprisingly–important variables.
• When the scientist had considerable control over decisionmaking concerning his or her research program–in other words, the freedom to set goals, hire research assistants, and expend funds. Under these circumstances, a new idea is less likely to be snuffed out before it can be developed into a creative and useful product.
• When the scientist felt secure and comfortable in his or her professional role. New ideas are often disruptive, and pursuing them carries the risk of failure. People are more likely to advance new ideas if they feel secure in their positions.
• When the scientist’s administrative superior “stayed out of the way.” Research is likely to be more innovative when the superior limits himself or herself to support and facilitation rather than direct involvement.
• When the project was relatively small with respect to the number of people involved, budget, and duration. Small size promotes flexibility, and this in turn is more conducive to creativity.
• When the scientist engaged in other activities, such as teaching or administration, in addition to the research project. Other work may provide useful stimulation or help one identify opportunities for developing or implementing new ideas. Some time away from the task, or an incubation period, is generally recognized as part of the creative process.”

The importance of any one of these factors was not very great, but their impact was cumulative. The presence of all or most of these conditions exerted a strongly favorable influence on the creative process. Conversely, the absence of these conditions made it quite unlikely that even highly creative scientists could develop their new ideas into innovative research results. Under unfavorable conditions, the most creatively inclined scientists produced even less innovative work than their less imaginative colleagues, presumably because they experienced greater frustration with their work environment.

There are, of course, exceptions to the rule. Some creativity occurs even in the face of intense opposition. A hostile environment can be stimulating, enlivening, and challenging. Some people gain satisfaction from viewing themselves as lonely fighters in the wilderness, but when it comes to conflict between a large organization and a creative individual within it, the organization generally wins.

Recognizing the role of organizational environment in stimulating or suppressing creativity points the way to one obvious set of measures to enhance creative organizational performance. Managers of analysis, from first-echelon supervisors to the Director of Central Intelligence, should take steps to strengthen and broaden the perception among analysts that new ideas are welcome. This is not easy; creativity implies criticism of that which already exists. It is, therefore, inherently disruptive of established ideas and organizational practices.

Particularly within his or her own office, an analyst needs to enjoy a sense of security, so that partially developed ideas may be expressed and bounced off others as sounding boards with minimal fear of criticism or ridicule for deviating from established orthodoxy. At its inception, a new idea is frail and vulnerable. It needs to be nurtured, developed, and tested in a protected environment before being exposed to the harsh reality of public criticism. It is the responsibility of an analyst’s immediate supervisor and office colleagues to provide this sheltered environment.

Conclusions

Creativity, in the sense of new and useful ideas, is at least as important in intelligence analysis as in any other human endeavor. Procedures to enhance innovative thinking are not new. Creative thinkers have employed them successfully for centuries. The only new elements–and even they may not be new anymore–are the grounding of these procedures in psychological theory to explain how and why they work, and their formalization in systematic creativity programs.

Another prerequisite to creativity is sufficient strength of character to suggest new ideas to others, possibly at the expense of being rejected or even ridiculed on occasion. “The ideas of creative people often lead them into direct conflict with the trends of their time, and they need the courage to be able to stand alone.”

Chapter 7

Structuring Analytical Problems

This chapter discusses various structures for decomposing and externalizing complex analytical problems when we cannot keep all the relevant factors in the forefront of our consciousness at the same time.

Decomposition means breaking a problem down into its component parts. Externalization means getting the problem out of our heads and into some visible form that we can work with.

There are two basic tools for dealing with complexity in analysis–decomposition and externalization.

Decomposition means breaking a problem down into its component parts. That is, indeed, the essence of analysis. Webster’s Dictionary defines analysis as division of a complex whole into its parts or elements.

The spirit of decision analysis is to divide and conquer: Decompose a complex problem into simpler problems, get one’s thinking straight in these simpler problems, paste these analyses together with a logical glue …

Externalization means getting the decomposed problem out of one’s head and down on paper or on a computer screen in some simplified form that shows the main variables, parameters, or elements of the problem and how they relate to each other.

Putting ideas into visible form ensures that they will last. They will lie around for days goading you into having further thoughts. Lists are effective because they exploit people’s tendency to be a bit compulsive–we want to keep adding to them. They let us get the obvious and habitual answers out of the way, so that we can add to the list by thinking of other ideas beyond those that came first to mind. One specialist in creativity has observed that “for the purpose of moving our minds, pencils can serve as crowbars” –just by writing things down and making lists that stimulate new associations.

Problem Structure

Anything that has parts also has a structure that relates these parts to each other. One of the first steps in doing analysis is to determine an appropriate structure for the analytical problem, so that one can then identify the various parts and begin assembling information on them. Because there are many different kinds of analytical problems, there are also many different ways to structure analysis.

Lists such as Franklin made are one of the simplest structures. An intelligence analyst might make lists of relevant variables, early warning indicators, alternative explanations, possible outcomes, factors a foreign leader will need to take into account when making a decision, or arguments for and against a given explanation or outcome.

Other tools for structuring a problem include outlines, tables, diagrams, trees, and matrices, with many sub-species of each. For example, trees include decision trees and fault trees. Diagrams includes causal diagrams, influence diagrams, flow charts, and cognitive maps.

Chapter 8
Analysis of Competing Hypotheses

Analysis of competing hypotheses, sometimes abbreviated ACH, is a tool to aid judgment on important issues requiring careful weighing of alternative explanations or conclusions. It helps an analyst overcome, or at least minimize, some of the cognitive limitations that make prescient intelligence analysis so difficult to achieve.

ACH is an eight-step procedure grounded in basic insights from cognitive

psychology, decision analysis, and the scientific method. It is a surprisingly effective, proven process that helps analysts avoid common analytic pitfalls. Because of its thoroughness, it is particularly appropriate for controversial issues when analysts want to leave an audit trail to show what they considered and how they arrived at their judgement.

When working on difficult intelligence issues, analysts are, in effect, choosing among several alternative hypotheses. Which of several possible explanations is the correct one? Which of several possible outcomes is the most likely one? As previously noted, this book uses the term “hypothesis” in its broadest sense as a potential explanation or conclusion that is to be tested by collecting and presenting evidence.

Analysis of competing hypotheses (ACH) requires an analyst to explicitly identify all the reasonable alternatives and have them compete against each other for the analyst’s favor, rather than evaluating their plausibility one at a time.

The way most analysts go about their business is to pick out what they suspect intuitively is the most likely answer, then look at the available information from the point of view of whether or not it supports this answer. If the evidence seems to support the favorite hypothesis, analysts pat themselves on the back (“See, I knew it all along!”) and look no further. If it does not, they either reject the evidence as misleading or develop another hypothesis and go through the same procedure again.

Simultaneous evaluation of multiple, competing hypotheses is very difficult to do. To retain three to five or even seven hypotheses in working memory and note how each item of information fits into each hypothesis is beyond the mental capabilities of most people. It takes far greater mental agility than listing evidence supporting a single hypothesis that was pre-judged as the most likely answer. It can be accomplished, though, with the help of the simple procedures discussed here. The box below contains a step-by-step outline of the ACH process.

Step 1

Identify the possible hypotheses to be considered. Use a group of analysts with different perspectives to brainstorm the possibilities.

Step-by-Step Outline of Analysis of Competing Hypotheses

1. Identify the possible hypotheses to be considered. Use a group of analysts with different perspectives to brainstorm the possibilities.
2. Make a list of significant evidence and arguments for and against each hypothesis.
3. Prepare a matrix with hypotheses across the top and evidence down the side. Analyze the “diagnosticity” of the evidence and arguments–that is, identify which items are most helpful in judging the relative likelihood of the hypotheses.
4. Refine the matrix. Reconsider the hypotheses and delete evidence and arguments that have no diagnostic value.
5. Draw tentative conclusions about the relative likelihood of each hypothesis. Proceed by trying to disprove the hypotheses rather than prove them.
6. Analyze how sensitive your conclusion is to a few critical items of evidence. Consider the consequences for your analysis if that evidence were wrong, misleading, or subject to a different interpretation.
7. Report conclusions. Discuss the relative likelihood of all the hypotheses, not just the most likely one.
8. Identify milestones for future observation that may indicate events are taking a different course than expected.

It is useful to make a clear distinction between the hypothesis generation and hypothesis evaluation stages of analysis. Step 1 of the recommended analytical process is to identify all hypotheses that merit detailed examination. At this early hypothesis generation stage, it is very useful to bring together a group of analysts with different backgrounds and perspectives. Brainstorming in a group stimulates the imagination and may bring out possibilities that individual members of the group had not thought of. Initial discussion in the group should elicit every possibility, no matter how remote, before judging likelihood or feasibility. Only when all the possibilities are on the table should you then focus on judging them and selecting the hypotheses to be examined in greater detail in subsequent analysis.

