Monday, June 10, 2019

How To Teach 2500 Years Of Intelligence History In About An Hour

Original version of the Art of War by Sun-Tzu
As with most survey courses, Introduction to Intelligence Studies has a ton of information that it needs to cover--all of it an inch deep and mile wide.  One of the most difficult parts of the syllabus to teach, however, is intelligence history.

Whether you start with the Bible or, as I do, with Chapter 13 of The Art Of War, you still have 2500 years of history to cover and typically about an hour long class to do it.  Don't get me wrong.  I think the history of intelligence ought to be at least a full course in any intelligence studies curriculum.  The truth is, though, you just don't have time to do it justice in a typical Intel 101 course.

I was confronted with this exact problem last year.  I had not taught first-year students for years, and when the time came in the syllabus to introduce these students to intel history, I was at a bit of a loss.  Some professors gloss over ancient history and start with the National Security Act of 1947.  Some compress it even more and focus entirely on post Cold War intelligence history.  Others take a more expansive view and select interesting stories from different periods of time to illustrate the general role of intelligence across history.  

All of these approaches are legitimate given the topic and the time constraints.  I wanted, however, to try to make the history of intel a bit more manageable for students new to the discipline.  I hit on an approach that makes sense to me and seemed to work well with the students.  I call it the Four Ages Of Intelligence.

The first age I call the Age of Concentration.  In ancient times, power and knowledge was concentrated in the hands of a relatively small number of people.  The king or queen, their generals, and the small number of officers and courtiers who could read or write were typically both the originators and targets of intelligence efforts.  These efforts, in turn, were often guided by the most senior people in a government.  Sun Tzu noted, "Hence it is that which none in the whole army are more intimate relations to be maintained than with spies."  George Washington, as well, was famous not only as a general but also as a spymaster.  

The Age of Concentration lasted, in my mind, from earliest times to about the early 1800's.  The nature of warfare began to change rapidly after the American and French Revolutions. 
Washington and the capture of the Hessians at Trenton.  
Large citizen armies and significant technological advances (railroads, telegraphs, photography, balloons!) made the process of running spy rings and collating and analyzing the information they collected too large for any one person or even a small group of people to manage.  


Enter the Age of Professionalization.  The 1800's saw the rise of the staff system and the modern civil service to help generals and leaders manage all the things these more modern militaries and governments had to do.  Of course, there had always been courtiers and others to do the king's business but now there was a need for a large number of professionals to deal with the ever-growing complexities of society.  The need for more professionals, in turn, demanded standardized processes that could be taught.  

For me, the Age of Professionalization lasted until the end of World War II when the Age of Institutionalization began.  Governments, particularly the US Government, began to see the need for permanent and relatively large intelligence organizations as a fundamental part of government.   
Logos of the CIA And KGB
Staffs and budgets grew.  Many organizations came (more or less) out of the shadows.  CIA, KGB, MI5 (and 6), ISI, and MSS all became well known abbreviations for intelligence agencies.  The need for intelligence-like collection and analysis of information became obvious in other areas.  Law enforcement agencies, businesses, and even international organizations started to develop "intelligence units" within their organizational structures.  


All of this lasted until about 1994 when, with the advent of the World Wide Web, the Age of Democratization began.   Seven years ago (!), I wrote an article called "Top Five Things Only Spies Used To Do But Everyone Does Now."  I talked about a whole bunch of things, like using sophisticated ciphers to encrypt data and examining detailed satellite photos, that used to be the purview of spies and spies alone.  Since then, it has only gotten worse.  Massive internet based deception operations and the rise of deepfake technology is turning us all into spymasters, weighing and sorting information wheat from information chaff.  Not only the threats but also the opportunities have grown exponentially.   For savvy users, there is also more good information, a greater ability to connect and learn, to understand the things that are critical to their success or failure but are outside their control, than ever before--and to do this on a personal rather than institutional level.

There are a couple of additional teaching points worth making here.  First is the role of information technology in all of this.  As the technology for communicating and coordinating activities has improved, the intelligence task has become more and more complicated.  This, in turn, has required the use of more and more people to manage the process, and that has changed how the process is done.  Other disciplines have been forced to evolve in the face of technological change.  It is no surprise, then, that intelligence is also subject to similar evolutionary pressures.

