6 Things To Think About While Discussing Requirements With A Decisionmaker (Part 2)

"And what kind of intelligence would the gentleman prefer today?"

How can I use the limited amount of time my decisionmakers have to discuss their intelligence requirements to get the maximum return on that investment?  Earlier this summer, I began a series on this precise theme.

I have already written about how to prepare for an intelligence requirements meeting and about how to deal with a virtual intelligence requirements environment.  Today, I am writing part two of a six part series discussing what intel professionals need to think about when they are actually in the meeting, talking to a decisionmaker about his or her requirements.

2.  What kind of intelligence is the DM looking for?

There are two broad (and informal) categories of intelligence - descriptive and estimative.  Descriptive intelligence is about explaining something that is relevant to the decision at hand.  Estimative intelligence is about what that "something" is likely to do next.  It is the difference between "Who is the president of Burkina Faso now?" and "Who is the next president of Burkina Faso likely to be?"

Estimative intelligence is obviously more valuable than descriptive intelligence.  Estimative intelligence allows the DM and his or her operational staff to plan for the future, to be proactive instead of reactive.  Surprisingly, though, DMs often forget to ask for estimates regarding issues they think will be relevant to their decisions.  It is worth the intelligence professionals time, therefore, to look for places where an estimate might be useful and suggest it as an additional requirement.

While I am never one to look for more work, the truth is that descriptive intelligence is becoming easier and easier to find.  The real value in having dedicated intel staff is in that staff's ability to make estimates.  If all you do is what computers do well (IE describe) then you run the risk of being downsized or eliminated the next time there is a budget crunch.

Tomorrow:  #3 What are the DM's assumptions?

6 Things To Think About While Discussing Requirements With A Decisionmaker

An intel professional successfully gets everything he needs from a
DM in a requirements briefing.  Guess which one is the unicorn...

How can I use the limited amount of time my decisionmakers have to discuss their intelligence requirements to get the maximum return on that investment?  Earlier this summer, I began a series on this precise theme.

I have already written about how to prepare for an intelligence requirements meeting and about how to deal with a virtual intelligence requirements environment.  Today, I am writing part one of a six part series discussing what intel professionals need to think about when they are actually in the meeting, talking to a decisionmaker about his or her requirements.

1.  Does the DM really want intelligence?

It goes without saying that an organization's mission is going to drive its intel requirements.  Whether the goal is to launch a new product line or take the next hill, decisionmakers need intel to help them think through the problem.

Unfortunately, DMs often conflate operational concerns ("What are we going to do?" kinds of questions) with intel concerns ("What is the other guy going to do?" kinds of questions).  This is particularly true in a business environment where intelligence as a distinct function of business is a relatively new concept.

Good intelligence requirements are typically about something which is important to an organization's success or failure but which is also outside that organization's control.  Good intelligence requirements are, in short, about the "other guy" - the enemy, the competitor, the criminal - or, at least, about the external environment.

Intelligence professionals need to be able to extract intelligence requirements from this broader conversation, play them back to the DM to confirm that both parties understand what needs to be done before they go to work.

Tomorrow:  #2 What kind of intelligence is the DM looking for?

How To Write A Mindnumbingly Dogmatic (But Surprisingly Effective) Estimate (Part 2 - Nuance)

In my last post on this topic, I outlined what I considered to be a pretty good formula for a pretty good estimate:

• Good WEP +
• Nuance +
• Due to's +
• Despite's +
• Statement of AC = 
• Good estimate!

I also talked about the difference between good WEPs, bad WEPs and best WEPs and if you are interested in all that, go back and read it.  What I intend to talk about today is the idea of nuance in an estimate.

Outline of the series so far (Click for full page version)

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.  I am currently writing a three part series on this and you can find Part 1 here and Part 2 here.  Part 3 will have to wait until a little later in the summer.
• 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.