Early rejection of unproven, but not disproved, hypotheses biases the subsequent analysis, because one does not then look for the evidence that might support them. Unproven hypotheses should be kept alive until they can be disproved.

Step 2

Make a list of significant evidence and arguments for and against each hypothesis.

In assembling the list of relevant evidence and arguments, these terms should be interpreted very broadly. They refer to all the factors that have an impact on your judgments about the hypotheses. Do not limit yourself to concrete evidence in the current intelligence reporting. Also include your own assumptions or logical deductions about another person’s or group’s or country’s intentions, goals, or standard procedures. These assumptions may generate strong preconceptions as to which hypothesis is most likely. Such assumptions often drive your final judgment, so it is important to include them in the list of “evidence.”

First, list the general evidence that applies to all the hypotheses. Then consider each hypothesis individually, listing factors that tend to support or contradict each one. You will commonly find that each hypothesis leads you to ask different questions and, therefore, to seek out somewhat different evidence.

Step 3

Prepare a matrix with hypotheses across the top and evidence down the side. Analyze the “diagnosticity” of the evidence and arguments- that is, identify which items are most helpful in judging the relative likelihood of alternative hypotheses.

Step 3 is perhaps the most important element of this analytical procedure. It is also the step that differs most from the natural, intuitive approach to analysis, and, therefore, the step you are most likely to overlook or misunderstand.

The procedure for Step 3 is to take the hypotheses from Step 1 and the evidence and arguments from Step 2 and put this information into a matrix format, with the hypotheses across the top and evidence and arguments down the side. This gives an overview of all the significant components of your analytical problem.

Then analyze how each piece of evidence relates to each hypothesis. This differs from the normal procedure, which is to look at one hypothesis at a time in order to consider how well the evidence supports that hypothesis. That will be done later, in Step 5. At this point, in Step 3, take one item of evidence at a time, then consider how consistent that evidence is with each of the hypotheses. Here is how to remember this distinction. In Step 3, you work acrossthe rows of the matrix, examining one item of evidence at a time to see how consistent that item of evidence is with each of the hypotheses. In Step 5, you work down the columns of the matrix, examining one hypothesis at a time, to see how consistent that hypothesis is with all the evidence.

To fill in the matrix, take the first item of evidence and ask whether it is consistent with, inconsistent with, or irrelevant to each hypothesis. Then make a notation accordingly in the appropriate cell under each hypothesis in the matrix. The form of these notations in the matrix is a matter of personal preference. It may be pluses, minuses, and question marks. It may be C, I, and N/A standing for consistent, inconsistent, or not applicable. Or it may be some textual notation. In any event, it will be a simplification, a shorthand representation of the complex reasoning that went on as you thought about how the evidence relates to each hypothesis.

After doing this for the first item of evidence, then go on to the next item of evidence and repeat the process until all cells in the matrix are filled

The matrix format helps you weigh the diagnosticity of each item of evidence, which is a key difference between analysis of competing hypotheses and traditional analysis.

Evidence is diagnostic when it influences your judgment on the relative likelihood of the various hypotheses identified in Step 1. If an item of evidence seems consistent with all the hypotheses, it may have no diagnostic value. A common experience is to discover that most of the evidence supporting what you believe is the most likely hypothesis really is not very helpful, because that same evidence is also consistent with other hypotheses. When you do identify items that are highly diagnostic, these should drive your judgment.

Step 4

Refine the matrix. Reconsider the hypotheses and delete evidence and arguments that have no diagnostic value.

The exact wording of the hypotheses is obviously critical to the conclusions one can draw from the analysis. By this point, you will have seen how the evidence breaks out under each hypothesis, and it will often be appropriate to reconsider and reword the hypotheses. Are there hypotheses that need to be added, or finer distinctions that need to be made in order to consider all the significant alternatives? If there is little or no evidence that helps distinguish between two hypotheses, should they be combined into one?

Also reconsider the evidence. Is your thinking about which hypotheses are most likely and least likely influenced by factors that are not included in the listing of evidence? If so, put them in. Delete from the matrix items of evidence or assumptions

that now seem unimportant or have no diagnostic value. Save these items in a separate list as a record of information that was considered.

Step 5

Draw tentative conclusions about the relative likelihood of each hypothesis. Proceed by trying to disprove hypotheses rather than prove them.

In Step 3, you worked across the matrix, focusing on a single item of evidence or argument and examining how it relates to each hypothesis. Now, work down the matrix, looking at each hypothesis as a whole. The matrix format gives an overview of all the evidence for and against all the hypotheses, so that you can examine all the hypotheses together and have them compete against each other for your favor.

In evaluating the relative likelihood of alternative hypotheses, start by looking for evidence or logical deductions that enable you to reject hypotheses, or at least to determine that they are unlikely. A fundamental precept of the scientific method is to proceed by rejecting or eliminating hypotheses, while tentatively accepting only those hypotheses that cannot be refuted. The scientific method obviously cannot be applied in toto to intuitive judgment, but the principle of seeking to disprove hypotheses, rather than confirm them, is useful.

No matter how much information is consistent with a given hypothesis, one cannot prove that hypothesis is true, because the same information may also be consistent with one or more other hypotheses. On the other hand, a single item of evidence that is inconsistent with a hypothesis may be sufficient grounds for rejecting that hypothesis.

People have a natural tendency to concentrate on confirming hypotheses they already believe to be true, and they commonly give more weight to information that supports a hypothesis than to information that weakens it. This is wrong; we should do just the opposite. Step 5 again requires doing the opposite of what comes naturally.

In examining the matrix, look at the minuses, or whatever other notation you used to indicate evidence that may be inconsistent with a hypothesis. The hypotheses with the fewest minuses is probably the most likely one. The hypothesis with the most minuses is probably the least likely one. The fact that a hypothesis is inconsistent with the evidence is certainly a sound basis for rejecting it. The pluses, indicating evidence that is consistent with a hypothesis, are far less significant. It does not follow that the hypothesis with the most pluses is the most likely one, because a long list of evidence that is consistent with almost any reasonable hypothesis can be easily made. What is difficult to find, and is most significant when found, is hard evidence that is clearly inconsistent with a reasonable hypothesis.

The matrix should not dictate the conclusion to you. Rather, it should accurately reflect your judgment of what is important and how these important factors relate to the probability of each hypothesis. You, not the matrix, must make the decision. The matrix serves only as an aid to thinking and analysis, to ensure consideration of all the possible interrelationships between evidence and hypotheses and identification of those few items that really swing your judgment on the issue.

If following this procedure has caused you to consider things you might otherwise have overlooked, or has caused you to revise your earlier estimate of the relative probabilities of the hypotheses, then the procedure has served a useful purpose. When you are done, the matrix serves as a shorthand record of your thinking and as an audit trail showing how you arrived at your conclusion.

This procedure forces you to spend more analytical time than you otherwise would on what you had thought were the less likely hypotheses. This is desirable. The seemingly less likely hypotheses usually involve plowing new ground and, therefore, require more work. What you started out thinking was the most likely hypothesis tends to be based on a continuation of your own past thinking. A principal advantage of the analysis of competing hypotheses is that it forces you to give a fairer shake to all the alternatives.

Step 6

Analyze how sensitive your conclusion is to a few critical items of evidence.

Consider the consequences for your analysis if that evidence were wrong, misleading, or subject to a different interpretation.

If there is any concern at all about denial and deception, this is an appropriate place to consider that possibility. Look at the sources of your key evidence. Are any of the sources known to the authorities in the foreign country? Could the information have been manipulated? Put yourself in the shoes of a foreign deception planner to evaluate motive, opportunity, means, costs, and benefits of deception as they might appear to the foreign country.

When analysis turns out to be wrong, it is often because of key assumptions that went unchallenged and proved invalid. It is a truism that analysts should identify and question assumptions, but this is much easier said than done. The problem is to determine which assumptions merit questioning. One advantage of the ACH procedure is that it tells you what needs to be rechecked.

In Step 6 you may decide that additional research is needed to check key judgments. For example, it may be appropriate to go back to check original source materials rather than relying on someone else’s interpretation. In writing your report, it is desirable to identify critical assumptions that went into your interpretation and to note that your conclusion is dependent upon the validity of these assumptions.

Step 7

Report conclusions. Discuss the relative likelihood of all the hypotheses, not just the most likely one.

If your report is to be used as the basis for decisionmaking, it will be helpful for the decisionmaker to know the relative likelihood of all the alternative possibilities. Analytical judgments are never certain. There is always a good possibility of their being wrong. Decisionmakers need to make decisions on the basis of a full set of alternative possibilities, not just the single most likely alternative. Contingency or fallback plans may be needed in case one of the less likely alternatives turns out to be true.