It is also noteworthy, however, that the various ages of intelligence have tended to become shorter with the near-logarithmic growth in technological capabilities.  In fact, when you map the length of the four ages on a logarithmic scale (see below) and draw a trendline, you can see a pretty good fit.  It also appears that the length of the current age, the Age of Democratization, might be a bit past its sell-by date.  This, of course, begs the question:  What age comes next?  I'm voting for the Age of Anarchy...and I am only half kidding.


Is this a perfect way of thinking about the history of intelligence?  No, of course not.  There are many, many exceptions to these broad patterns that I see.  Still, in a survey class, with limited time to cover the topic, I think focusing on these broad patterns that seemed to dominate makes some sense.  

Friday, May 10, 2019

I Am Leaving Mercyhurst And...

Look, Ma!  An (almost) clean desk!
... joining the US Army War College!

It has been an honor and a privilege to work with the faculty here in the Intelligence Studies Department at Mercyhurst over the last 16 years.  Having the opportunity to help build a world class program is an experience I will never forget.

As important as my colleagues, however, are the extraordinary students I have had the pleasure to teach and work with.  Whether we were sweating in the halls of the old Wayne Street building or livin' large in our fancy, new digs in the Center for Academic Engagement, getting to work with really smart, super dedicated students was probably the best thing about the job.  Watching them continue to grow and succeed as alumni is even more rewarding.  I am convinced that, one day, the DNI will almost certainly be a Mercyhurst alum (Several Directors of Strategic Intelligence for some Fortune 500 companies already are).

As much as I am sorry to leave Mercyhurst, I am very excited about my next position as Professor Of Strategic Futures at the War College.  There are few missions as important as developing strategic leaders and ideas for the US Army and I am proud to be part of the effort.

I expect to be out of my office here by the end of the month, so, if you have any last minute business to attend to, please reach out soon.  After the end of the month, the best way to reach me until I get to Carlisle in July is via gmail (kris dot wheaton at gmail dot com).  Once I have new contact info, I will post it.

I fully expect to continue to publish new thoughts, articles, and anything interesting I run across here on Sources and Methods.  In fact, I expect to be able to write more often.  

Stay tuned!  It's about to get (more) interesting...

Tuesday, March 19, 2019

What's The Relationship Of An Organization's Goals And Resources To The Type Of Intelligence It Needs?

"Don't blame me, blame this!"
I was trying to find some space on the whiteboard in my office and it occurred to me that I really needed to do something with some of these thoughts.

One of the most interesting (to me, at least) had to do with the relationship between an organization's goals and its resources coupled with the notion of tactical, operational and strategic intelligence.

There is probably not an entry level course in intelligence anywhere in the world that does not cover the idea of tactical, operational and strategic intelligence.  Diane Chido and I have argued elsewhere that these three categories should be defined by the resources that an organization risks when making a decision associated with the intel.  In other words, decisions that risk few of an organization's resources are tactical while those that risk many of the organizations's resources are strategic.  Thus, within this context, the nature of the intelligence support should reflect the nature of the decision and the defining characteristic of the decision is the amount of the organization's resources potentially at risk.   

That all seemed well and good, but it seemed to me to be missing something.  Finally (Diane and I wrote our article in 2007, so you can draw your own conclusions...), it hit me!  The model needed to also take into consideration the potential impact on the goals and purposes of the organization.

Here's the handy chart that (hopefully) explains what I mean:


What I realized is that the model that Diane and I had proposed had an assumption embedded in it.  In short, we were assuming that the decisionmaker would understand the relationship between their eventual decision, the resources of the organization, and the impact the decision would have on the organization's goals.  

While there are good reasons to make this assumption (decisionmakers are supposed to make these kinds of calculations, not intel), it is clearly not always the case.  Furthermore, adding this extra bit of nuance to the model makes it more complete.

Let's take a look at some examples.  If the impact on resources of deciding to pursue a particular course of action is low but the pay-off is high, that's a no-brainer (Example:  You don't need the DIRNSA to tell you to have a hard-to-crack password).  Of course you are going to try it!  Even if you fail, it will have cost you little.  Likewise, if the impact on resources is high and the impact on goals is low, then doing whatever it is you are about to do is likely stupid (Example:  Pretty much the whole damn Franklin-Nashville Campaign).

