Will This New Game Genre Change Intelligence Training, Education?

Most gamers understand that games fall into genres.  For example, Scrabble, Boggle and my own game, Widget, are all examples of "word games".  There is no standardized list of game genres, of course, but gamers are like Supreme Court Justice Potter Stewart when it comes to genres (Stewart, in trying to define pornography, famously wrote in Jacobellis v. Ohio, "I know it when I see it.").

Most games, then, fit neatly into existing genres and truly new genres come along only rarely.  It is even more rare for a new genre of games to have a large-scale cultural or social impact.  The last such genre that I can think of was the role-playing game, epitomized by the first and still one of the most popular games, Dungeons and Dragons.  Whether you played D and D or not (or liked it if you did play it), there is no denying that it spawned a genre of games that impacted and continue to impact both culture and society.

Today there is a new genre of games - cooperative tabletop games - that I think has a chance to have a similar impact on the way we teach not just intelligence but just about everything.

Cooperative games are labelled as such because players cooperate with each other to defeat the game.  This kind of play style has long been a staple of many video games where players will gather as teams to defeat a common enemy.

While there are a few examples that date back as far as the 1980's, modern cooperative tabletop games typically require much more nuanced gameplay than their video game counterparts.  True cooperation on everything from strategy to resources is usually necessary to defeat these challenging games.  

If you are not familiar with this genre (and most people are not), I strongly recommend you get some of these games and play them.  Two good examples to start with are Pandemic and Forbidden Desert.  Both games pit you and the rest of the players in a race to beat the game.  Either everyone wins or no one wins.

There are many variations on the theme but typically these games throw an escalating series of challenges at the players.  Pandemic, for example, envisions a team of experts working to stop a global disease epidemic.  Forbidden Desert asks players to collect a series of artifacts and escape the desert before sandstorms swallow the players.

Players in these games usually assume a variety of roles, such as Engineer or Medic, each with a particular skill useful in defeating whatever it is the game throws at them.  Players can and do discuss everything from strategy to resource allocation.  This kind of game doesn't just encourage cooperation but demands it from every player.

My recent game, Spymaster (which has proved incredibly popular - I have given out nearly 200 copies to date), was designed as such a game.  Small groups of players have to make collaborative decisions about how and where to place certain collection assets in order to collect various information requirements, all while losing the fewest possible assets.  While the current version of Spymaster allows the players to determine how they will make decisions about asset allocation, I am thinking about an "advanced" version of the game that will assign various roles to the players coupled, of course, with unique capabilities associated with each role.

Whether you have had a chance to play Spymaster or not, once you have played a couple of these kinds of games, the possibilities for their use in class becomes very apparent.  There is a lot of learning going on in these games and not all of it is knowledge-based.  Teamwork, conflict management and collaboration are all essential elements of these games.

More importantly for classroom use, these games can be designed to take a relativity small amount of time to play.  Unlike videogames, tabletop games also tend to expose the underlying system to the players in a bit more detail.  Likewise, tabletop games are vastly less expensive to design and produce than videogames which means that more topics could be covered for the same or less money - clearly a consideration in these budget restricted times.  Finally, bringing a tabletop game into a secure facility is vastly easier than trying to import electrons.

Do I really think that cooperative tabletop games will change intel training and education?  I'm not sure, but I know that they can - and that this is an experiment in games-based learning worth attempting.

Thinking in Parallel (Part Three - Testing The Mercyhurst Model Against The Real World)

Part 1 -- Introduction
Part 2 -- The Mercyhurst Model

For the last 11 years, I have been using the model described in Part 2 to structure my Strategic Intelligence class at Mercyhurst University.  This is a capstone class for seniors and 2nd year graduate students within the Intelligence Studies program at Mercyhurst.  This class is centered on a real world project for a real-world decisionmaker, often within the US National Security Community.  To date, I have overseen 133 of these types of projects.