When one recognizes the importance of proceeding by eliminating rather than confirming hypotheses, it becomes apparent that any written argument for a certain judgment is incomplete unless it also discusses alternative judgments that were considered and why they were rejected. In the past, at least, this was seldom done.

Step 8

Identify milestones for future observation that may indicate events are taking a different course than expected.

Analytical conclusions should always be regarded as tentative. The situation may change, or it may remain unchanged while you receive new information that alters your appraisal. It is always helpful to specify in advance things one should look for or be alert to that, if observed, would suggest a significant change in the probabilities. This is useful for intelligence consumers who are following the situation on a continuing basis. Specifying in advance what would cause you to change your mind will also make it more difficult for you to rationalize such developments, if they occur, as not really requiring any modification of your judgment.

Summary and Conclusion

Three key elements distinguish analysis of competing hypotheses from conventional intuitive analysis.

• Analysis starts with a full set of alternative possibilities, rather than with a most likely alternative for which the analyst seeks confirmation. This ensures that alternative hypotheses receive equal treatment and a fair shake.
• Analysis identifies and emphasizes the few items of evidence or assumptions that have the greatest diagnostic value in judging the relative likelihood of the alternative hypotheses. In conventional intuitive analysis, the fact that key evidence may also be consistent with alternative hypotheses is rarely considered explicitly and often ignored.
• Analysis of competing hypotheses involves seeking evidence to refute hypotheses. The most probable hypothesis is usually the one with the least evidence against it, not the one with the most evidence for it. Conventional analysis generally entails looking for evidence to confirm a favored hypothesis.

A principal lesson is this. Whenever an intelligence analyst is tempted to write the phrase “there is no evidence that …,” the analyst should ask this question: If this hypothesis is true, can I realistically expect to see evidence of it? In other words, if India were planning nuclear tests while deliberately concealing its intentions, could the analyst realistically expect to see evidence of test planning? The ACH procedure leads the analyst to identify and face these kinds of questions.

There is no guarantee that ACH or any other procedure will produce a correct answer. The result, after all, still depends on fallible intuitive judgment applied to incomplete and ambiguous information. Analysis of competing hypotheses does, however, guarantee an appropriate process of analysis. This procedure leads you through a rational, systematic process that avoids some common analytical pitfalls. It increases the odds of getting the right answer, and it leaves an audit trail showing the evidence used in your analysis and how this evidence was interpreted. If others disagree with your judgment, the matrix can be used to highlight the precise area of disagreement. Subsequent discussion can then focus productively on the ultimate source of the differences.

The ACH procedure has the offsetting advantage of focusing attention on the few items of critical evidence that cause the uncertainty or which, if they were available, would alleviate it. This can guide future collection, research, and analysis to resolve the uncertainty and produce a more accurate judgment.

PART THREE–COGNITIVE BIASES

Chapter 9
What Are Cognitive Biases?

This mini-chapter discusses the nature of cognitive biases in general. The four chapters that follow it describe specific cognitive biases in the evaluation of evidence, perception of cause and effect, estimation of probabilities, and evaluation of intelligence reporting.

Cognitive biases are mental errors caused by our simplified information processing strategies. It is important to distinguish cognitive biases from other forms of bias, such as cultural bias, organizational bias, or bias that results from one’s own self- interest. In other words, a cognitive bias does not result from any emotional or intellectual predisposition toward a certain judgment, but rather from subconscious mental procedures for processing information. A cognitive bias is a mental error that is consistent and predictable.

Cognitive biases are similar to optical illusions in that the error remains compelling even when one is fully aware of its nature. Awareness of the bias, by itself, does not produce a more accurate perception. Cognitive biases, therefore, are, exceedingly difficult to overcome.

Chapter 10
Biases in Evaluation of Evidence

Evaluation of evidence is a crucial step in analysis, but what evidence people rely on and how they interpret it are influenced by a variety of extraneous factors. Information presented in vivid and concrete detail often has unwarranted impact, and people tend to disregard abstract or statistical information that may have greater evidential value. We seldom take the absence of evidence into account. The human mind is also oversensitive to the consistency of the evidence, and insufficiently sensitive to the reliability of the evidence. Finally, impressions often remain even after the evidence on which they are based has been totally discredited.

The intelligence analyst works in a somewhat unique informational environment. Evidence comes from an unusually diverse set of sources: newspapers and wire services, observations by American Embassy officers, reports from controlled agents and casual informants, information exchanges with foreign governments, photo reconnaissance, and communications intelligence. Each source has its own unique strengths, weaknesses, potential or actual biases, and vulnerability to manipulation and deception. The most salient characteristic of the information environment is its diversity–multiple sources, each with varying degrees of reliability, and each commonly reporting information which by itself is incomplete and sometimes inconsistent or even incompatible with reporting from other sources. Conflicting information of uncertain reliability is endemic to intelligence analysis, as is the need to make rapid judgments on current events even before all the evidence is in.

The Vividness Criterion

The impact of information on the human mind is only imperfectly related to its true value as evidence. Specifically, information that is vivid, concrete, and personal has a greater impact on our thinking than pallid, abstract information that may actually have substantially greater value as evidence. For example:

• Information that people perceive directly, that they hear with their own ears or see with their own eyes, is likely to have greater impact than information received secondhand that may have greater evidential value.
• Case histories and anecdotes will have greater impact than more informative but abstract aggregate or statistical data.

Events that people experience personally are more memorable than those they only read about. Concrete words are easier to remember than abstract words, and words of all types are easier to recall than numbers. In short, information having the qualities cited in the preceding paragraph is more likely to attract and hold our attention. It is more likely to be stored and remembered than abstract reasoning or statistical summaries, and therefore can be expected to have a greater immediate effect as well as a continuing impact on our thinking in the future.

Personal observations by intelligence analysts and agents can be as deceptive as secondhand accounts. Most individuals visiting foreign countries become familiar with only a small sample of people representing a narrow segment of the total society. Incomplete and distorted perceptions are a common result.

a “man-who” example seldom merits the evidential weight intended by the person citing the example, or the weight often accorded to it by the recipient.

The most serious implication of vividness as a criterion that determines the impact of evidence is that certain kinds of very valuable evidence will have little influence simply because they are abstract. Statistical data, in particular, lack the rich and concrete detail to evoke vivid images, and they are often overlooked, ignored, or minimized.

For example, the Surgeon General’s report linking cigarette smoking to cancer should have, logically, caused a decline in per-capita cigarette consumption. No such decline occurred for more than 20 years. The reaction of physicians was particularly informative. All doctors were aware of the statistical evidence and were more exposed than the general population to the health problems caused by smoking. How they reacted to this evidence depended upon their medical specialty. Twenty years after the Surgeon General’s report, radiologists who examine lung x-rays every day had the lowest rate of smoking. Physicians who diagnosed and treated lung cancer victims were also quite unlikely to smoke. Many other types of physicians continued to smoke. The probability that a physician continued to smoke was directly related to the distance of the physician’s specialty from the lungs. In other words, even physicians, who were well qualified to understand and appreciate the statistical data, were more influenced by their vivid personal experiences than by valid statistical data.

Absence of Evidence

A principal characteristic of intelligence analysis is that key information is often lacking. Analytical problems are selected on the basis of their importance and the perceived needs of the consumers, without much regard for availability of information. Analysts have to do the best they can with what they have, somehow taking into account the fact that much relevant information is known to be missing.

Ideally, intelligence analysts should be able to recognize what relevant evidence is lacking and factor this into their calculations. They should also be able to estimate the potential impact of the missing data and to adjust confidence in their judgment accordingly. Unfortunately, this ideal does not appear to be the norm. Experiments suggest that “out of sight, out of mind” is a better description of the impact of gaps in the evidence.

This problem has been demonstrated using fault trees, which are schematic drawings showing all the things that might go wrong with any endeavor. Fault trees are often used to study the fallibility of complex systems such as a nuclear reactor or space capsule.

Missing data is normal in intelligence problems, but it is probably more difficult to recognize that important information is absent and to incorporate this fact into judgments on intelligence questions than in the more concrete “car won’t start” experiment.

Oversensitivity to Consistency

The internal consistency in a pattern of evidence helps determine our confidence in judgments based on that evidence. In one sense, consistency is clearly an appropriate guideline for evaluating evidence. People formulate alternative explanations or estimates and select the one that encompasses the greatest amount of evidence within a logically consistent scenario. Under some circumstances, however, consistency can be deceptive. Information may be consistent only because it is highly correlated or redundant, in which case many related reports may be no more informative than a single report. Or it may be consistent only because information is drawn from a very small sample or a biased sample.