While many of these elements may only be obvious after the fact, to the extent that these kinds of things are observable before the decision is made, reflecting on them may well help both intelligence professionals and decisionmakers understand what is needed of them when confronted by a particular problem.  

Tuesday, February 12, 2019

How To Write A Mindnumbingly Dogmatic (But Surprisingly Effective) Estimate (All 3 Parts)

At the top end of the analytic art sits the estimate.  While it is often useful to describe, explain, classify or even discuss a topic, what, as Sun Tzu would say, "enables the wise sovereign and the good general to strike and conquer, and achieve things beyond the reach of ordinary men, is foreknowledge."  Knowing what is likely (or unlikely) to happen is much more useful when creating a plan than only knowing what is happening.

Estimates are like pizza, though.  There are many different ways to make them and many of those ways are good.  However, with our young analysts, just starting out in the Mercyhurst program, we try to teach them one good, solid, never fail way to write an estimate.  You can sort of think of it as the pepperoni pizza of estimates.

Here's the formula:

  • Good WEP +
  • Nuance +
  • Due to's +
  • Despite's +
  • Statement of AC = 
  • Good estimate!
I'm going to spend the rest of this article breaking this down.  

Outline of this article (Click on link to see full map)

Good (Best!) WEPs

Let's start with what makes a good Word of Estimative Probability - a WEP.   Note:  Linguistic experts call these Verbal Probability Expressions and if you want to dive into the literature - and there's a lot - you should use this phrase to search for it.  

WEPs should first be distinguished from words of certainty.  Words of certainty, such as "will" and "won't" typically don't belong in intelligence estimates.  These words presume that the analyst has seen the future and can speak with absolute conviction about it.  Until the aliens get back with the crystal balls they promised us after Roswell, it's best if analysts avoid words of certainty in their estimates.


Notice I also said "good" WEPs, though.  A good WEP is one that effectively communicates a range of probabilities and a bad WEP is one that doesn't.  Examples?  Sure!  Bad WEPs are easy to spot:  "Possibly", "could", and "might" are all bad WEPs.  They communicate ranges of probability so broad that they are useless in decisionmaking.  They usually only serve to add uncertainty rather than reduce it in the minds of decisionmakers.  You can test this yourself.  Construct an estimate using "possible" such as "It is possible that Turkey will invade Iraq this year."  Then ask people to rank the likelihood of this statement on a scale of 1-100.  Ask enough people and you will get everything from 1 TO 100.  This is a bad WEP.


Good WEPs are generally interpreted by listeners to refer to a bounded range of probabilities.  Take the WEP "remote" for example.  If I said "There is a remote chance that Turkey will invade Iraq this year" we might argue if that means there is a 5% chance or a 10% chance but no one would argue that this means that there is a 90% chance of such an invasion.


The Kesselman List
Can we kick this whole WEP thing up a notch?  Yes, we can.  It turns out that there are not only "good" WEPs but there are "best" WEPs.  That is, there are some good WEPs that communicate ranges of probabilities better than others.  Here at Mercyhurst, we use the Kesselman List (see above).  Alumna Rachel Kesselman wrote her thesis on this topic a million years ago (approx.).  She read all of the literature then available and came up with a list of words, based on that literature, that were most well defined (i.e. had the tightest range of probabilities).  The US National Security Community has its own list but we like Rachel's better.  I have written about this elsewhere and you can even read Rachel's thesis and judge for yourself.  We think the Kesselman List has better evidence to support it.  That's why we use it.  We're just that way.

Before I finish, let me say a word about numbers.  It is entirely reasonable and, in fact, may well be preferable, to use numbers to communicate a range of probabilities rather than words.  In some respects this is just another way to make pizza, particularly when compared to using a list where words are explicitly tied to a numerical range of probabilities.  Why then, do I consider it the current best practice to use words?  There are four reasons:

  • Tradition.  This is the way the US National Security Community does it.  While we don't ignore theory, the Mercyhurst program is an applied program.  It seems to make sense, then, to start here but to teach the alternatives as well.  That is what we do.  
  • Anchoring bias.  Numbers have a powerful place in our minds.  As soon as you start linking notoriously squishy intelligence estimates to numbers you run the risk of triggering this bias.  Of course, using notoriously squishy words (like "possible") runs the risk of no one really knowing what you mean.  Again, a rational middle ground seems to lie in a structured list of words clearly associated with numerical ranges.
  • Cost of increasing accuracy vs the benefit of increasing accuracy.  How long would you be willing to listen to two smart analysts argue over whether something had an 81% or an 83% chance of happening?  Imagine that the issue under discussion is really important to you.  How long?  What if it were 79% vs 83%?  57% vs 83%?  35% vs 83%?  It probably depends on what "really important" means to you and how much time you have.  The truth is, though, that wringing that last little bit of uncertainty out of an issue is what typically costs the most and it is entirely possible that the cost of doing so vastly exceeds the potential benefit.  This is particularly true in intelligence questions where the margin of error is likely large and, to the extent that the answers depend on the intentions of the actors,  fundamentally irreducible.  
  • Buy-in.  Using words, even well defined words, is what is known as a "coarse grading" system.  We are surrounded with these systems.  Our traditional, A, B, C, D, F grading system used by most US schools is a coarse grading system as is our use of pass/fail on things like the driver's license test.  I have just begun to dig into the literature on coarse grading but one of the more interesting things I have found is that it seems to encourage buy-in.  We may not be able to agree on whether it is 81% or 83% as in the previous example, but we can both agree it is "highly likely" and move on.  This seems particularly important in the context of intelligence as a decision-support activity where the entire team (not just the analysts) have to take some form of action based on the estimate.  
Nuance

WEPs are important but they clearly aren't the only thing.  What adds value to an estimate is its level of nuance.

Let me give you an example of what I mean:  
  • The GDP of Yougaria is likely to grow.
  • The GDP of Yougaria is likely to grow by 3-4% over the next 12 months.
Both of these are estimates and both of these use good WEPs but one is obviously better than the other.  Why?  Nuance.

Mercyhurst Alum Mike Lyden made a stab at defining what we mean by "nuance" in his 2007 thesis, The Efficacy of Accelerated Analysis in Strategic Level Intelligence Estimates.  There he defined it as how many of the basic journalistic questions (Who, What, When, Why, Where, and How) the estimate addressed.  

For example, Mike would likely give the first estimate above a nuance score of 1.  It really only answers the "What" question.  I think he would give the second estimate a 3 as it appears to answer not only the "What" question but also the "When" and "How (or how much)" questions as well.  Its not a perfect system but it makes the point.

In general, I think it is obvious that more nuance is better than less.  A more nuanced estimate is more likely to be useful and it is less likely to be misinterpreted.  There are some issues that crop up and need to be addressed, however - nuances to the nuance rule, if you will.
  • What if I don't have the evidence to support a more nuanced estimate?  Look at the second estimate above.  What if you had information to support a growing economy but not enough information (or too much uncertainty in the information you did have) to make an estimate regarding the size and time frame for that growth?  I get it.  You wouldn't feel comfortable putting numbers and dates to this growth.  What would you feel comfortable with?  Would you be more comfortable with an adverb ("grow moderately")?  Would you be more comfortable with a date range ("over the next 6 to 18 months")?  Is there a way to add more nuance in any form with which you can still be comfortable as an analyst?  The cardinal rule here is to not add anything that you can't support with facts and analysis - that you are not willing to personally stand behind.  If, in the end, all you are comfortable with is "The economy is likely to grow" then say that.  I think, however, if you ponder it for a while, you may be able to come up with another formulation that addresses the decisionmaker's need for nuance and your need to be comfortable with your analysis.
  • What if the requirement does not demand a nuanced estimate?  What if all the decisionmaker needed to know was whether the economy of Yougaria was likely to grow?  He/She doesn't need to know any more to make his/her decision.  In fact, spending time and effort to add nuance would actually be counterproductive.  In this case, there is no need to add nuance.  Answer the question and move on.  That said, my experience suggests that this condition is rather more rare than not.  Even when DMs say they just need a "simple" answer, they often actually needs something, well, more nuanced.  Whether this is the case or not is something that should be worked out in the requirements process.  
  • What if all this nuance makes my estimate sound clunky?  So, yeah.  An estimate with six clauses in it is going to be technically accurate and very nuanced but sound as clunky and awkward as a sentence can sound.  Well-written estimates fall at the intersection of good estimative practice and good grammar.  You can't sacrifice either, which is why they can be very hard to craft.  The solution is, of course, to either refine your single estimative sentence or to break up the estimative sentence into several sentences.  In my next post on this, where I will talk about "due to's and "despite's", I will give you a little analytic sleight of hand that can help you with this problem.

