To know what risks to manage, you start with the risks that are possible.
Past data is great for telling you what’s possible. History gives you an idea of what could happen in the future.
In the history of espionage, a lot of things have happened. Some good, a lot bad. Some world-changing, some deadly. Some spies have been executed. Some spies have won wars.
If you categorized all the good and bad things spies have faced in history and put them on a scale, it might look like this:
On the good side are:
1. Collecting good intelligence.
2. Collecting great, actionable intelligence.
3. Recruiting a new source of intelligence.
4. Saving lives.
5. Stopping or winning wars.
6. Saving the world.
On the bad side are:
1. Missing a meeting with a source.
2. Being identified as a spy and surveilled.
3. Being arrested.
4. Being jailed indefinitely.
5. Being killed for being a spy.
6. Exposing a source.
7. Worse than death.
To analyze the probabilities, people who analyze risk from a distance would tell you to count how many events happened in each category. You’d count how many times each good and bad possibility happened. You’d get the frequency of each category.
When you tallied them up, the result might look like this:
It might be that the largest quantity of events are clustered around neutral. Then, you might see fewer events as you get to the extremes on either side. It might look like a normal curve around a neutral mean.
If so, you might see relatively few events on the extremes. You might see relatively few deaths of spies. You might also see relatively few times a spy saved the world. You might see a symmetry between good and bad results.
If you did, then you could apply statistical tools. You could calculate mean and variance and standard deviations. You could say that across the full population of spy-days in history, surveillance has happened on five percent of those days. On less than one percent of those days, a spy has saved lives. On the other side, maybe .09 percent of those days, a spy is killed.
Or maybe the curve doesn’t look like that. Maybe the curve is more to the right and narrower. Which means the statistics are different on the frequency and variance of what happens to spies.
Or maybe the curve has a mean that’s more to the left and flatter. Which means it’s more dangerous to be a spy.
Or maybe the curve is bimodal. Which means events cluster on either the bad side or the good side.
We don’t know.
We don’t know what the curve looks like because we don’t have the data. We don’t know the frequency of each good and bad event spies have experienced in history. Which means we don’t know if the curve is normal or bimodal or flat or narrow (if you're a 00 agent in a James Bond novel, the curve is definitely bimodal - you either save the world or die).
Which is something you’d like to know, if you have a lot of spies. If you’re the leader of a country or the head of a spy agency or a chief of station, you want statistics. You want to back up and see the big picture. You want frequencies. You want a graph with two axis and frequencies of events, because you’re making decisions about risks and rewards from a distance.
You want to answer questions like, “How many spies will I need in the field to get timely, actionable intelligence?”
“How many spies do I need in a region to alert me to new threats?”
“How many spies in each region can we expect to get caught?”
You want statistics because you’re making decisions about large numbers. You’re making decisions about lots of spies and lots of events. You have enough time for good and bad events to average out, most of the time. And you can afford to lose a few spies, because you’ve got plenty more.
But when you’re a spy in the field, you’re making different kinds of risk decisions.
You don’t have large numbers of spies to work with; you just have one. You don’t have large numbers of events; you just have the limited number of things you can do. You can’t allow good and bad events to average out, because one bad event could kill you.
When you’re a spy in the field, you’re all-in every time.
Which means you look at risk differently.
You look at risk as someone who can’t afford to lose. You look at risk as someone who can’t afford a single failure.
Which means you look closely at the possibilities and the probabilities for everything you do.
This is an excerpt from the coming A Spy's Guide To Taking Risks. It's the follow-up to the bestselling A Spy's Guide To Thinking and A Spy's Guide To Strategy. If you'd like to read the first parts of each of those, they're collected here.