If the available evidence is consistent, analysts will often overlook the fact that it represents a very small and hence unreliable sample taken from a large and heterogeneous group. This is not simply a matter of necessity– of having to work with the information on hand, however imperfect it may be. Rather, there is an illusion of validity caused by the consistency of the information.

The tendency to place too much reliance on small samples has been dubbed the “law of small numbers.” This is a parody on the law of large numbers, the basic statistical principle that says very large samples will be highly representative of the population from which they are drawn. This is the principle that underlies opinion polling, but most people are not good intuitive statisticians. People do not have much intuitive feel for how large a sample has to be before they can draw valid conclusions from it. The so-called law of small numbers means that, intuitively, we make the mistake of treating small samples as though they were large ones.

Coping with Evidence of Uncertain Accuracy

There are many reasons why information often is less than perfectly accurate: misunderstanding, misperception, or having only part of the story; bias on the part of the ultimate source; distortion in the reporting chain from subsource through source, case officer, reports officer, to analyst; or misunderstanding and misperception by the analyst. Further, much of the evidence analysts bring to bear in conducting analysis is retrieved from memory, but analysts often cannot remember even the source of information they have in memory let alone the degree of certainty they attributed to the accuracy of that information when it was first received.

The human mind has difficulty coping with complicated probabilistic relationships,

so people tend to employ simple rules of thumb that reduce the burden of processing such information. In processing information of uncertain accuracy or reliability, analysts tend to make a simple yes or no decision. If they reject the evidence, they tend to reject it fully, so it plays no further role in their mental calculations. If they accept the evidence, they tend to accept it wholly, ignoring the probabilistic nature of the accuracy or reliability judgment. This is called a “best guess” strategy.

A more sophisticated strategy is to make a judgment based on an assumption that the available evidence is perfectly accurate and reliable, then reduce the confidence in this judgment by a factor determined by the assessed validity of the information. For example, available evidence may indicate that an event probably (75 percent) will occur, but the analyst cannot be certain that the evidence on which this judgment is based is wholly accurate or reliable.

The same processes may also affect our reaction to information that is plausible but known from the beginning to be of questionable authenticity. Ostensibly private statements by foreign officials are often reported though intelligence channels. In many instances it is not clear whether such a private statement by a foreign ambassador, cabinet member, or other official is an actual statement of private views, an indiscretion, part of a deliberate attempt to deceive the US Government, or part of an approved plan to convey a truthful message that the foreign government believes is best transmitted through informal channels.

Knowing that the information comes from an uncontrolled source who may be trying to manipulate us does not necessarily reduce the impact of the information.

Persistence of Impressions Based on Discredited

Evidence

Impressions tend to persist even after the evidence that created those impressions has been fully discredited. Psychologists have become interested in this phenomenon because many of their experiments require that the test subjects be deceived. For example, test subjects may be made to believe they were successful or unsuccessful in performing some task, or that they possess certain abilities or personality traits, when this is not in fact the case. Professional ethics require that test subjects be disabused of these false impressions at the end of the experiment, but this has proved surprisingly difficult to achieve.

Test subjects’ erroneous impressions concerning their logical problem-solving abilities persevered even after they were informed that manipulation of good or poor teaching performance had virtually guaranteed their success or failure.

An interesting but speculative explanation is based on the strong tendency to seek causal explanations, as discussed in the next chapter. When evidence is first received, people postulate a set of causal connections that explains this evidence.

The stronger the perceived causal linkage, the stronger the impression created by the evidence.

Colloquially, one might say that once information rings a bell, the bell cannot be unrung.

The ambiguity of most real-world situations contributes to the operation of this perseverance phenomenon. Rarely in the real world is evidence so thoroughly discredited as is possible in the experimental laboratory. Imagine, for example, that you are told that a clandestine source who has been providing information for some time is actually under hostile control.

Chapter 11
Biases in Perception of Cause and Effect

Judgments about cause and effect are necessary to explain the past, understand the present, and estimate the future. These judgments are often biased by factors over which people exercise little conscious control, and this can influence many types of judgments made by intelligence analysts. Because of a need to impose order on our environment, we seek and often believe we find causes for what are actually accidental or random phenomena. People overestimate the extent to which other countries are pursuing a coherent, coordinated, rational plan, and thus also overestimate their own ability to predict future events in those nations. People also tend to assume that causes are similar to their effects, in the sense that important or large effects must have large causes.

When inferring the causes of behavior, too much weight is accorded to personal qualities and dispositions of the actor and not enough to situational determinants of the actor’s behavior. People also overestimate their own importance as both a cause and a target of the behavior of others. Finally, people often perceive relationships that do not in fact exist, because they do not have an intuitive understanding of the kinds and amount of information needed to prove a relationship.

There are several modes of analysis by which one might infer cause and effect. In more formal analysis, inferences are made through procedures that collectively comprise the scientific method. The scientist advances a hypothesis, then tests this hypothesis by the collection and statistical analysis of data on many instances of the phenomenon in question. Even then, causality cannot be proved beyond all possible doubt. The scientist seeks to disprove a hypothesis, not to confirm it. A hypothesis is accepted only when it cannot be rejected.

Collection of data on many comparable cases to test hypotheses about cause and effect is not feasible for most questions of interest to the Intelligence Community, especially questions of broad political or strategic import relating to another country’s intentions. To be sure, it is feasible more often than it is done, and increased use of scientific procedures in political, economic, and strategic research is much to be encouraged. But the fact remains that the dominant approach to intelligence analysis is necessarily quite different. It is the approach of the historian rather than the scientist, and this approach presents obstacles to accurate inferences about causality.

The key ideas here are coherence and narrative. These are the principles that guide the organization of observations into meaningful structures and patterns. The historian commonly observes only a single case, not a pattern of covariation (when two things are related so that change in one is associated with change in the other) in many comparable cases. Moreover, the historian observes simultaneous changes in so many variables that the principle of covariation generally is not helpful in sorting out the complex relationships among them. The narrative story, on the other hand, offers a means of organizing the rich complexity of the historian’s observations. The historian uses imagination to construct a coherent story out of fragments of data.

The intelligence analyst employing the historical mode of analysis is essentially a storyteller.

He or she constructs a plot from the previous events, and this plot then dictates the possible endings of the incomplete story. The plot is formed of the “dominant concepts or leading ideas” that the analyst uses to postulate patterns of relationships among the available data. The analyst is not, of course, preparing a work of fiction. There are constraints on the analyst’s imagination, but imagination is nonetheless involved because there is an almost unlimited variety of ways in which the available data might be organized to tell a meaningful story. The constraints are the available evidence and the principle of coherence. The story must form a logical and coherent whole and be internally consistent as well as consistent with the available evidence.

Recognizing that the historical or narrative mode of analysis involves telling a coherent story helps explain the many disagreements among analysts, inasmuch as coherence is a subjective concept. It assumes some prior beliefs or mental model about what goes with what. More relevant to this discussion, the use of coherence rather than scientific observation as the criterion for judging truth leads to biases that presumably influence all analysts to some degree. Judgments of coherence may be influenced by many extraneous factors, and if analysts tend to favor certain types of explanations as more coherent than others, they will be biased in favor of those explanations.

Bias in Favor of Causal Explanations

One bias attributable to the search for coherence is a tendency to favor causal explanations. Coherence implies order, so people naturally arrange observations into regular patterns and relationships. If no pattern is apparent, our first thought is that we lack understanding, not that we are dealing with random phenomena that have no purpose or reason.

These examples suggest that in military and foreign affairs, where the patterns are at best difficult to fathom, there may be many events for which there are no valid causal explanations. This certainly affects the predictability of events and suggests limitations on what might logically be expected of intelligence analysts.

Bias Favoring Perception of Centralized Direction

Very similar to the bias toward causal explanations is a tendency to see the actions of other governments (or groups of any type) as the intentional result of centralized direction and planning. “…most people are slow to perceive accidents, unintended consequences, coincidences, and small causes leading to large effects. Instead, coordinated actions, plans and conspiracies are seen.” Analysts overestimate the extent to which other countries are pursuing coherent, rational, goal-maximizing policies, because this makes for more coherent, logical, rational explanations. This bias also leads analysts and policymakers alike to overestimate the predictability of future events in other countries.

But a focus on such causes implies a disorderly world in which outcomes are determined more by chance than purpose. It is especially difficult to incorporate these random and usually unpredictable elements into a coherent narrative, because evidence is seldom available to document them on a timely basis. It is only in historical perspective, after memoirs are written and government documents released, that the full story becomes available.