Due to's And Despite's

Consider again the estimate from above:  "The GDP of Yougaria is likely to grow 3-4% over the next 12 months."  Why?  Why do you, the analyst, think this is the case?  

Typically, there are a few key facts and some accompanying logic that are acting as drivers of these kinds of estimates.  It may have something to do with trade, for example, or with new economic opportunities opening up in the country.  It may be more about where the country is in the business cycle than anything else.  For whatever reason, these are the critical facts and logic that underpin your entire estimate.  If you are wrong about these drivers, because of incomplete collection, poor analysis or deliberate deception, your estimate is likely wrong as well.

I call these factors "due to's" because you can easily see them as a "due to" clause added to the estimate:  
"Due to a substantial increase in trade and the discovery of significant oil reserves in the northern part of the country, the GDP of Yougaria will likely increase 3-4% over the next 12 months."
If "due to's" are driving your faith in your estimate, "despite" clauses are the ones undermining it.  In any non-trivial exercise in estimation there are likely many facts which undermine your estimate.  In the example above, yes, there was an uptick in trade and the oil reserves are great but what about the slight increase in unemployment last month?  Or the reduction in consumer confidence?  

Much more than mere procatalepsis (gosh, I love that word...), the true intent behind the "despite" clause is to be intellectually honest with the decisionmaker you are supporting as an intelligence professional.  In short, you are saying two things to that DM.  First, "I recognize that not all of the facts available support my estimate"  and, second, "despite this, I still believe my estimate is accurate."  

How might that play itself out in our example?  
Despite recent increases in unemployment, the GDP of Yougaria is likely to grow 3-4% over the next 12 months.  Increases in trade have been strong and the recently discovered oil reserves in the northern part of the country will likely drive significant growth over the next year.
Or
Due to a substantial increase in trade and the discovery of significant oil reserves in the northern part of the country, the GDP of Yougaria will likely increase 3-4% over the next 12 months.  While unemployment recently ticked upward, this is likely due to seasonal factors and is only temporary.
These are just examples, of course, and the actual formulation depends on the facts at hand.  The goal remains the same in all cases - here's what I think, here's why, here's why not and here's why the "why nots" don't matter. 

Analytic Confidence

If the estimate is what the analyst thinks is likely or unlikely to happen then analytic confidence can most easily be thought of as the odds that the analyst is wrong.  Imagine two analysts in two different parts of the world have been asked to assess Yougaria's economy for the next year.  One is a beginner with no real experience or contacts in Yougaria.  His sources are weak and he is under considerable time pressure to produce.  The other analyst, operating wholly independently of the first, is a trained economist with many years experience with Yougaria.  His sources are excellent and he has a proven track record of estimating Yougaria's economic performance.  

Now, imagine that both of them just so happen to come to the exact same estimative conclusion - Yougaria's GDP is likely to grow 3-4% over the next 12 months.  Both report their estimative conclusions to their respective decisionmakers.  

It is not too difficult to see that the decisionmaker of the first analyst might be justifiably hesitant to commit significant resources based on this estimate of Yougaria's economic performance.  Absent additional analysis, it is quite obvious that there are a number of good reasons why the analyst in this case might be wrong.  

The decisionmaker supported by the second analyst is in exactly the opposite position.  Here there are very good reasons to trust the analyst's estimate and to commit to courses of action that are premised on its accuracy.  In the first case we could say that the analytic confidence is low while in the second case we could say it is high.  

What are the factors that suggest whether an analyst is more likely to be right or wrong?  Some of the earliest research on this was done by a Mercyhurst alum, Josh Peterson.  In his thesis he went out and looked for research based reasons why a particular analyst is more likely to be right or wrong.  He managed to identify seven reasons:
  • How good are your sources?
  • How well do your independent sources corroborate each other?
  • Are you a subject matter expert?  (This is less important than you might think, however.)
  • Did you collaborate with other analysts and exactly how did you do that? (Some methods are counterproductive.) 
  • Did you structure your thinking in a way proven to improve your forecasting accuracy? (A number of commonly taught techniques don't work particularly well BTW.)
  • How complex did you perceive the task to be?
  • How much time pressure were you under?
Josh would be the first person to tell you the flaws in his research.  For a start, he doesn't know if this list is complete nor does he know how much weight each factor should receive.  In general, then, there is a lot more research to be done on the concept of analytic confidence.  That said, we do know some things and it would be intellectually dishonest not to give decisionmakers some sense of our level of confidence when we make our estimates.

