Part 9 -- Departures From The Intelligence Cycle
Part 10 -- The New Intelligence Process
Part 11 -- The New Intelligence Process: The First Picture
Part 12 -- The New Intelligence Process: The Second Picture
In the end, whether you accept this new model of the intelligence process or not, it is clear that the hoary image of the intelligence cycle needs to be put to rest. Whether you would do that with full honors or, as I advocate, with the use of explosives, is irrelevant. The cycle, as should be clear by now, needs to go.
To summarize, the cycle fails on three counts at least: We cannot define what it is and what it isn't, it does not match the way intelligence actually works in the 21st Century and it does not help us explain our processes to the decisionmakers we support. Efforts to fix these flaws have not worked and, furthermore, this is all widely recognized by those who have studied the role and impact of the cycle.
In addition, the community of intelligence professionals (and I include academics who study intelligence in this group) will have to be the ones to lay the cycle to rest. Not only does no one else care, but also the community of intelligence professionals has, as the WMD report noted, "an almost perfect record of resisting external recommendations."
Yes, the interregnum will be difficult. The decisionmakers we support, the professionals with whom we work and the students we teach will all ask -- and deserve -- good answers. These answers will come slowly at first. In fact, at the outset, we may only be able to "teach the controversy", as it were.
Hopefully, over time, though, the need for a new vision of the intelligence process will drive intellectual curiosity and, through the iterative process of creation and destruction, something more robust will emerge; an improved model that will stand the tests of the next 60 years. While I have clearly already placed my bets in this regard, I will be happy if the community of intelligence professionals merely recognizes the need to move beyond its historical constraints, accepts this siren's call for what it is, plugs its ears and sails off in a new direction - any direction.
Because anything would be better than continuing to pretend that the world has not really changed since the 1940's. Anything would be better than continuing to spend countless wasted hours explaining and attempting to justify something that should have been retired long ago. Anything, in short, would be better than continuing to lie to ourselves.
Friday, March 23, 2012
Wednesday, March 21, 2012
Part 12 -- The New Intelligence Process: The Second Picture (Let's Kill The Intelligence Cycle)
Part 9 -- Departures From The Intelligence Cycle
Part 10 -- The New Intelligence Process
Part 11 -- The New Intelligence Process: The First Picture
(Note: I started this series of posts many months ago with the intent of completing it in short order. Life, as it so often does, got in the way... If you are new to the series or you have forgotten what the excitement was all about, I recommend beginning at the beginning. For the rest of you, thank you for your patience!)
At the highest level, intelligence clearly supports the decisionmaking process. Understanding this is a first step to understanding what drives intelligence requirements and what defines good intelligence products. This is the message of the first picture.
But what about the details? Broad context is fine as far as it goes, but how should the modern intelligence professional think about the process of getting intelligence done? The second picture is designed to answer these questions.
The single most important thing to notice about this image is that it imagines intelligence as a parallel rather than as a sequential process. In this image of the process, there are four broad themes, or sub-processes, moving across time from a nebulous start to a fuzzy finish, with each theme rising to a high point in terms of emphasis at different points in the process. Also intended by this image is the idea that each theme constantly reflects back and forth among the other three, influencing them as they each influence each other at every point in time.
Let me anticipate an initial objection to this picture -- that the intelligence process has a "start" and a "finish". The intelligence function, to be sure, is an ongoing one and this was one of the implied lessons of the first picture. Having made that point there, here I think it is important to focus on how intelligence products are actually generated. In this respect, clearly, there is a point at which a question (an intelligence requirement) is asked. It may be indistinct, poorly formed or otherwise unclear, but the focus of an intelligence effort does not exist in any meaningful way until there is a question that is, in some way, relevant to the decisionmaking process the intelligence unit supports.
Likewise, there is a finish. It may take place in an elevator or in a formal brief, in a quick email or in a 50 page professionally printed and bound document, but answering those questions, i.e. the dissemination of the intelligence product, in whatever form, signifies the end of the process. Yes, this process then begins immediately anew with new questions, and yes, there are always multiple questions being asked and answered simultaneously but neither observation invalidates the general model.