The broad parameters of the projects have remain unchanged since 2003.  Students in the class are divided into teams and are assigned by the instructor to one of 4-5 projects available during that term.  Each project is sponsored by a national security, business, or law enforcement organization that has a strategic intelligence question.  To date, sponsors of these questions have included organizations such as the National Geospatial-intelligence Agency, the Defense Intelligence Agency, the National Intelligence Council, the National Security Agency, 66th Military Intelligence Group, and the Navy’s Criminal Investigative Service to name just a few.  To give readers a sense of the wide variety of questions intelligence studies students are expected to answer in this course, I have listed a few recent examples of them below:

1. What role will non-state actors (NSAs) play and what impact will NSAs have in Sub-Saharan Africa over the next five years?o What is the likely importance of NSAs vs. State Actors, Supra-State Actors and other relevant categories of actors in sub Saharan Africa?o What are the roles of these actors in key countries, such as Niger?o Are there geographic, cultural, economic or other patterns of activity along which the roles of these actors are either very different or strikingly similar?o What analytical processes and methodologies were applied to the questions above and which proved to be effective or ineffective? 

2. What are the most important and most likely impacts on, and threats to, US national interests (including but not limited to political, military, economic and social interests) resulting from infectious and chronic human disease originating outside the US over the next 10-15 years?  

3. What are the likely trends in Brazil’s oil/liquid fuel market and electric power sector in the next ten years?  Where will these trends likely manifest themselves?o What energy capacity and security issues are likely to be the most significant to Brazil’s economy in the next ten years?o How will Brazil likely address current and/or future energy security issues over the next ten years?o Where will Brazil address these energy shortfalls?

In each case, students had only 10 weeks to conduct the research, write the analysis and present the final product to the decisionmaker.  The students had no additional financial resources available to them and, other than the question itself, received no support directly from the decisionmaker.  Students rarely had any subject matter expertise in the area under question and were only allowed to use open sources.  Students were expected to integrate lessons learned from all previous intelligence studies classes and to manage all aspects of the project without significant supervision.  Finally, all the students, in addition to this project, were taking a full academic load at the same time.  

After all of the deliverables had been produced and disseminated, the decisionmakers sponsoring the projects were asked to provide objective feedback directly to the course instructor.  This feedback, in turn, was evaluated on a five point scale correlated with traditional grading practices and professional expectations.  In short, a 3 on this scale is roughly equivalent to a "B" and a “4” on this scale is roughly equal to “A” work in a university setting.  A “5”, on the other hand, is the kind of work that would be expected from a working (albeit junior) intelligence professional.  The chart below indicates how the annual averages have changed over time.

(Note:   While this chart may appear to reflect grade inflation more than any other suggested effect, it should be noted that “A” is essentially “average” among Mercyhurst University Intelligence Studies seniors and 2nd Year graduate students and has been for the entire time frame shown above.  The current dropout rate from the program is approximately 50% and much of that is due to a strict 3.0 minimum GPA in order to stay in the program.  As a result, seniors and second year graduate students (the only students allowed to take the class), typically have GPAs that average 3.6 or above.  For example, two years ago, 18 of the top 20 GPA’s in the entire University belonged to Intelligence Studies students.)

Anecdotally, it is possible to state the exact impact of these reports within national security agencies in only a few cases.  For example, the report that answered the question on global health mentioned earlier earned this praise from the National Intelligence Council: 

“Although the Mercyhurst "NIE" should not be construed as an official U.S. government publication, we consider this product an invaluable contribution to the NIC's global disease project: not only in terms of content, but also for the insights it provides into methodological approaches. The Mercyhurst experience was also an important lesson in how wikis can be successfully deployed to facilitate such a multifaceted and participatory research project.”

Likewise, in David Moore’s book Sensemaking:  A Structure for an Intelligence Revolution (published by the National Defense Intelligence College in 2011), the study on non-state actors in sub-Saharan actors produced in answer to the question mentioned above was judged more rigorous than a similar study conducted by the National Intelligence Council (in cooperation with the Eurasia Group).

Beyond the national security community, however, the impact of these reports on various businesses and other organizations is often easier to determine.  For example, senior managers at Composiflex, a mid-sized composites manufacturer, indicated, “We used this project as a seed for our new marketing plan in 2007 and now an industry that we had not even tapped before is 30% of our business.” 

