Notes on The HEAD Game: High-Efficiency Analytic Decision Making and the Art of Solving Complex Problems Quickly
When is the last time you had to make a big decision? When did you last have to act on incomplete or vague information? Most professional service careers require the ability to make decisions based on a store of specific knowledge applied in the context of various scenarios. Even in our daily lives we face difficult choices that can sometimes cause anxiety. That’s why it’s vital that in addition to working on building our knowledge base, we also look at how we make decisions and how we can improve our decision-making ability.
That is what The HEAD Game is all about. It’s written by former deputy director of the CIA counterterrorist center, Philip Mudd. In his career as a CIA analyst, the decision to act on any portion of gathered intelligence had very real life and death consequences. Therefore, it was imperative that there was a method to reduce errors and make the best decisions possible. This post seeks to summarize Mudd’s HEAD game methodology. It stands for High Efficiency Analytic Decision-Making, and it systematizes the process of gathering, sorting, and acting on information.
The art of thinking backwards
So how does one start to think like a CIA analyst? Well it starts with a mindset. Mudd calls this the art of thinking backwards. Before jumping headstrong into research and proposals, you have to start with what it is you (or your boss, client, etc…) need to know to solve a problem. This means starting at the finish and working backwards. This is not the first source to stress the importance of flipping problems on their head. The great mathematician Carl Jacobi used the maxim (oft quoted by the great Charlie Munger) “Invert. Always invert”. Start from the end and work backwards.
Ask the right questions
Second, you need to ensure that you are asking the right questions. My favorite example from the book is the picnic planning committee. If you are the head of a picnic planning committee, there are several questions you could ask if you had to determine whether or not to reschedule tomorrow afternoon’s scheduled picnic time.
“What will the weather be like tomorrow?”
“Will it rain tomorrow?”
“What is the probability of rain in the afternoon when we hope to start our picnic?”
Each of these questions could have a true and accurate answer, but the first two may not lead to the best decision. Imagine the following answers to the above questions.
“Rainy and cool”
“High probability of rain”
“Rain most likely in the morning. Probability of afternoon showers are low”
This example does a good job of showing the importance of being intentional in your line of questioning. The first two questions get answers that may persuade you to reschedule. However, the third question provides an answer that gives you more confidence to proceed with your afternoon festivities.
The problem with people in knowledge fields; law, medicine, finance, even tech, is that too often the expert focuses more on their own knowledge without truly examining what the customer needs. This can cause a break-down in communication that is all too common in expert-layperson relationships. Therefore, articulating the right question is paramount; and it’s what Mudd claims differentiates experts from analysts.
Identify the drivers
The next step in your decision-making process is to identify the most important drivers that can affect the outcome. This step is designed to organize the large amounts of data that may influence your decision. Each bit of information gets sorted into which driver it falls under.
A simplified example is that of buying a house. Imagine you are one half of a young couple with modest income and two kids aged 8 and 4. The possibility of having a third child has not been ruled out.
The question:
Which house is best suited for our family situation for the next 5 years? (notice how this question is specific and helps narrow the thought process. You will see that it also impacts the drivers.)
The drivers:
Strength of elementary and middle schools. (a broad survey of school districts may be influenced by strong high schools but that should not get taken into account as a primary driver due to time horizon.
Proximity and availability to quality affordable child-care.
Safety of neighborhood.
Price for the space needed and ease in converting or expanding the livable space (the possibility of a third child, suggests an option to expand such as finishing a basement, converting a study to a bedroom, etc… adds value to a selection)
Obviously, you could think of many other potential drivers. If you are an avid gardener, then space for a garden would be an obvious driver, or size of the garage if you are a car enthusiast. The point is that all the data points can then be sorted into each of the designated drivers, and any information that does not fall into those drivers should be filed under a miscellaneous category and not assigned as much weight as the key drivers in your decision.