Mental Modelling
Modeling should go on throughout the entire intelligence process, however. As new information comes in or analysis gets produced, the model may well grow, shrink or morph as the concepts and the relationships between those concepts become more clear. At some point (typically early) in the intelligence process, however, the emphasis shifts away from modeling and towards collecting, analyzing and producing. While mental modeling doesn’t become unimportant, it does begin to lose importance as less time is devoted to modeling and more to the other three functions.
Collection, like modeling, never stops. Intelligence professionals will continue to collect information relevant to the particular requirement right up to the day the final product is published. In fact, collection on a particularly difficult problem (i.e. almost all of them) will often continue after publication. Decisionmakers and analysts alike want to know if they were correct in their key assumptions, how accurate the final product was and all understand a need to continue to track particularly important requirements over time.
The next sub-process to take precedence is analysis. As with both modeling and collection, analysis begins almost immediately. Tentative answers leap to mind and, in simple cases or where time is a severe constraint, these initial responses may have to do. Analysis doesn’t really move to the forefront, however, until the requirement is understood and enough collection has taken place for the analyst to sense that adequate information exists to begin to go beyond tentative analyses and take a crack at answering the overall question or questions.
Analysis is where the raw material of intelligence, information, gets turned into products that address the decisionmaker’s requirements. It is also the task most fraught with difficulties. From the type of information used (typically unstructured) to the methods used to analyze this information to the form of the final product, analysts face enormous practical and psychological difficulties. While the goal is clear – reduce the decisionmaker’s level of uncertainty – the best ways to get there are often unclear or rely on untested or poorly tested methods.
Part 10 -- The New Intelligence Process
Part 11 -- The New Intelligence Process: The First Picture
(Note: I started this series of posts many months ago with the intent of completing it in short order. Life, as it so often does, got in the way... If you are new to the series or you have forgotten what the excitement was all about, I recommend beginning at the beginning. For the rest of you, thank you for your patience!)
At the highest level, intelligence clearly supports the decisionmaking process. Understanding this is a first step to understanding what drives intelligence requirements and what defines good intelligence products. This is the message of the first picture.
But what about the details? Broad context is fine as far as it goes, but how should the modern intelligence professional think about the process of getting intelligence done? The second picture is designed to answer these questions.
 |
| The Second Picture |
The single most important thing to notice about this image is that it imagines intelligence as a parallel rather than as a sequential process. In this image of the process, there are four broad themes, or sub-processes, moving across time from a nebulous start to a fuzzy finish, with each theme rising to a high point in terms of emphasis at different points in the process. Also intended by this image is the idea that each theme constantly reflects back and forth among the other three, influencing them as they each influence each other at every point in time.
Let me anticipate an initial objection to this picture -- that the intelligence process has a "start" and a "finish". The intelligence function, to be sure, is an ongoing one and this was one of the implied lessons of the first picture. Having made that point there, here I think it is important to focus on how intelligence products are actually generated. In this respect, clearly, there is a point at which a question (an intelligence requirement) is asked. It may be indistinct, poorly formed or otherwise unclear, but the focus of an intelligence effort does not exist in any meaningful way until there is a question that is, in some way, relevant to the decisionmaking process the intelligence unit supports.
Likewise, there is a finish. It may take place in an elevator or in a formal brief, in a quick email or in a 50 page professionally printed and bound document, but answering those questions, i.e. the dissemination of the intelligence product, in whatever form, signifies the end of the process. Yes, this process then begins immediately anew with new questions, and yes, there are always multiple questions being asked and answered simultaneously but neither observation invalidates the general model.
What of the sub-processes, though? What are they and how do they relate to each other? The four include mental modeling, collection
of relevant information, analysis
of that information and production (i.e. how the intelligence will be communicated to the decisionmakers).
Mental Modelling
Until intelligence becomes a process where machines exclusively speak only to other machines, the mental models carried around by intelligence professionals and the decisionmakers they support will be an inseparable part of the intelligence process. While most intelligence professionals readily acknowledge the strengths and weaknesses of human cognition, one of the most important qualities, in my mind, of this model is that it embeds these strengths and weaknesses directly into the process and acknowledges the influence of the human condition on intelligence.