Likewise, Joel Deuterman, the CEO of Velocity.net, an Internet Service Provider, stated, “The analysts discovered that our approach was actually a cutting-edge, developing standard in our industry…What really substantiated the data for us was to see many of our existing customers on the list. Then we knew we could rely on the validity of the ones they had found for us.”  

Even foreign organizations have seen the benefit of these products including Ben Rawlence, the Advisor for Foreign Affairs and Defense in the Whip’s Office of the Liberal Democrat Party in the UK, stating, “The research carried out by your students was first class, and has been of substantial use to Members of Parliament…  It was comprehensive, well sourced and intelligently put together.  I have had no hesitation recommending it to our MPs and Lords in the same way that I recommend briefings provided for us by professional research organisations…” 

While it is possible to imagine more rigorous testing of this model of the intelligence process, the long term success of the process in generating actionable intelligence for a wide variety of customers on a range of difficult problems in a very short time using limited resources is hard to ignore.  More importantly, not only has the process proven itself successful but this success has trended upwards as improvements have been made over the years in terms of structuring the course and teaching material consistent with this approach to the intelligence process.

*****

Intelligence in the 21st century is best thought of as a series of sub-processes operating interactively and in parallel.  

This conclusion, by itself, has significant implications for the training and education of intelligence professionals.  In the first place, it suggests that it is no longer possible to specialize in one area to the exclusion of another.  Intelligence professionals will have to be trained to think more broadly, to be able to jump more fluidly from modeling to collection to analysis to production and back as the process of creating intelligence moves forward over time.  

Likewise, hardware and software support systems will need to be designed that facilitate this leaping back and forth between the various sub-processes.  Designing products that work sequentially in a parallel world will not only frustrate but will also slow down the process of generating intelligence – a result that is absolutely counter to the intelligence needs of modern decisionmakers.  

Finally, as dramatic as this type of change might appear to be, it is, perhaps, better thought of as merely aligning the training and education of intelligence professionals with what it is they already do.

Thinking In Parallel (Part 2 - The Mercyhurst Model)

Part 1 -- Introduction

While a number of tweaks and modifications to the cycle have been proposed over the years , very few professionals or academics have recommended wholesale abandonment of this vision of the intelligence process.  

This is odd.  

Other fields routinely modify and improve their processes in order to remain more competitive or productive.  The US Army, for example, has gone through several major revisions to its combat doctrine over the last 30 years, from the Active Defense Doctrine of the 1970’s to the AirLand Battle Doctrine of the 80’s and 90’s to Network Centric Operations in the early part of the 21st Century.  The model of the intelligence process, the Intelligence Cycle, however, has largely remained the same throughout this period despite the criticisms leveled against it.  The best answers, then, to the questions, “What is the intelligence process?” and “What should the Intelligence process be?” remain open theoretical questions, ripe for examination.

There are common themes, however, that emerge from this discussion of process.  These themes dictate, in my mind, that a complete understanding of the intelligence process must always include both an understanding of intelligence's role in relationship to both operations and the decisionmaker and an understanding of how intelligence products are created.  Likewise, I believe that the process of creating intelligence is best visualized as a parallel rather than as a sequential process.  I call this the "Mercyhurst Model" and believe it is a better way to do intelligence.  More importantly, I think I have the evidence to back that statement up.  

The first of the common themes referenced above is that the center of the process should be an interactive relationship between operations, the decisionmaker and the intelligence unit.  It is very clear that the intelligence process cannot be viewed in a vacuum.  If it is correct to talk about an “intelligence process” on one side of the coin, it is equally important for intelligence professionals to realize that there is a operational process, just as large if not larger and equally important if not more so, on the other side and a decisionmaking process that includes both.

