Rank Ordering The 26 Risks From The 2008 Global Risk Report (WEF)

The World Economic Forum just concluded in Davos, Switzerland. The annual meeting of many of the world's economic and political leaders is always worth watching but the most interesting document to come out this year, for me at least, was the Global Risk Report for 2008 (Download the full text here).

Put together by some pretty high speed, low drag thinkers from places like Citigroup, Swiss Re and the Wharton School Risk Center, the report is roughly equivalent to an NIE for the Davos crowd. The document makes estimates regarding the likelihood and the severity (in terms of lives and money) of 26 separate events over the next 10 years. Some, like the risk of a Katrina-like disaster in another major metropolitan area somewhere in the world, are based on what the report refers to as "traditional actuarial models". Other risks, such as seven identified geopolitical risks, have much greater uncertainty associated with them. All of the risks are rated on a 1-5 scale with a 1 = least likely or least severe and a 5 = most likely or most severe. The table below gives a summary of the risks, the scores associated with those risks and what exactly the numbers mean.



A couple of interesting things here. First, no single risk has a greater than 20% chance of occurring according to the authors. Second, the scales, in terms of severity, are not set at regular intervals for either lives or money. They spike upward very quickly, almost logarithmically, so that the differences between a 2 and a 3 and a 3 and 4, for example, are quite stark -- almost as if the economists of Davos were making a Richter Scale for risk. Third, some of the titles are a little obtuse. You will have to see the full text to understand what some of them mean.

Finally, though, I found it fascinating what the authors of the document didn't do. They didn't look at the risks from the standpoint of overall severity. In other words, they did not do what I did in the chart above -- create a single chart with all three variables in it. They chose to look at likelihood versus severity in terms of lives separate from likelihood versus severity in terms of cost. They created two charts and two tables to show how these variables worked together.

While these views were very interesting, it seemed to beg the question, "How should we rank order the risks?" Decisionmakers have to allocate resources (for a more detailed discussion of the central importance of this idea to notions of strategy and strategic intelligence see this article by me and Diane Chido). Typically, decisionmakers allocate more resources to attempt to mitigate or take advantage of the most important risks and allocate fewer resources to the less important risks. All of this seems like common sense until you try to rank order the risks. There are lots of methods for making this type of determination but one of the most common is expected value.

The concept behind expected value is simple: If I have a 30% chance of making 10 dollars, my expected value is .3 X 10 = 3 dollars. It can also lead to what might seem like a counterintuitive answer. Quick, which would you rather have, a 30% chance at 10 dollars or a 1% chance at $1000? While many people pick the first deal, the expected value of the first is only $3 while the expected value of the second deal is $10. Expected values can be either positive or negative. In the case of the Davos data, we are clearly talking about expected costs.

We already have the likelihood values and, since the World Economic Forum was kind enough to use 5 point scales for both of their severity ratings, the equation is pretty simple. The expected value of the Davos estimates would be the likelihood of occurrence multiplied by the average of the two severity ratings (cash and lives). In terms of rank ordering it makes no difference if I use the likelihood scale or the actual percentages associated with that scale. In order to keep things simple, I used the likelihood scale.

OK, OK, I hear you. You can't equate cash and lives (even if the fact that the authors put the two on the same five point scale almost begs you to do so...). This is, of course, the likely reason that the authors of the document would claim for not doing the expected value calculation that I am about to do. I think they are being a little too coy. Many of the guys who wrote this are actuaries or come from the insurance field. They put a dollar value on life every day. It is their job. I suspect that explaining how they do this and justifying their decision just seemed like something that was too difficult to do so they left us with a less useful document.

Which is a shame because I think there are at least two ways they could explain themselves that would have made this document more useful to decisionmakers. First, the intent behind using the combined severity scores is for the purpose of rank ordering not for the purpose of valuing. What we want here is some sense of which risk is overall most critical and, in general terms, how much worse is one risk than another. Second, there appears to be an inadvertent bias towards life in the assessment of overall severity. There are five total category 4 or 5 level risks with respect to severity in terms of cost. Only one of those has any associated cost in lives. On the other hand, there are also five category 4 or 5 risks with respect to severity in terms of lives lost. The severity in terms of cost for each of these, however, runs between 3 and 4, virtually guaranteeing that, assuming equal risks of both events, events with more lives lost will be more important than events where more money is lost.

In the end, I wish they had taken a crack at it because I know they are better mathematicians than I and would have done a better job of it. Anyway, here goes! When you do the math, the 26 risks for the next 10 years break down along these lines:



Lots of interesting stuff here as well. First, none of the risks get close to the maximum score of 25. Second, the top two risks are both what the World Economic Forum categorizes as "society" risks -- chronic disease and a pandemic. It is also interesting to see that terrorism is #18 out of 26, roughly equal to the collapse of the nuclear non-proliferation treaty in terms of its expected cost. Finally it is amazing to me how many of these risks could be readily and accurately assessed using exclusively open sources.

It is also instructive to look at the averages for each of the five sectors the WEF evaluated:



Clearly, society risks have the greatest potential cost while risks associated with technology pose the least expected cost. The other three categories, geopolitics, economy and environment, have roughly the same expected cost and lie between the two extremes.

One final comment; a thought experiment, really. Let's assume that the list is generally accurate, that these are the risks the world will face in the next ten years and that they are ranked more or less according to their level of importance. How do the intelligence resources that should be watching these risks stack up against the risks themselves? How much is spent on intelligence concerning chronic disease in the developed world and is that amount weighted appropriately given this issue's importance?

Clearly every one of these global risks has national security implications but not every one translates into a traditional national security priority. Which ones do the intelligence community cover and which ones do other agencies cover? Given that they are all interconnected (the full document even has a network diagram showing how they are all interconnected), where does the integrated picture emerge? Who is responsible for producing it?

Even in those areas where the intelligence community clearly has a stake, have resources been allocated appropriately? For example, failed and failing states have an expected cost that is roughly twice as much as international terrorism. Do we spend twice as much on intelligence concerning failed and failing states as we do on intelligence concerning terrorism? If we don't, are we being rational?

I can only speculate about the answers to these questions but I would be interested in your comments.

(Note: The full spreadsheets for the data above are available here)