This bias has important consequences. Assuming that a foreign government’s actions result from a logical and centrally directed plan leads an analyst to:

• Have expectations regarding that government’s actions that may not be fulfilled if the behavior is actually the product of shifting or inconsistent values, bureaucratic bargaining, or sheer confusion and blunder.
• Draw far-reaching but possibly unwarranted inferences from isolated statements or actions by government officials who may be acting on their own rather than on central direction.
• Overestimate the United States’ ability to influence the other government’s actions.
• Perceive inconsistent policies as the result of duplicity and Machiavellian maneuvers, rather than as the product of weak leadership, vacillation, or bargaining among diverse bureaucratic or political interests.

Similarity of Cause and Effect

When systematic analysis of covariation is not feasible and several alternative causal explanations seem possible, one rule of thumb people use to make judgments of cause and effect is to consider the similarity between attributes of the cause and attributes of the effect. Properties of the cause are “…inferred on the basis of being correspondent with or similar to properties of the effect.” Heavy things make heavy noises; dainty things move daintily; large animals leave large tracks. When dealing with physical properties, such inferences are generally correct.

The tendency to reason according to similarity of cause and effect is frequently found in conjunction with the previously noted bias toward inferring centralized direction. Together, they explain the persuasiveness of conspiracy theories. Such theories are invoked to explain large effects for which there do not otherwise appear to be correspondingly large causes.

Intelligence analysts are more exposed than most people to hard evidence of real plots, coups, and conspiracies in the international arena. Despite this–or perhaps because of it–most intelligence analysts are not especially prone to what are generally regarded as conspiracy theories. Although analysts may not exhibit this bias in such extreme form, the bias presumably does influence analytical judgments in myriad little ways. In examining causal relationships, analysts generally construct causal explanations that are somehow commensurate with the magnitude of their effects and that attribute events to human purposes or predictable forces rather than to human weakness, confusion, or unintended consequences.

Internal vs. External Causes of Behavior

Much research into how people assess the causes of behavior employs a basic dichotomy between internal determinants and external determinants of human actions. Internal causes of behavior include a person’s attitudes, beliefs, and personality. External causes include incentives and constraints, role requirements, social pressures, or other forces over which the individual has little control. The research examines the circumstances under which people attribute behavior either to stable dispositions of the actor or to characteristics of the situation to which the actor responds.

Differences in judgments about what causes another person’s or government’s behavior affect how people respond to that behavior. How people respond to friendly or unfriendly actions by others may be quite different if they attribute the behavior to the nature of the person or government than if they see the behavior as resulting from situational constraints over which the person or government has little control.

Not enough weight is assigned to external circumstances that may have influenced the other person’s choice of behavior. This pervasive tendency has been demonstrated in many experiments under quite diverse circumstances and has often been observed in diplomatic and military interactions.

Susceptibility to this biased attribution of causality depends upon whether people are examining their own behavior or observing that of others. It is the behavior of others that people tend to attribute to the nature of the actor, whereas they see their own behavior as conditioned almost entirely by the situation in which they find themselves. This difference is explained largely by differences in information available to actors and observers. People know a lot more about themselves.

The actor has a detailed awareness of the history of his or her own actions under similar circumstances. In assessing the causes of our own behavior, we are likely to consider our previous behavior and focus on how it has been influenced by different situations. Thus situational variables become the basis for explaining our own behavior. This contrasts with the observer, who typically lacks this detailed knowledge of the other person’s past behavior. The observer is inclined to focus on how the other person’s behavior compares with the behavior of others under similar circumstances.

This difference in the type and amount of information available to actors and observers applies to governments as well as people. An actor’s personal involvement with the actions being observed enhances the likelihood of bias. “Where the observer is also an actor, he is likely to exaggerate the uniqueness and emphasize the dispositional origins of the responses of others to his own actions.”

The persistent tendency to attribute cause and effect in this manner is not simply the consequence of self-interest or propaganda by the opposing sides. Rather, it is the readily understandable and predictable result of how people normally attribute causality under many different circumstances.

As a general rule, biased attribution of causality helps sow the seeds of mistrust and misunderstanding between people and between governments. We tend to have quite different perceptions of the causes of each other’s behavior.

Overestimating Our Own Importance

Individuals and governments tend to overestimate the extent to which they successfully influence the behavior of others. This is an exception to the previously noted generalization that observers attribute the behavior of others to the nature of the actor. It occurs largely because a person is so familiar with his or her own efforts to influence another, but much less well informed about other factors that may have influenced the other’s decision.

In estimating the influence of US policy on the actions of another government, analysts more often than not will be knowledgeable of US actions and what they are intended to achieve, but in many instances they will be less well informed concerning the internal processes, political pressures, policy conflicts, and other influences on the decision of the target government.

Illusory Correlation

At the start of this chapter, covariation was cited as one basis for inferring causality. It was noted that covariation may either be observed intuitively or measured statistically. This section examines the extent to which the intuitive perception of covariation deviates from the statistical measurement of covariation.

Statistical measurement of covariation is known as correlation. Two events are correlated when the existence of one event implies the existence of the other. Variables are correlated when a change in one variable implies a similar degree of change in another. Correlation alone does not necessarily imply causation. For example, two events might co-occur because they have a common cause, rather than because one causes the other. But when two events or changes do co-occur, and the time sequence is such that one always follows the other, people often infer that the first caused the second. Thus, inaccurate perception of correlation leads to inaccurate perception of cause and effect.

Judgments about correlation are fundamental to all intelligence analysis. For example, assumptions that worsening economic conditions lead to increased political support for an opposition party, that domestic problems may lead to foreign adventurism, that military government leads to unraveling of democratic institutions, or that negotiations are more successful when conducted from a position of strength are all based on intuitive judgments of correlation between these variables. In many cases these assumptions are correct, but they are seldom tested by systematic observation and statistical analysis.

Much intelligence analysis is based on common-sense assumptions about how people and governments normally behave. The problem is that people possess a great facility for invoking contradictory “laws” of behavior to explain, predict, or justify different actions occurring under similar circumstances. “Haste makes waste” and “He who hesitates is lost” are examples of inconsistent explanations and admonitions. They make great sense when used alone and leave us looking foolish when presented together. “Appeasement invites aggression” and “agreement is based upon compromise” are similarly contradictory expressions.

When confronted with such apparent contradictions, the natural defense is that “it all depends on. …” Recognizing the need for such qualifying statements is one of the differences between subconscious information processing and systematic, self- conscious analysis. Knowledgeable analysis might be identified by the ability to fill in the qualification; careful analysis by the frequency with which one remembers to do so.

Of the 86 test subjects involved in several runnings of this experiment, not a single one showed any intuitive understanding of the concept of correlation. That is, no one understood that to make a proper judgment about the existence of a relationship, one must have information on all four cells of the table.

Let us now consider a similar question of correlation on a topic of interest to intelligence analysts. What are the characteristics of strategic deception and how can analysts detect it? In studying deception, one of the important questions is: what are the correlates of deception? Historically, when analysts study instances of deception, what else do they see that goes along with it, that is somehow related to deception, and that might be interpret as an indicator of deception? Are there certain practices relating to deception, or circumstances under which deception is most likely to occur, that permit one to say, that, because we have seen x or y or z, this most likely means a deception plan is under way? This would be comparable to a doctor observing certain symptoms and concluding that a given disease may be present. This is essentially a problem of correlation. If one could identify several correlates of deception, this would significantly aid efforts to detect it.

The lesson to be learned is not that analysts should do a statistical analysis of every relationship. They usually will not have the data, time, or interest for that. But analysts should have a general understanding of what it takes to know whether a relationship exists. This understanding is definitely not a part of people’s intuitive knowledge. It does not come naturally. It has to be learned. When dealing with such issues, analysts have to force themselves to think about all four cells of the table and the data that would be required to fill each cell.

Even if analysts follow these admonitions, there are several factors that distort judgment when one does not follow rigorous scientific procedures in making and recording observations. These are factors that influence a person’s ability to recall examples that fit into the four cells. For example, people remember occurrences more readily than non-occurrences. “History is, by and large, a record of what people did, not what they failed to do.”

Many erroneous theories are perpetuated because they seem plausible and because people record their experience in a way that supports rather than refutes them.

Chapter 12
Biases in Estimating Probabilities

In making rough probability judgments, people commonly depend upon one of several simplified rules of thumb that greatly ease the burden of decision. Using the “availability” rule, people judge the probability of an event by the ease with which they can imagine relevant instances of similar events or the number of such events that they can easily remember. With the “anchoring” strategy, people pick some natural starting point for a first approximation and then adjust this figure based on the results of additional information or analysis. Typically, they do not adjust the initial judgment enough.

Expressions of probability, such as possible and probable, are a common source of ambiguity that make it easier for a reader to interpret a report as consistent with the reader’s own preconceptions. The probability of a scenario is often miscalculated. Data on “prior probabilities” are commonly ignored unless they illuminate causal relationships.