What does this look like in practice?  Well, I tend to think the best we can do right now is to divide the concept of confidence into three levels.  Humans are usually pretty good at intuitively spotting the very best or the very worst but not so good with rank ordering things in the middle.  I teach students that this means that the most common assessment of analytic confidence is likely moderate with high and low reserved for those situations where the seven factors are either largely present or largely absent.  

What then would our Yougarian estimate look like with analytic confidence added to the mix?
Due to a substantial increase in trade and the discovery of significant oil reserves in the northern part of the country, the GDP of Yougaria will likely increase 3-4% over the next 12 months.  While unemployment recently ticked upward, this is likely due to seasonal factors and is only temporary. 
Analytic confidence in this estimate is moderate.  The analyst had adequate time and the task was not particularly complex.  However, the reliability of the sources available on this topic was average with no high quality sources available for the estimate.  The sources available did tend to corroborate each other however, and analyst collaboration was very strong.
Final Thoughts

This is not not the only way to write an effective estimate.  There are other formulations that likely offer equal or even greater clarity.  There is clearly a need for additional research in virtually all of the elements outlined here.  There is also room for more creative solutions that convey the degree of  uncertainty with more precision, encourage analyst buy-in, and communicate all of that more effectively to the decisionmakers supported.

The overly dogmatic "formula" discussed here is, however, a place to start.   Particularly useful with entry-level analysts who may be unused to the rigor necessary in intelligence analysis, this approach helps them create "good enough" analysis in a relatively short time while providing a sound basis for more advanced formulations.

Monday, February 11, 2019

How To Write A Mindnumbingly Dogmatic (But Surprisingly Effective) Estimate (Part 3 - "Due to's", "Despite's" and Analytic Confidence)

In the first post on this topic, I introduced the idea of a "formula" for a pretty good estimate.  I also talked about using good words of estimative probability.  In the second post, I talked about nuance and what I mean by that term.  In this post, the last post of the series, I want to talk about the last three elements of this approach to writing a good estimate, "due to's", "despite's" and analytic confidence.

Outline of this part of the series (Click link to see full version)
Due to's And Despite's

Consider again the estimate from Part 2 of this series:  "The GDP of Yougaria is likely to grow 3-4% over the next 12 months."  Why?  Why do you, the analyst, think this is the case?  

Typically, there are a few key facts and some accompanying logic that are acting as drivers of these kinds of estimates.  It may have something to do with trade, for example, or with new economic opportunities opening up in the country.  It may be more about where the country is in the business cycle than anything else.  For whatever reason, these are the critical facts and logic that underpin your entire estimate.  If you are wrong about these drivers, because of incomplete collection, poor analysis or deliberate deception, your estimate is likely wrong as well.

I call these factors "due to's" because you can easily see them as a "due to" clause added to the estimate:  
"Due to a substantial increase in trade and the discovery of significant oil reserves in the northern part of the country, the GDP of Yougaria will likely increase 3-4% over the next 12 months."
If "due to's" are driving your faith in your estimate, "despite" clauses are the ones undermining it.  In any non-trivial exercise in estimation there are likely many facts which undermine your estimate.  In the example above, yes, there was an uptick in trade and the oil reserves are great but what about the slight increase in unemployment last month?  Or the reduction in consumer confidence?  

Much more than mere procatalepsis (gosh, I love that word...), the true intent behind the "despite" clause is to be intellectually honest with the decisionmaker you are supporting as an intelligence professional.  In short, you are saying two things to that DM.  First, "I recognize that not all of the facts available support my estimate"  and, second, "despite this, I still believe my estimate is accurate."  