Farengar Secret-Fire Has A Quest For You! Or What Video Games Can Teach Us About Virtual Intel Requirements

A couple of weeks ago, I wrote a post about the 3 Things You Must Know Before You Discuss Intelligence Requirements With A Decisionmaker.  That post was designed for intel professionals who have the luxury of being able to sit down with the decisionmakers they support and have a conversation with them about what it is they want from their intelligence unit.  I also stated that doing this in a virtual environment or on an automated requirements management system like COLISEUM was both more difficult and something I would discuss in the future.

Well, today is your lucky day!  The future is here!

When I think about who does requirements best in a virtual environment, I think about video games.  Particularly, I think about massively multi-player online role-playing games (MMORPGs for short).  Very, very specifically, I think about the questing systems that are standard fare in virtually all of these types of games.  

Quests are the requirements statements of games.  I have included an example of a quest below (it is from a game called Skyrim which is awesome and highly recommended).  These quests differ from intel requirements in that almost all of them are operationally focused (What do we need to do to accomplish our mission?) instead of intelligence focused (What do we need to know about the other guy to accomplish our mission?).  That said, there are still a number of things we can learn from a well-formulated quest that will make intel requirements in a virtual environment easier to craft and to understand.

Retrieved from Immersive Questing

Video game designers know they have to get quests right the first time.  They don't have an opportunity to talk to the players outside the game so all the necessary information needs to be in the quest itself.  On the other hand, they have to make the quest seem realistic.  Failing to maintain this balance runs the risk of creating an unplayable game.  As a result, video game designers have developed a number of conventions that allow the quests to sound real but be complete.  Intel has the "real" part down so it is about making sure that it is complete that matters.  In this respect, the final version of a good virtual intel requirement bears a remarkable resemblance to the final version of a good quest.  Here are the specifics:

• They both provide background.  Why am I doing this?  What is the context for this quest?  In video games, putting the quest in context allows the story to unfold.  In intelligence work, this context allows the intelligence professional to better understand the decisionmaker's intent.  This, in turn, allows the intelligence professional to have a better understanding of the kinds of information and estimates that will prove most useful.
• They both define terms.  In the quest above, I am to look for a Dragonstone.  What is a Dragonstone?  The quest defines that for me.  In intelligence work, agreeing on definitions of terms (particularly common terms) is incredibly helpful.  For example, you get a request to do a stability study on Ghana.  What term needs to be defined before you go ahead?  Stability.  We do this exercise every year in our intro classes.  There are multiple definitions of stability out there.  Which one is most appropriate for this decisionmaker is a critical question to ask and answer before proceeding.
• They both use terms consistently.  If I encounter another quest asking for me to find a Dragonstone, I can count on it being the same thing I am looking for in this quest.  Likewise, in an intelligence requirement, if I define a term in a certain way in one place, I will use that term - not what I think is a synonym, no matter how reasonable it sounds to me - consistently throughout the requirement.  
• They both often come in standard formats.  All video game players are familiar with a variety of standard quest formats such as the Fetch Quest (like the one above) where the task is to go, get something, and bring it back.  Intelligence requirements also come in more-or-less standard forms such as requests for descriptive or estimative intelligence.  Categorizing requests for intelligence and then studying them for similarities should allow an intelligence unit to develop a list of useful questions to ask based simply on the type of request it is.   

Requirements statements, whether managed in person or virtually, are almost always going to start out messy.  Without the advantage of a back-and-forth, personal conversation, the virtual requirements process has a greater potential, however, for breakdown.  Thinking of the requirement as a quest allows intelligence professionals to re-frame the process and focus on the essential elements of the requirement and, perhaps,  anticipate and address predictable points of potential failure in advance.

Look for the final part of this series later this summer when I talk about all the things you need to think about in the middle of requirements discussion with a decisionmaker!

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

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.

How to Write a Mindnumbingly Dogmatic (but Surprisingly Effective) Estimate (Outline)

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 next couple of posts breaking this down.  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 Syria 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 Syria 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.  

I'll talk about the rest of the "formula" later in the summer!