The operational and intelligence processes overlap in significant ways, particularly with respect to the purpose and the goals of the individual or organization they support.  The intelligence professional is, however, focused externally and attempts to answer questions such as “What is the enemy up to?” and “What are the threats and opportunities in my environment?”  The decisionmaking side of the coin is more focused on questions such as “How will we organize ourselves to take advantage of the opportunity or to mitigate the threat?” and “How do we optimize the use of our own resources to accomplish our objectives?”  In many ways, the fundamental intelligence question is “What are they likely to do?” and the decisionmaker’s question is “What are we going to do?”  The image below suggests this relationship graphically.

The second theme is that it should be from this shared vision of the organization’s purpose and goals that intelligence requirements “emerge”.  With few exceptions, there does not seem to be much concern among the various authors who have written about the intelligence process about where requirements come from.  While most acknowledge that they generally come from the decisionmakers or operators who have questions or need estimates to help them make decisions, it also seems to be appropriate for intelligence professionals to raise issues or provide information that was not specifically requested when relevant to the goals and purpose of the organization.  In short, there seems to be room for both “I need this” coming from a decisionmaker and for “I thought you would want to know this” coming from the intelligence professional as long as it is relevant to the organization’s goals and purposes.

Theoretically, at least, the shared vision of the goals and purpose of the organization should drive decisionmaker feedback as well.  The theoretical possibility of feedback, however, is regularly compared with the common perception of reality, at least within the US national security community, that feedback is ad hoc at best.  There, the intelligence professionals preparing the intelligence are oftentimes so distant from the decisionmakers they are supporting that feedback is a rare occurrence and, if it comes at all, is typically only when there has been a flaw in the analysis or products.  As former Deputy Director Of National Intelligence for Analysis, Thomas Fingar (among others), has noted, “There are only two possibilities: policy success and intelligence failure” suggesting that “bad” intelligence is often a convenient whipping boy for poor decisions while “good” intelligence rarely gets credit for the eventual decisionmaker successes.

It is questionable whether this perception of reality applies throughout the intelligence discipline or even within the broader national security community.  Particularly on a tactical level, where the intelligence professional often shares the same foxhole, as it were, with the decisionmaker, it becomes obvious relatively quickly how accurate and how useful the intelligence provided is to the operators.   While most intelligence professionals subscribe to the poor feedback theory, most intelligence professionals also have a story or two about how they were able to give analysis to decisionmakers and how that analysis made a real difference, a difference willingly acknowledged by that decisionmaker.  The key to this kind of feedback seems less related to the issue or to intelligence writ large and more related to how closely tied are the intelligence and decisionmaking functions.  The more distance between the two, the less feedback, unsurprisingly, there is likely to be.

The third theme, is that from the requirement also emerges a mental model in the mind of the intelligence professional regarding the kinds of information that the he or she needs in order to address the requirement.  This model, whether implicit or explicit, emerges as the intelligence professional thinks about how best to answer the question and is constructed in the mind of the intelligence professional based on previous knowledge and the professional’s understanding of the question.  

This mental model typically contains 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, Phillip Tetlock has demonstrated that a relatively high level of general knowledge about the world significantly improves forecasting accuracy across any domain of knowledge, even highly specialized ones (Counter-intuitively, he also offers good evidence to suggest that high degrees of specialized knowledge, even within the domain under investigation does not add significantly to forecasting accuracy). 

The mental model is more than just an outline, however.  It is where biases and mental shortcuts are most likely to impact the analysis.  It is where divergent thinking strategies are most likely to benefit and where their opposites, convergent thinking strategies such as grouping, prioritizing and filtering, need to be most carefully applied.  One of the true benefits of this model over the traditional Intelligence Cycle is that it explicitly includes humans in the loop - both what they do well and what they don't.

Almost as soon as the requirement gains enough form to be answerable, however, and even if it continues to be modified as a result of an exchange or series of exchanges between the decisionmakers and the intelligence professionals, four processes, operating in parallel, start to take hold: The modeling process we just discussed, collection (in a broad sense) of additional relevant information, analysis of that information with the requirement in mind and early ideas about production (i.e. how the final product will look, feel and be disseminated in order to facilitate communicating the results to the decisionmaker).