Availability Rule

One simplified rule of thumb commonly used in making probability estimates is known as the availability rule. In this context, “availability” refers to imaginability or retrievability from memory. Psychologists have shown that two cues people use unconsciously in judging the probability of an event are the ease with which they can imagine relevant instances of the event and the number or frequency of such events that they can easily remember. People are using the availability rule of thumb whenever they estimate frequency or probability on the basis of how easily they can recall or imagine instances of whatever it is they are trying to estimate.

people are frequently led astray when the ease with which things come to mind is influenced by factors unrelated to their probability. The ability to recall instances of an event is influenced by how recently the event occurred, whether we were personally involved, whether there were vivid and memorable details associated with the event, and how important it seemed at the time. These and other factors that influence judgment are unrelated to the true probability of an event.

Intelligence analysts may be less influenced than others by the availability bias. Analysts are evaluating all available information, not making quick and easy inferences. On the other hand, policymakers and journalists who lack the time or access to evidence to go into details must necessarily take shortcuts. The obvious shortcut is to use the availability rule of thumb for making inferences about probability.

Many events of concern to intelligence analysts

…are perceived as so unique that past history does not seem relevant to the evaluation of their likelihood. In thinking of such events we often construct scenarios, i.e., stories that lead from the present situation to the target event. The plausibility of the scenarios that come to mind, or the difficulty of producing them, serve as clues to the likelihood of the event. If no reasonable scenario comes to mind, the event is deemed impossible or highly unlikely. If several scenarios come easily to mind, or if one scenario is particularly compelling, the event in question appears probable.

Many extraneous factors influence the imaginability of scenarios for future events, just as they influence the retrievability of events from memory. Curiously, one of these is the act of analysis itself. The act of constructing a detailed scenario for a possible future event makes that event more readily imaginable and, therefore, increases its perceived probability. This is the experience of CIA analysts who have used various tradecraft tools that require, or are especially suited to, the analysis of unlikely but nonetheless possible and important hypotheses.

In sum, the availability rule of thumb is often used to make judgments about likelihood or frequency. People would be hard put to do otherwise, inasmuch as it is such a timesaver in the many instances when more detailed analysis is not warranted or not feasible. Intelligence analysts, however, need to be aware when they are taking shortcuts. They must know the strengths and weaknesses of these procedures…

For intelligence analysts, recognition that they are employing the availability rule should raise a caution flag. Serious analysis of probability requires identification and assessment of the strength and interaction of the many variables that will determine the outcome of a situation.

Anchoring

Another strategy people seem to use intuitively and unconsciously to simplify the task of making judgments is called anchoring. Some natural starting point, perhaps from a previous analysis of the same subject or from some partial calculation, is used as a first approximation to the desired judgment. This starting point is then adjusted, based on the results of additional information or analysis. Typically, however, the starting point serves as an anchor or drag that reduces the amount of adjustment, so the final estimate remains closer to the starting point than it ought to be.

Whenever analysts move into a new analytical area and take over responsibility for updating a series of judgments or estimates made by their predecessors, the previous judgments may have such an anchoring effect. Even when analysts make their own initial judgment, and then attempt to revise this judgment on the basis of new information or further analysis, there is much evidence to suggest that they usually do not change the judgment enough.

Anchoring provides a partial explanation of experiments showing that analysts tend to be overly sure of themselves in setting confidence ranges. A military analyst who estimates future missile or tank production is often unable to give a specific figure as a point estimate.

Reasons for the anchoring phenomenon are not well understood. The initial estimate serves as a hook on which people hang their first impressions or the results of earlier calculations. In recalculating, they take this as a starting point rather than starting over from scratch, but why this should limit the range of subsequent reasoning is not clear.

There is some evidence that awareness of the anchoring problem is not an adequate antidote. This is a common finding in experiments dealing with cognitive biases. The biases persist even after test subjects are informed of them and instructed to try to avoid them or compensate for them.

One technique for avoiding the anchoring bias, to weigh anchor so to speak, may be to ignore one’s own or others’ earlier judgments and rethink a problem from scratch.

In other words, consciously avoid any prior judgment as a starting point. There is no experimental evidence to show that this is possible or that it will work, but it seems worth trying. Alternatively, it is sometimes possible to avoid human error by employing formal statistical procedures.

Expression of Uncertainty

Probabilities may be expressed in two ways. Statistical probabilities are based on empirical evidence concerning relative frequencies. Most intelligence judgments deal with one-of-a-kind situations for which it is impossible to assign a statistical probability. Another approach commonly used in intelligence analysis is to make a “subjective probability” or “personal probability” judgment. Such a judgment is an expression of the analyst’s personal belief that a certain explanation or estimate is correct. It is comparable to a judgment that a horse has a three-to-one chance of winning a race.

When intelligence conclusions are couched in ambiguous terms, a reader’s interpretation of the conclusions will be biased in favor of consistency with what the reader already believes.

The main point is that an intelligence report may have no impact on the reader if it is couched in such ambiguous language that the reader can easily interpret it as consistent with his or her own preconceptions. This ambiguity can be especially troubling when dealing with low-probability, high-impact dangers against which policymakers may wish to make contingency plans.

How can analysts express uncertainty without being unclear about how certain they are? Putting a numerical qualifier in parentheses after the phrase expressing degree of uncertainty is an appropriate means of avoiding misinterpretation. This may be an odds ratio (less than a one-in-four chance) or a percentage range (5 to 20 percent) or (less than 20 percent). Odds ratios are often preferable, as most people have a better intuitive understanding of odds than of percentages.

Assessing Probability of a Scenario

Intelligence analysts sometimes present judgments in the form of a scenario–a series of events leading to an anticipated outcome. There is evidence that judgments concerning the probability of a scenario are influenced by amount and nature of detail in the scenario in a way that is unrelated to actual likelihood of the scenario.

A scenario consists of several events linked together in a narrative description. To calculate mathematically the probability of a scenario, the proper procedure is to multiply the probabilities of each individual event. Thus, for a scenario with three events, each of which will probably (70 percent certainty) occur, the probability of the scenario is .70 x .70 x .70 or slightly over 34 percent. Adding a fourth probable (70 percent) event to the scenario would reduce its probability to 24 percent.

Most people do not have a good intuitive grasp of probabilistic reasoning. One approach to simplifying such problems is to assume (or think as though) one or more probable events have already occurred. This eliminates some of the uncertainty from the judgment.

When the averaging strategy is employed, highly probable events in the scenario tend to offset less probable events. This violates the principle that a chain cannot be stronger than its weakest link. Mathematically, the least probable event in a scenario sets the upper limit on the probability of the scenario as a whole. If the averaging strategy is employed, additional details may be added to the scenario that are so plausible they increase the perceived probability of the scenario, while, mathematically, additional events must necessarily reduce its probability.

Base-Rate Fallacy

In assessing a situation, an analyst sometimes has two kinds of evidence available– specific evidence about the individual case at hand, and numerical data that summarize information about many similar cases. This type of numerical information is called a base rate or prior probability. The base-rate fallacy is that the numerical data are commonly ignored unless they illuminate a causal relationship.

Most people do not incorporate the prior probability into their reasoning because it does not seem relevant. It does not seem relevant because there is no causal relationship between the background information on the percentages of jet fighters in the area and the pilot’s observation.

The so-called planning fallacy, to which I personally plead guilty, is an example of a problem in which base rates are not given in numerical terms but must be abstracted from experience. In planning a research project, I may estimate being able to complete it in four weeks. This estimate is based on relevant case-specific evidence: desired length of report, availability of source materials, difficulty of the subject matter, allowance for both predictable and unforeseeable interruptions, and so on. I also possess a body of experience with similar estimates I have made in the past. Like many others, I almost never complete a research project within the initially estimated time frame! But I am seduced by the immediacy and persuasiveness of the case- specific evidence. All the causally relevant evidence about the project indicates I should be able to complete the work in the time allotted for it. Even though I know from experience that this never happens, I do not learn from this experience. I continue to ignore the non-causal, probabilistic evidence based on many similar projects in the past, and to estimate completion dates that I hardly ever meet. (Preparation of this book took twice as long as I had anticipated. These biases are, indeed, difficult to avoid!)

Chapter 13

Hindsight Biases in Evaluation of Intelligence Reporting

Evaluations of intelligence analysis–analysts’ own evaluations of their judgments as well as others’ evaluations of intelligence products–are distorted by systematic biases. As a result, analysts overestimate the quality of their analytical performance, and others underestimate the value and quality of their efforts. These biases are not simply the product of self-interest and lack of objectivity. They stem from the nature of human mental processes and are difficult and perhaps impossible to overcome.