These mental models typically contain at least two kinds of information,
information already known and information that needs to be gathered. Analysts rarely start with a completely blank
slate. In fact, a relatively high level
of general knowledge about the world has been demonstrated to significantly
improve forecasting accuracy across any domain of knowledge, even highly
specialized ones (Counter-intuitively, there is good evidence to suggest that
high degrees of specialized knowledge, even within the domain under investigation
does not add significantly to forecasting accuracy).
The flip side of this coin is psychological bias, which has a way of leading analysts astray without them even
being aware of it. An extensive overview of these
topics is beyond the scope of this post but it is safe to say that, whether implicit or explicit, these models, containing what we know, what we think we need to know and how our minds will process all this information, emerge as the intelligence professional
thinks about how best to answer the question.
Typically, at the outset of the intelligence process is is this modeling function that receives the most emphasis. Figuring out how to think about the problem, understanding what kind of information needs to be collected and identifying key assumptions in both the questions and the model are necessary to some degree before the other functions can begin in earnest. This is particularly true with a new or particularly complex requirement. Furthermore, this modeling function is often informal or even implicit. It is rare, in current practice, to see the mental model on which collection is planned and analysis conducted made explicit. This is unfortunate since making the model explicit has proven, if done properly, to accelerate the other sub-processes, limit confusion within a team and produce more accurate forecasts.
Modeling should go on throughout the entire intelligence process, however. As new information comes in or analysis gets produced, the model may well grow, shrink or morph as the concepts and the relationships between those concepts become more clear. At some point (typically early) in the intelligence process, however, the emphasis shifts away from modeling and towards collecting, analyzing and producing. While mental modeling doesn’t become unimportant, it does begin to lose importance as less time is devoted to modeling and more to the other three functions.
Collection
Typically, the next sub-process to take precedence is collection. Again, as with modeling, collection begins almost as soon as a rudimentary requirement forms in the mind of the intelligence professional. People naturally begin to draw on their own memories and, if the question is complicated enough, begin to look for additional information to answer the question. In more complex questions, where the information needs are clearly higher, the intelligence professional even comes up with a collection plan and tasks others to collect the information in order to help address the requirement.
Collection, like modeling, never stops. Intelligence professionals will continue to collect information relevant to the particular requirement right up to the day the final product is published. In fact, collection on a particularly difficult problem (i.e. almost all of them) will often continue after publication. Decisionmakers and analysts alike want to know if they were correct in their key assumptions, how accurate the final product was and all understand a need to continue to track particularly important requirements over time.
All that said, collection does tend to lose importance relative to other functions over time. Economists call this diminishing returns and it reflects a
general rule that collection efforts, when considered across the entire
spectrum of activities, from no knowledge about a subject to the
current level of knowledge about a subject, typically add less and less
genuinely new information over time. Again, this is not to say that collection becomes unimportant, it is simply a reflection of the fact that other processes tend to increase in importance with respect to collection at some point in the process.
Analysis
The next sub-process to take precedence is analysis. As with both modeling and collection, analysis begins almost immediately. Tentative answers leap to mind and, in simple cases or where time is a severe constraint, these initial responses may have to do. Analysis doesn’t really move to the forefront, however, until the requirement is understood and enough collection has taken place for the analyst to sense that adequate information exists to begin to go beyond tentative analyses and take a crack at answering the overall question or questions.
Analysis is where the raw material of intelligence, information, gets turned into products that address the decisionmaker’s requirements. It is also the task most fraught with difficulties. From the type of information used (typically unstructured) to the methods used to analyze this information to the form of the final product, analysts face enormous practical and psychological difficulties. While the goal is clear – reduce the decisionmaker’s level of uncertainty – the best ways to get there are often unclear or rely on untested or poorly tested methods.
Production
The final sub-process is production (which, for our purposes here, also includes dissemination). As with all the other functions, it, too, begins on day one. It is clearly, however, the least important function at the outset of the intelligence process. Still, intelligence professionals do give some thought (and experienced professionals have learned to give more than a little thought) up front to the form and nature of the final product at the beginning of the process.
The final sub-process is production (which, for our purposes here, also includes dissemination). As with all the other functions, it, too, begins on day one. It is clearly, however, the least important function at the outset of the intelligence process. Still, intelligence professionals do give some thought (and experienced professionals have learned to give more than a little thought) up front to the form and nature of the final product at the beginning of the process.