The notional graphic below visualizes the relationship between these four factors over the life of an intelligence product.  Such a product might have a short suspense (or due date) as in the case of a crisis or a lengthier timeline, as in the case of most strategic reports, but the fundamental relationship between the four functions will remain the same.  All four begin almost immediately but, through the course of the project, the amount of time spent focused on each function will change, with each function dominating the overall process at some point.  The key, however, is that these four major functions operate in parallel rather than in sequence, with each factor informing and influencing the other three at any given point in the process.

A good example of how these four functions interrelate is your own internal dialogue when someone asks you a question.  Understanding the question is clearly the first part followed almost immediately by a usually unconscious realization of what it would take to answer the question along with a basic understanding of the form that answer needs to take.  You might recall information from memory but you also realize that there are certain facts you might need to check out before you answer the question.  If the question is more than a simple fact–based question, you would probably have to do at least some type of analysis before framing the answer in a form that would most effectively communicate your thoughts to the person asking the question.  You would likely speak differently to a child than you would to an adult, for example, and, if the question pertained to a sport, you would likely answer the question differently when speaking with a rabid fan than to a foreigner who knew nothing about that particular sport.

This model of the process concludes then where it started, back with the relationship between the decisionmaker, the intelligence professional and the goals and purposes of the organization.  The question here is not requirements, however, but feedback.  The intelligence products the intelligence unit produced were, ultimately, either useful or not.  The feedback that results from the execution of the intelligence process will impact, in many ways, the types of requirements put to the intelligence unit in the future, the methods and processes the unit will use to address those requirements and the way in which the decisionmaker will view future products.

This model envisions the intelligence process as one where everything, to one degree or another, is happening at once.  It starts with the primacy of the relationship between the intelligence professional and the decisionmakers those professionals support.  It broadens and redefines, however, those few generally agreed upon functions of the intelligence cycle but sees them as operating in parallel with each taking precedence in more or less predictable ways throughout the process.  This model, however, explicitly adds the creation and refinement of the mental model of the requirement created by the intelligence unit as an essential part of the process.  This combined approach captures the best of the old and new ways of thinking about the process of intelligence.  Does it, however, test well against the reality of intelligence as it is performed on real-world intelligence problems?

Part Three - Testing The Mercyhurst Model Against The Real World

Thinking In Parallel: A 21st Century Vision Of The Intelligence Process

(Note:  I recently was asked to present a paper on my thoughts about re-defining the intelligence process and the implications of that redefinition on education, training and integration across the community at the US Intelligence Community's Geospatial Training Council's (CGTC) conference in Washington DC.  For those familiar with my earlier work in the intelligence cycle and the damage it is causing, you will find this paper shorter and less about the Cycle and more about the alternative to it I am proposing (and the evidence to support the adoption of that alternative...).  Enjoy!)

Abstract:  Effective integration and information sharing within the intelligence community is not possible until the fundamental process of intelligence is re-imagined for the 21st Century.  The current model, the Intelligence Cycle, developed in World War 2 and widely criticized, has outlived its useful life.  In fact, it has become part of the problem.  This paper abandons this sequential process that was appropriate for a slower and less information rich environment.  Instead, a more streamlined parallel process is proposed.  Accompanying this new vision of the intelligence process will be an analysis of data collected from over 130 real-world intelligence projects conducted using this model of the intelligence process and delivered to decisionmakers in the national security (including GEOINT), law enforcement and business sectors.  Additionally, the training and education implications as well as the kinds of software and hardware systems necessary to support this new understanding of the process are discussed.

Part 1 -- Introduction

"We must begin by redefining the traditional linear intelligence cycle, which is more a manifestation of the bureaucratic structure of the intelligence community than a description of the intelligence exploitation process." -- Eliot Jardines, former head of the Open Source Center, in prepared testimony in front of Congress, 2005  

"When it came time to start writing about intelligence, a practice I began in my later years at the CIA, I realized that there were serious problems with the intelligence cycle.  It is really not a very good description of the ways in which the intelligence process works."  Arthur Hulnick, "What's Wrong With The Intelligence Cycle", Strategic Intelligence, Vol. 1 (Loch Johnson, ed), 2007