Hindsight biases influence the evaluation of intelligence reporting in three ways:

• Analysts normally overestimate the accuracy of their past judgments.
• Intelligence consumers normally underestimate how much they learned from intelligence reports.
• Overseers of intelligence production who conduct postmortem analyses of an intelligence failure normally judge that events were more readily foreseeable than was in fact the case.

The analyst, consumer, and overseer evaluating analytical performance all have one thing in common. They are exercising hindsight. They take their current state of knowledge and compare it with what they or others did or could or should have known before the current knowledge was received. This is in sharp contrast with intelligence estimation, which is an exercise in foresight, and it is the difference between these two modes of thought–hindsight and foresight–that seems to be a source of bias.

an analyst’s intelligence judgments are not as good as analysts think they are, or as bad as others seem to believe. Because the biases generally cannot be overcome, they would appear to be facts of life that analysts need to take into account in evaluating their own performance and in determining what evaluations to expect from others. This suggests the need for a more systematic effort to:

• Define what should be expected from intelligence analysts.
• Develop an institutionalized procedure for comparing intelligence judgments and estimates with actual outcomes.
• Measure how well analysts live up to the defined expectations.

The discussion now turns to the experimental evidence demonstrating these biases from the perspective of the analyst, consumer, and overseer of intelligence.

The Analyst’s Perspective

Analysts interested in improving their own performance need to evaluate their past estimates in the light of subsequent developments. To do this, analysts must either remember (or be able to refer to) their past estimates or must reconstruct their past estimates on the basis of what they remember having known about the situation at the time the estimates were made.

Experimental evidence suggests a systematic tendency toward faulty memory of past estimates. That is, when events occur, people tend to overestimate the extent to which they had previously expected them to occur. And conversely, when events do not occur, people tend to underestimate the probability they had previously assigned to their occurrence. In short, events generally seem less surprising than they should on the basis of past estimates. This experimental evidence accords with analysts’ intuitive experience. Analysts rarely appear–or allow themselves to appear–very surprised by the course of events they are following.

The Consumer’s Perspective

When consumers of intelligence reports evaluate the quality of the intelligence product, they ask themselves the question: “How much did I learn from these reports that I did not already know?” In answering this question, there is a consistent tendency for most people to underestimate the contribution made by new information.

people tend to underestimate both how much they learn from new information and the extent to which new information permits them to make correct judgments with greater confidence. To the extent that intelligence consumers manifest these same biases, they will tend to underrate the value to them of intelligence reporting.

The Overseer’s Perspective

An overseer, as the term is used here, is one who investigates intelligence performance by conducting a postmortem examination of a high-profile intelligence failure.

Such investigations are carried out by Congress, the Intelligence Community staff, and CIA or DI management. For those outside the executive branch who do not regularly read the intelligence product, this sort of retrospective evaluation of known intelligence failures is a principal basis for judgments about the quality of intelligence analysis.

A fundamental question posed in any postmortem investigation of intelligence failure is this: Given the information that was available at the time, should analysts have been able to foresee what was going to happen? Unbiased evaluation of intelligence performance depends upon the ability to provide an unbiased answer to this question.

The experiments reported in the following paragraphs tested the hypotheses that knowledge of an outcome increases the perceived inevitability of that outcome, and that people who are informed of the outcome are largely unaware that this information has changed their perceptions in this manner.

An average of all estimated outcomes in six sub-experiments (a total of 2,188 estimates by 547 subjects) indicates that the knowledge or belief that one of four possible outcomes has occurred approximately doubles the perceived probability of that outcome as judged with hindsight as compared with foresight.

The fact that outcome knowledge automatically restructures a person’s judgments about the relevance of available data is probably one reason it is so difficult to reconstruct how our thought processes were or would have been without this outcome knowledge.

These results indicate that overseers conducting postmortem evaluations of what analysts should have been able to foresee, given the available information, will tend to perceive the outcome of that situation as having been more predictable than was, in fact, the case. Because they are unable to reconstruct a state of mind that views the situation only with foresight, not hindsight, overseers will tend to be more critical of intelligence performance than is warranted.

Discussion of Experiments

Experiments that demonstrated these biases and their resistance to corrective action were conducted as part of a research program in decision analysis funded by the Defense Advanced Research Projects Agency. Unfortunately, the experimental subjects were students, not members of the Intelligence Community. There is, nonetheless, reason to believe the results can be generalized to apply to the Intelligence Community. The experiments deal with basic human mental processes, and the results do seem consistent with personal experience in the Intelligence Community. In similar kinds of psychological tests, in which experts, including intelligence analysts, were used as test subjects, the experts showed the same pattern of responses as students.

One would expect the biases to be even greater in foreign affairs professionals whose careers and self-esteem depend upon the presumed accuracy of their judgments.

Can We Overcome These Biases?

Analysts tend to blame biased evaluations of intelligence performance at best on ignorance and at worst on self-interest and lack of objectivity. Both these factors may also be at work, but the experiments suggest the nature of human mental processes is also a principal culprit. This is a more intractable cause than either ignorance or lack of objectivity.

in these experimental situations the biases were highly resistant to efforts to overcome them. Subjects were instructed to make estimates as if they did not already know the answer, but they were unable to do so. One set of test subjects was briefed specifically on the bias, citing the results of previous experiments. This group was instructed to try to compensate for the bias, but it was unable to do so. Despite maximum information and the best of intentions, the bias persisted.

This intractability suggests the bias does indeed have its roots in the nature of our mental processes. Analysts who try to recall a previous estimate after learning the actual outcome of events, consumers who think about how much a report has added to their knowledge, and overseers who judge whether analysts should have been able to avoid an intelligence failure, all have one thing in common. They are engaged in a mental process involving hindsight. They are trying to erase the impact of knowledge, so as to remember, reconstruct, or imagine the uncertainties they had or would have had about a subject prior to receipt of more or less definitive information.

There is one procedure that may help to overcome these biases. It is to pose such questions as the following: Analysts should ask themselves, “If the opposite outcome had occurred, would I have been surprised?” Consumers should ask, “If this report had told me the opposite, would I have believed it?” And overseers should ask, “If the opposite outcome had occurred, would it have been predictable given the information that was available?” These questions may help one recall or reconstruct the uncertainty that existed prior to learning the content of a report or the outcome of a situation.

PART IV—CONCLUSIONS

Chapter 14

Improving Intelligence Analysis

This chapter offers a checklist for analysts–a summary of tips on how to navigate the minefield of problems identified in previous chapters. It also identifies steps that managers of intelligence analysis can take to help create an environment in which analytical excellence can flourish.

Checklist for Analysts

This checklist for analysts summarizes guidelines for maneuvering through the minefields encountered while proceeding through the analytical process. Following the guidelines will help analysts protect themselves from avoidable error and improve their chances of making the right calls. The discussion is organized around six key steps in the analytical process: defining the problem, generating hypotheses, collecting information, evaluating hypotheses, selecting the most likely hypothesis, and the ongoing monitoring of new information.

Defining the Problem

Start out by making certain you are asking–or being asked–the right questions. Do not hesitate to go back up the chain of command with a suggestion for doing something a little different from what was asked for. The policymaker who originated the requirement may not have thought through his or her needs, or the requirement may be somewhat garbled as it passes down through several echelons of management.

Generating Hypotheses

Identify all the plausible hypotheses that need to be considered. Make a list of as many ideas as possible by consulting colleagues and outside experts. Do this in a brainstorming mode, suspending judgment for as long as possible until all the ideas are out on the table.

At this stage, do not screen out reasonable hypotheses only because there is no evidence to support them. This applies in particular to the deception hypothesis. If another country is concealing its intent through denial and deception, you should probably not expect to see evidence of it without completing a very careful analysis

of this possibility. The deception hypothesis and other plausible hypotheses for which there may be no immediate evidence should be carried forward to the next stage of analysis until they can be carefully considered and, if appropriate, rejected with good cause.

Collecting Information

Relying only on information that is automatically delivered to you will probably not solve all your analytical problems. To do the job right, it will probably be necessary to look elsewhere and dig for more information. Contact with the collectors, other Directorate of Operations personnel, or first-cut analysts often yields additional information. Also check academic specialists, foreign newspapers, and specialized journals.

Collect information to evaluate all the reasonable hypotheses, not just the one that seems most likely. Exploring alternative hypotheses that have not been seriously considered before often leads an analyst into unexpected and unfamiliar territory. For example, evaluating the possibility of deception requires evaluating another country’s or group’s motives, opportunities, and means for denial and deception. This, in turn, may require understanding the strengths and weaknesses of US human and technical collection capabilities.