Requirements typically come with an implied or explicit “deliverable” associated with them. Is the answer, for example, to the intelligence requirement to be in the form of a briefing or is it to be a written report? Knowing this at the outset helps the intelligence professionals tasked with answering the requirement to plan and to identify items along the way that will make the production of the final product easier. For example, knowing that the final product is to be a briefing, gives the intelligence professionals associated with the project time to identify relevant graphics during the project rather than going back and finding such graphics at the last minute. Likewise, if the final briefing is to be a written document, the time necessary to write and edit such a product might be substantial and this, in turn, would need to be factored into the planning process.
Production is an incredibly important but often under-appreciated function within the intelligence process. If intelligence products are not accessible, i.e. packaged with the decisionmaker in mind, then they are unlikely to be read or used. Under such circumstances, all of the hard work done by intelligence professionals up to this point is wasted. On the other hand, there is a fine line between making a document or other type of intelligence report accessible and selling a particular position or way of thinking about a problem. Intelligence professionals have to steer clear of those production methods and “tricks” that can come across as advertising or advocacy. Production values should not compromise the goal of objectivity.
Likewise, some intelligence professionals associate high production values with pandering to the decisionmaker. These professionals see adding multimedia, graphics, color and other design features to an intelligence product to be unnecessary “chrome” or “bling”. These professionals, many from earlier generations, think that intelligence products “should stand on their own” and that the ease with which such “tricks” are used in modern production is not an excuse to deviate from time-honored traditions in production.
The guiding principle here, of course, is not what the intelligence professional thinks but what the decisionmaker the intelligence professional is supporting thinks. Some decisionmakers will, of course, prefer their intelligence products in a simple text-based format. Others, including many business professionals, will want less text and more supporting data, including charts and graphs. Some (and the demand for this may well increase in the future) will want their reports in a video format for use on their personal multimedia device.
Production is an incredibly important but often under-appreciated function within the intelligence process. If intelligence products are not accessible, i.e. packaged with the decisionmaker in mind, then they are unlikely to be read or used. Under such circumstances, all of the hard work done by intelligence professionals up to this point is wasted. On the other hand, there is a fine line between making a document or other type of intelligence report accessible and selling a particular position or way of thinking about a problem. Intelligence professionals have to steer clear of those production methods and “tricks” that can come across as advertising or advocacy. Production values should not compromise the goal of objectivity.
Likewise, some intelligence professionals associate high production values with pandering to the decisionmaker. These professionals see adding multimedia, graphics, color and other design features to an intelligence product to be unnecessary “chrome” or “bling”. These professionals, many from earlier generations, think that intelligence products “should stand on their own” and that the ease with which such “tricks” are used in modern production is not an excuse to deviate from time-honored traditions in production.
The guiding principle here, of course, is not what the intelligence professional thinks but what the decisionmaker the intelligence professional is supporting thinks. Some decisionmakers will, of course, prefer their intelligence products in a simple text-based format. Others, including many business professionals, will want less text and more supporting data, including charts and graphs. Some (and the demand for this may well increase in the future) will want their reports in a video format for use on their personal multimedia device.
Intelligence professionals in general, then, will need to have a wider variety of production skills in the future and, while production concerns do not take precedence until closer to the end of the project, the need to think about them at some level permeates the entire project.
Next: The Whole Picture
Sunday, March 18, 2012
Which Language Should I Take? (Careers In Intelligence)
 |
| http://thesteamerstrunk.blogspot.com/2011/01/february-is-great-language-learning.html |
I advise about 50 students and, of all the questions I have been asked over the years, this is, by far, the most common. Parents, too, ask this question when their high school students come to visit the campus.
Many students and parents think they know the answer to this question and are usually looking for some sort of confirmation that Chinese, Arabic or Russian are the “right” languages. They are confused (and maybe a bit disappointed) when I tell them, “It depends.”
In fact, I think it depends on three things: Interest, aptitude and goals. Let me talk about the one I think is most important first - interest.