"Although meant to be little more than a quick schematic presentation, the CIA diagram [of the intelligence cycle] misrepresents some aspects and misses many others." -- Mark Lowenthal, Intelligence:  From Secrets to Policy (2nd Ed.,2003) 

"Over the years, the intelligence cycle has become somewhat of a theological concept:  No one questions its validity.  Yet, when pressed, many intelligence officers admit that the intelligence process, 'really doesn't work that way.'" -- Robert Clark, Intelligence Analysis:  A Target-centric Approach, 2010

Academics have noted it and professionals have confirmed it:  Our current best depiction of the intelligence process, the so-called "intelligence cycle", is fatally flawed.  Moreover, I believe these flaws have become so severe, so grievous, that continued adherence to and promotion of the cycle is actually counterproductive.  In this paper I intend to briefly outline the main flaws in the intelligence cycle, to discuss how the continued use of the cycle hampers, indeed extinguishes, efforts to effectively integrate and share information and, finally, suggest an alternative process – a parallel process – that, if adopted, would transform intelligence training and education.

*****

Despite its popularity, the history of the cycle is unclear.  US army regulations published during WWI identify collection, collation and dissemination of military intelligence as essential duties of what was then called the Military Intelligence Division but there was no suggestion that these three functions happen in a sequence, much less in a cycle.

By 1926, military intelligence officers were recommending four distinct functions for tactical combat intelligence:  Requirements, collection, "utilization" (i.e. analysis), and dissemination, though, again, there was no explicit mention of an intelligence cycle.

The first direct mention of the intelligence cycle (see image) is from the 1948 book, Intelligence Is For Commanders.  Since that time, the cycle, as a model of how intelligence works, has become pervasive.  A simple Google image search on the term, "Intelligence Cycle" rapidly gives one a sense of the wide variety of agencies, organizations and businesses that use some variant of the cycle.

The Google Image Search above highlights the first major criticism of the Intelligence Cycle:  Which one is correct?  In fact, an analysis of a variety of Intelligence Cycles from both within and from outside the intelligence community reveals significant differences often within a single organization (See chart below gathered from various official websites in 2011).
While there is some consistency (“collection”, for example, is mentioned in every variant of the cycle), these disparities have significant training and education implications that will likely manifest themselves as different agencies attempt to impose their own understanding of the process during joint operations.  Different agencies teaching fundamentally different versions of the process will likewise seriously impact the systems designed to support analysts and operators within agencies.  This, in turn, will likely make cross-agency integration and information sharing more difficult or even impossible.

The image also highlights the second major problem with the cycle:  Where is the decisionmaker?  None of the versions of the intelligence cycle listed above explicitly include or explain the role of the decisionmaker in the process.  Few, in fact, include a specific feedback or evaluation step.  From the standpoint of a junior professional in a training environment (particularly in a large organization such as the US National Security Intelligence Community where intelligence professionals are often both bureaucratically and geographically distant from the decisionmakers they support), this can create the impression that intelligence is a “self-licking ice-cream cone” – existing primarily for its own pleasure rather than as an important component of a decision support system.

Finally, and most damningly (and as virtually all intelligence professionals know):  “It just doesn’t work that way.”  The US military's Joint Staff Publication 2.0, Joint Intelligence (Page 1-5), describes modern intelligence as the antithesis of the sequential process imagined by the Cycle.  Instead, intelligence is clearly described as fast-paced and interactive, with many activities taking place simultaneously (albeit with different levels of emphasis):

"In many situations, various intelligence operations occur almost simultaneously or may be bypassed altogether. For example, a request for imagery requires planning and direction activities but may not involve new collection, processing, or exploitation. In this case, the imagery request could go directly to a production facility where previously collected and exploited imagery is reviewed to determine if it will satisfy the request. Likewise, during processing and exploitation, relevant information may be disseminated directly to the user without first undergoing detailed all-source analysis and intelligence production. Significant unanalyzed operational information and critical intelligence should be simultaneously available to both the commander (for time-sensitive decision-making) and to the all source intelligence analyst (for the production and dissemination of intelligence assessments and estimates). Additionally, the activities within each type of intelligence operation are conducted continuously and in conjunction with activities in each intelligence operation category. For example, intelligence planning (IP) occurs continuously while intelligence collection and production plans are updated as a result of previous requirements being satisfied and new requirements being identified. New requirements are typically identified through analysis and production and prioritized dynamically during the conduct of operations or through joint operation planning.”