Once you get enough information to fill out your drivers, it is important to assign a confidence level to them. Continuing from the example above, let’s say that you are highly confident the schools in the area you are looking in are great. You assign a high confidence level there. But what if you get conflicting reports on the quality and availability of child care? What if the daycares are expensive and have a couple bad reviews from reasonably reliable sources? What if the in-home providers are available now, but it’s possible they may not be at the time you need them? In this sort of situation, you may think that quality affordable healthcare should be available, but you would assign a middle level of confidence on that driver.
Measure Performance
The next step is to measure your performance. This is often a tricky task as too many people look at the outcome to grade performance instead of grading the process. I like to think of it like the poker player who bets big on a bad hand, and ends up getting a lucky draw to win the pot. He may say “see how great of a bet that was? I’m so good at poker.” when in reality he was just lucky. A good poker player will ask “did I make the right choice given the cards and information I had at the time?” He recognizes that you can win the pot on a terrible bet, and you can play just right and still take a bad-beat.
A good analyst will ask “did I successfully interpret the needs of my client, boss, spouse, etc…?”, “Did I ask the right questions?”, “Did I assign the right drivers?”, “Did I have the correct confidence in the information I relied on?”. You need to judge the process and not focus as hard as the outcome. Mudd quotes coach John Wooden to stress his point here.
“…I never focused on winning – didn’t even mention it. Rather, I did everything I could to make sure that all our players gave everything they had to give both in practice and in games. The score will take care of itself when you take care of the effort that precedes the score.” – John Wooden
What are we missing?
Finally, on all big decisions you need to have a strong devil’s advocate. The CIA has a term they use, which is being rapidly adopted by Silicon Valley firms, for the person or people who adopt this role. They are called the “red team”. In the zombie apocalypse movie World War Z, the country of Israel is incredibly better prepared than the rest of the world. They attributed this to what they called the 10th Man rule. The job of the red team or the 10th man, is to ask “What are we missing?”, “What if our drivers are not accurate?”, “What could go wrong?”, “What if seemingly anomalous data is relevant and important?”
In our personal and professional lives, when we are looking at problems, this most often will just take the form of just putting on our “red team” hat, or asking a trusted advisor, colleague, or loved one to play that role for us. We need to step back and look for what we could be missing.
Conclusion
Throughout our lives we are faced with decisions that can have huge consequences. Most of the time the data we have to use is incomplete, imprecise, or of questionable accuracy. It helps to have a system in place to approach these problems.
I do recommend reading this book for anyone interested in seeing more how the process can work in real life scenarios. Most of it is geared towards true analyst type positions where the task is gathering actionable information to a third party decision-maker, but its concepts can still be readily applied across many disciplines and scenarios. The stories from the CIA are interesting and relevant and the appendices in the back of the book offer great information that, as I said earlier, I am sure I will refer to again in the future. There are also some excellent tips on how to craft committees and teams to help in team driven decision-making that are worth the read.
To summarize how to apply the HEAD Game method of decision-making:
- Know the end-user’s goal, by thinking backwards.
- Ask the right questions
- Identify the correct drivers
- Grade the confidence of each designated driver
- Grade the process, not the outcome
- Put on your “red team” hat
Personally, I have adopted most of these principles. I remind myself constantly of the importance of knowing who my end-user is, inverting the problem to start with the solution first, make sure I am asking the right questions, and always seeking out counter arguments. I have a lot of room to improve in the area of assigning drivers and will be working this year on a way to better systematize that step. I’ll keep you posted on my progress.
So what are the biggest challenges you are facing today? Try playing the HEAD game to see if it can help you come to the best possible decision.
I recommend checking it out.
The HEAD Game: High-Efficiency Analytic Decision Making and the Art of Solving Complex Problems Quickly
Until next time…
“It takes a long time to learn that experts master data, but they only transition to analysts when they realize that an impressive recall of data has little to do with analysis and understanding complex problems. That’s what [this book is] about, making up for all the lost time in college classes that trained all of us to be experts but not efficient analysts.” – Philip Mudd
Further reading at Investing 101 – The 5 Best Books for Beginning Investors
*Feature photo is an image from the 2001 movie Spy Game from Universal. This humble writer has no affiliation with Mr. Redford, Mr. Pitt, or Universal Studios.