I am a pretty strong believer that interest should drive learning. Interest equals attention and effort and, over time, those things matter quite a bit in how much one learns and how well one does in a language. Picking a language to learn in college because it is in demand in the current job market or it is perceived to be the “right” language to learn is, in my opinion, a mistake.
Aptitude is another important and often overlooked factor. I have been to the Defense Language Institute (twice, once for Italian and once for what used to be called Serbo-Croatian). I was able to qualify for these courses partly because I did well on the Defense Language Aptitude Battery or DLAB. The DLAB does not measure how well you know a language; it measures how well you can learn any language and it is, in my experience, surprisingly accurate.
Students (i.e. soldiers and other government employees) who did very well on this test learned even the most difficult languages fairly easily. Students who did poorly on this test struggled with languages that are traditionally easier for English speakers to learn. It turns out that language ability is a lot like musical talent; some people have it and some do not.
Over the next few weeks, all of our freshmen will take the Modern Language Aptitude Test, which is a commercial equivalent of the DLAB. We used to have students take language courses as freshmen but changed a few years ago because we wanted our students to have a better feel for the intelligence career field (which is important for the last factor I will discuss) and because we wanted them to take the MLAT before they decided which language program to pursue.
Let me be clear: We do not use the MLAT as a way to sort our students. A student can still take any language they wish to take. We do think, however, that it is unfair to students not to give them the information they need to make a more informed choice. In short, a student who does poorly on the MLAT can still take a language such as Japanese or Arabic but they should not be surprised if they have to work harder at it than others to achieve a similar level of mastery.
(Note: There are other good reasons not to use the MLAT in such a black and white manner. When the military tests someone for DLI, they expect them to spend many hours mastering the language. This is an expensive proposition and they want to be sure that the student has the aptitude to achieve those mastery goals. College level programs in a language rarely rise to these exacting standards. For example, consider my Italian language course. At the time I took it, it was a 24 week course. For five days a week, six hours a day, we learned Italian. After hours, we were expected to put in at least 2 additional hours on homework. Each week added up to roughly 40 hours studying a language. This is roughly equivalent to the number of classroom hours a college student spends in a semester. Even if the student spends as much time outside of class studying as they do in class, the typical DLI student arguably spends more time studying and practicing a language in a couple of weeks than a college student spends in a semester. In addition, the college language course is deliberately geared towards the average student (or whatever passes for average at that university). This is not the case at DLI, where the students have been carefully selected based on the government’s assessment of capabilities and the needs of the military or government agency sponsoring the position.)The final factor to consider is goals. Students in our program often start out narrowly focused on positions within the CIA or FBI. Many go on to achieve these goals but it is not uncommon for students, once exposed to the full array of opportunities within the national security (not to mention the business and law enforcement) fields, to change these goals.
As the goals change, the languages that might augment those goals also change. For example, if a student starts to become more interested in the possibilities of intelligence in business, then any of the romance languages, German or Japanese become very good choices. Europe and Japan continue to be major trading partners of the US and having a good understanding of any of these languages is a plus. Some people tend to disparage Spanish. This is a mistake. A growing percentage of Americans speak Spanish as a first language and if a student is interested in a career in law enforcement intelligence or in customs enforcement, a working knowledge of Spanish is becoming increasingly important.
Chinese, Russian and Arabic are clearly important languages but they are also very challenging for most English speakers. Taking these kinds of courses without a strong interest, aptitude or goal can be a frustrating experience for many students.
If a student really wishes to become reasonably proficient in a particular language, I also recommend that they start planning early on for a semester or more abroad in a country where the language is spoken routinely. There is nothing like daily immersion in a language to add depth and nuance to your vocabulary.
A final word about languages: Some students are better off thinking about a digital language track; that is, they should focus on learning a computer language rather than a traditional spoken language. Ideally, I would recommend that a student learn both. One of the most important things an analyst can bring to the table is an appreciation of the value of culture in analysis. There is no better way to get that appreciation in my experience than by learning a language – any language. That said, being able to code, to be unafraid of the workings of a computer or a network of computers, are critical 21st century skills as well. In those cases where a student has a tin ear but an aptitude for all things digital, learning a computer language is a respectable option.
Subscribe to:
Posts (Atom)