The training and education implications of this kind of disconnect between the real-world of intelligence and the process as taught in the classroom, between practice and theory, are both severe and negative.  

At one end of the spectrum it is as simple as a violation of the long-term military principle of “Train as you will fight”.  Indeed it is only questionable as to which approach will be more counterproductive:  Forcing students of intelligence to learn the Cycle only to realize after graduation and on their own that it is unrealistic or throwing a slide of the Cycle up on the projector only to have an experienced instructor indicate that “This is what you have to learn but this isn’t the way it really works.”  Both scenarios regularly take place within the training circles of the intelligence community.

At the other end of the spectrum, the damage is much more nuanced and systemic.  Specifically, intelligence professionals aren’t just undermining their own training, they are miscommunicating to those outside the community as well.  The effects of this may seem manageable, even trivial, to some but imagine a software engineer trying to design a product to support intelligence operations.  This individual will know nothing but the Cycle, will take this as an accurate description of the process, and design products accordingly.  

In fact, it was the failure of these kinds of software projects to gain traction within the Intelligence Community that led Georgia Tech visual analytics researcher Youn-ah Kang and her advisor, Dr. John Stasko, to undertake an in-depth, longitudinal field study to determine how, exactly, intelligence professionals did what they did.  While all of the results of their study are both interesting and relevant, the key misconception they identified is that “Intelligence analysis is about finding an answer to a problem via a sequential process.”  In turn, the failure to recognize this misconception earlier resulted in a failure of many of the tools they and others had created.  In short, as Kang and Stasko noted, “Many visual analytics tools thus support specific states only (e.g., shoebox and evidence file, evidence marshalling, foraging), and often they do not blend into the entire process of intelligence analysis.

Next:  Part 2 -- The Mercyhurst Model

Visualizing Intelligence Analysis (Comiqs.com)

Many people are visual learners and with so much multimedia around, it is pretty much inevitable that the forms that intelligence analysis will take in the future will go far beyond the typical written summary and oral brief.

While tailoring the content of intelligence to meet a decisionmaker's preconceptions or desires has always been just plain wrong, tailoring the form to meet the needs of a particular decisionmaker just makes sense. If intelligence is not "accessible" to a decisionmaker, it is likely to be ignored and if it is ignored, it might as well not have been created. After all, the first version of the President's Daily Brief was designed to fit in President Kennedy's shirt pocket (It was called the PICL for "President's Intelligence Checklist" (the people who wrote it worked in the "PICL Factory"). I have also heard that President Reagan asked the intel community to create videos for him to watch, particularly on key leaders but I don't know if that is true).

All of this is by way of introduction to a new online service called Comiqs.com. The service allows the user to easily create comic-like products using images from Flickr.com or their own images. These products can then be easily embedded into web-pages or blogs.

I gave it a try and you can see the results below. I took Ken Foster's recent article for ISN on Peru and tried to turn it into a one page "comic". It was pretty easy to use the Comiqs search function to find relevant images from Flickr and then very easy to add the text. While this was just a test and I have obviously missed most of Ken's nuance, I am pretty satisfied with the ability of this tool to "translate" one medium into another.

My frustrations with Comiqs stem mostly from the lack of functionality in the current version. There are limited tools for editing pictures and no tool for creating a good quality print version of the comic. Since the product is in beta, it will likely see dramatic improvements in the near term. I certainly hope so. All in all, I found it easy and, well, fun.

I don't say it would work for any type of intelligence product (not sure the PDB guys want to jump on this just yet...) but I could imagine it as a useful tool for certain kinds of intel products for certain kinds of audiences. For example, I could imagine it as a very useful tool for creating training materials for young soldiers (Anyone out there remember PS Magazine?).