How might that play itself out in our example?  
Despite recent increases in unemployment, the GDP of Yougaria is likely to grow 3-4% over the next 12 months.  Increases in trade have been strong and the recently discovered oil reserves in the northern part of the country will likely drive significant growth over the next year.
Or
Due to a substantial increase in trade and the discovery of significant oil reserves in the northern part of the country, the GDP of Yougaria will likely increase 3-4% over the next 12 months.  While unemployment recently ticked upward, this is likely due to seasonal factors and is only temporary.
These are just examples, of course, and the actual formulation depends on the facts at hand.  The goal remains the same in all cases - here's what I think, here's why, here's why not and here's why the "why nots" don't matter. 

Analytic Confidence

If the estimate is what the analyst thinks is likely or unlikely to happen then analytic confidence can most easily be thought of as the odds that the analyst is wrong.  Imagine two analysts in two different parts of the world have been asked to assess Yougaria's economy for the next year.  One is a beginner with no real experience or contacts in Yougaria.  His sources are weak and he is under considerable time pressure to produce.  The other analyst, operating wholly independently of the first, is a trained economist with many years experience with Yougaria.  His sources are excellent and he has a proven track record of estimating Yougaria's economic performance.  

Now, imagine that both of them just so happen to come to the exact same estimative conclusion - Yougaria's GDP is likely to grow 3-4% over the next 12 months.  Both report their estimative conclusions to their respective decisionmakers.  

It is not too difficult to see that the decisionmaker of the first analyst might be justifiably hesitant to commit significant resources based on this estimate of Yougaria's economic performance.  Absent additional analysis, it is quite obvious that there are a number of good reasons why the analyst in this case might be wrong.  

The decisionmaker supported by the second analyst is in exactly the opposite position.  Here there are very good reasons to trust the analyst's estimate and to commit to courses of action that are premised on its accuracy.  In the first case we could say that the analytic confidence is low while in the second case we could say it is high.  

What are the factors that suggest whether an analyst is more likely to be right or wrong?  Some of the earliest research on this was done by a Mercyhurst alum, Josh Peterson.  In his thesis he went out and looked for research based reasons why a particular analyst is more likely to be right or wrong.  He managed to identify seven reasons:
  • How good are your sources?
  • How well do your independent sources corroborate each other?
  • Are you a subject matter expert?  (This is less important than you might think, however.)
  • Did you collaborate with other analysts and exactly how did you do that? (Some methods are counterproductive.) 
  • Did you structure your thinking in a way proven to improve your forecasting accuracy? (A number of commonly taught techniques don't work particularly well BTW.)
  • How complex did you perceive the task to be?
  • How much time pressure were you under?
Josh would be the first person to tell you the flaws in his research.  For a start, he doesn't know if this list is complete nor does he know how much weight each factor should receive.  In general, then, there is a lot more research to be done on the concept of analytic confidence.  That said, we do know some things and it would be intellectually dishonest not to give decisionmakers some sense of our level of confidence when we make our estimates.

What does this look like in practice?  Well, I tend to think the best we can do right now is to divide the concept of confidence into three levels.  Humans are usually pretty good at intuitively spotting the very best or the very worst but not so good with rank ordering things in the middle.  I teach students that this means that the most common assessment of analytic confidence is likely moderate with high and low reserved for those situations where the seven factors are either largely present or largely absent.  

What then would our Yougarian estimate look like with analytic confidence added to the mix?
Due to a substantial increase in trade and the discovery of significant oil reserves in the northern part of the country, the GDP of Yougaria will likely increase 3-4% over the next 12 months.  While unemployment recently ticked upward, this is likely due to seasonal factors and is only temporary. 
Analytic confidence in this estimate is moderate.  The analyst had adequate time and the task was not particularly complex.  However, the reliability of the sources available on this topic was average with no high quality sources available for the estimate.  The sources available did tend to corroborate each other however, and analyst collaboration was very strong.
Final Thoughts

This is not not the only way to write an effective estimate.  There are other formulations that likely offer equal or even greater clarity.  There is clearly a need for additional research in virtually all of the elements outlined here.  There is also room for more creative solutions that convey the degree of  uncertainty with more precision, encourage analyst buy-in, and communicate all of that more effectively to the decisionmakers supported.

The overly dogmatic "formula" discussed here is, however, a place to start.   Particularly useful with entry-level analysts who may be unused to the rigor necessary in intelligence analysis, this approach helps them create "good enough" analysis in a relatively short time while providing a sound basis for more advanced formulations.