It is important to suspend judgment while information is being assembled on each of the hypotheses. It is easy to form impressions about a hypothesis on the basis of very little information, but hard to change an impression once it has taken root. If you find yourself thinking you already know the answer, ask yourself what would cause you to change your mind; then look for that information.

Try to develop alternative hypotheses in order to determine if some alternative–when given a fair chance–might not be as compelling as your own preconceived view. Systematic development of an alternative hypothesis usually increases the perceived likelihood of that hypothesis. “A willingness to play with material from different angles and in the context of unpopular as well as popular hypotheses is an essential ingredient of a good detective, whether the end is the solution of a crime or an intelligence estimate”.

Evaluating Hypotheses

Do not be misled by the fact that so much evidence supports your preconceived idea of which is the most likely hypothesis. That same evidence may be consistent with several different hypotheses. Focus on developing arguments against each hypothesis rather than trying to confirm hypotheses. In other words, pay particular attention to evidence or assumptions that suggest one or more hypotheses are less likely than the others.

Assumptions are fine as long as they are made explicit in your analysis and you analyze the sensitivity of your conclusions to those assumptions. Ask yourself, would different assumptions lead to a different interpretation of the evidence and different conclusions?

Do not assume that every foreign government action is based on a rational decision in pursuit of identified goals. Recognize that government actions are sometimes best explained as a product of bargaining among semi-independent bureaucratic entities, following standard operating procedures under inappropriate circumstances, unintended consequences, failure to follow orders, confusion, accident, or coincidence.

Selecting the Most Likely Hypothesis

Proceed by trying to reject hypotheses rather than confirm them. The most likely hypothesis is usually the one with the least evidence against it, not the one with the most evidence for it.

In presenting your conclusions, note all the reasonable hypotheses that were considered.

Ongoing Monitoring

In a rapidly changing, probabilistic world, analytical conclusions are always tentative. The situation may change, or it may remain unchanged while you receive new information that alters your understanding of it. Specify things to look for that, if observed, would suggest a significant change in the probabilities.

Pay particular attention to any feeling of surprise when new information does not fit your prior understanding. Consider whether this surprising information is consistent with an alternative hypothesis. A surprise or two, however small, may be the first clue that your understanding of what is happening requires some adjustment, is at best incomplete, or may be quite wrong.

Management of Analysis

The cognitive problems described in this book have implications for the management as well as the conduct of intelligence analysis. This concluding section looks at what managers of intelligence analysis can do to help create an organizational environment in which analytical excellence flourishes. These measures fall into four general categories: research, training, exposure to alternative mind-sets, and guiding analytical products.

Support for Research

Management should support research to gain a better understanding of the cognitive processes involved in making intelligence judgments. There is a need for better understanding of the thinking skills involved in intelligence analysis, how to test job applicants for these skills, and how to train analysts to improve these skills. Analysts also need a fuller understanding of how cognitive limitations affect intelligence analysis and how to minimize their impact. They need simple tools and techniques to help protect themselves from avoidable error. There is so much research to be done that it is difficult to know where to start.

Training

Most training of intelligence analysts is focused on organizational procedures, writing style, and methodological techniques. Analysts who write clearly are assumed to be thinking clearly. Yet it is quite possible to follow a faulty analytical process and write a clear and persuasive argument in support of an erroneous judgment.

More training time should be devoted to the thinking and reasoning processes involved in making intelligence judgments, and to the tools of the trade that are available to alleviate or compensate for the known cognitive problems encountered in analysis. This book is intended to support such training.

It would be worthwhile to consider how an analytical coaching staff might be formed to mentor new analysts or consult with analysts working particularly difficult issues. One possible model is the SCORE organization that exists in many communities. SCORE stands for Senior Corps of Retired Executives. It is a national organization of retired executives who volunteer their time to counsel young entrepreneurs starting their own businesses.

New analysts could be required to read a specified set of books or articles relating to analysis, and to attend a half-day meeting once a month to discuss the reading and other experiences related to their development as analysts. A comparable voluntary program could be conducted for experienced analysts. This would help make analysts more conscious of the procedures they use in doing analysis. In addition to their educational value, the required readings and discussion would give analysts a common experience and vocabulary for communicating with each other, and with management, about the problems of doing analysis.

My suggestions for writings that would qualify for a mandatory reading program include: Robert Jervis’ Perception and Misperception in International Politics (Princeton University Press, 1977); Graham Allison’s Essence of Decision: Explaining the Cuban Missile Crisis (Little, Brown, 1971); Ernest May’s “Lessons” of the Past: The Use and Misuse of History in American Foreign Policy (Oxford University Press, 1973); Ephraim Kam’s, Surprise Attack (Harvard University Press, 1988); Richard Betts’ “Analysis, War and Decision: Why Intelligence Failures Are Inevitable,” World Politics, Vol. 31, No. 1 (October 1978); Thomas Kuhn’s The Structure of Scientific Revolutions (University of Chicago Press, 1970); and Robin Hogarth’s Judgement and Choice (John Wiley, 1980). Although these were all written many years ago, they are classics of permanent value. Current analysts will doubtless have other works to recommend. CIA and Intelligence Community postmortem analyses of intelligence failure should also be part of the reading program.

To encourage learning from experience, even in the absence of a high-profile failure, management should require more frequent and systematic retrospective evaluation of analytical performance. One ought not generalize from any single instance of a correct or incorrect judgment, but a series of related judgments that are, or are not, borne out by subsequent events can reveal the accuracy or inaccuracy of the analyst’s mental model. Obtaining systematic feedback on the accuracy of past judgments is frequently difficult or impossible, especially in the political intelligence field. Political judgments are normally couched in imprecise terms and are generally conditional upon other developments. Even in retrospect, there are no objective criteria for evaluating the accuracy of most political intelligence judgments as they are presently written.

In the economic and military fields, however, where estimates are frequently concerned with numerical quantities, systematic feedback on analytical performance is feasible. Retrospective evaluation should be standard procedure in those fields in which estimates are routinely updated at periodic intervals. The goal of learning from retrospective evaluation is achieved, however, only if it is accomplished as part of an objective search for improved understanding, not to identify scapegoats or assess blame. This requirement suggests that retrospective evaluation should be done routinely within the organizational unit that prepared the report, even at the cost of some loss of objectivity.

Exposure to Alternative Mind-Sets

The realities of bureaucratic life produce strong pressures for conformity. Management needs to make conscious efforts to ensure that well-reasoned competing views have the opportunity to surface within the Intelligence Community. Analysts need to enjoy a sense of security, so that partially developed new ideas may be expressed and bounced off others as sounding boards with minimal fear of criticism for deviating from established orthodoxy.

Intelligence analysts have often spent less time living in and absorbing the culture of the countries they are working on than outside experts on those countries. If analysts fail to understand the foreign culture, they will not see issues as the foreign government sees them. Instead, they may be inclined to mirror-image–that is, to assume that the other country’s leaders think like we do. The analyst assumes that the other country will do what we would do if we were in their shoes.

Mirror-imaging is a common source of analytical error,

Pre-publication review of analytical reports offers another opportunity to bring alternative perspectives to bear on an issue. Review procedures should explicitly question the mental model employed by the analyst in searching for and examining evidence. What assumptions has the analyst made that are not discussed in the draft itself, but that underlie the principal judgments? What alternative hypotheses have been considered but rejected, and for what reason? What could cause the analyst to change his or her mind?

Ideally, the review process should include analysts from other areas who are not specialists in the subject matter of the report. Analysts within the same branch or division often share a similar mind-set. Past experience with review by analysts from other divisions or offices indicates that critical thinkers whose expertise is in other areas make a significant contribution. They often see things or ask questions that the author has not seen or asked. Because they are not so absorbed in the substance, they are better able to identify the assumptions and assess the argumentation, internal consistency, logic, and relationship of the evidence to the conclusion. The reviewers also profit from the experience by learning standards for good analysis that are independent of the subject matter of the analysis.

Guiding Analytical Products

On key issues, management should reject most single-outcome analysis–that is, the single-minded focus on what the analyst believes is probably happening or most likely will happen.

One guideline for identifying unlikely events that merit the specific allocation of resources is to ask the following question: Are the chances of this happening, however small, sufficient that if policymakers fully understood the risks, they might want to make contingency plans or take some form of preventive or preemptive action? If the answer is yes, resources should be committed to analyze even what appears to be an unlikely outcome.

Finally, management should educate consumers concerning the limitations as well as the capabilities of intelligence analysis and should define a set of realistic expectations as a standard against which to judge analytical performance.

The Bottom Line

Analysis can be improved! None of the measures discussed in this book will guarantee that accurate conclusions will be drawn from the incomplete and ambiguous information that intelligence analysts typically work with. Occasional intelligence failures must be expected. Collectively, however, the measures discussed here can certainly improve the odds in the analysts’ favor.