A couple of years ago, the Department of Homeland Security hired a bunch of science fiction writers to come in for a day and think of ways terrorists could attack America. If our inability to prevent 9/11 marked a failure of imagination, as some said at the time, then who better than science fiction writers to inject a little imagination into counterterrorism planning?
I discounted the exercise at the time, calling it “embarrassing.” I never thought that 9/11 was a failure of imagination. I thought, and still think, that 9/11 was primarily a confluence of three things: the dual failure of centralized coordination and local control within the FBI, and some lucky breaks on the part of the attackers. More imagination leads to more movie-plot threats — which contributes to overall fear and overestimation of the risks. And that doesn’t help keep us safe at all.
Recently, I read a paper by Magne Jørgensen that provides some insight into why this is so. Titled More Risk Analysis Can Lead to Increased Over-Optimism and Over-Confidence, the paper isn’t about terrorism at all. It’s about software projects.
Most software development project plans are overly optimistic, and most planners are overconfident about their overoptimistic plans. Jørgensen studied how risk analysis affected this. He conducted four separate experiments on software engineers, and concluded (though there are lots of caveats in the paper, and more research needs to be done) that performing more risk analysis can make engineers more overoptimistic instead of more realistic.
Potential explanations all come from behavioral economics: cognitive biases that affect how we think and make decisions. (I’ve written about some of these biases and how they affect security decisions, and there’s a great book on the topic as well.)
First, there’s a control bias. We tend to underestimate risks in situations where we are in control, and overestimate risks in situations when we are not in control. Driving versus flying is a common example. This bias becomes stronger with familiarity, involvement and a desire to experience control, all of which increase with increased risk analysis. So the more risk analysis, the greater the control bias, and the greater the underestimation of risk.
The second explanation is the availability heuristic. Basically, we judge the importance or likelihood of something happening by the ease of bringing instances of that thing to mind. So we tend to overestimate the probability of a rare risk that is seen in a news headline, because it is so easy to imagine. Likewise, we underestimate the probability of things occurring that don’t happen to be in the news.
A corollary of this phenomenon is that, if we’re asked to think about a series of things, we overestimate the probability of the last thing thought about because it’s more easily remembered.
According to Jørgensen’s reasoning, people tend to do software risk analysis by thinking of the severe risks first, and then the more manageable risks. So the more risk analysis that’s done, the less severe the last risk imagined, and thus the greater the underestimation of the total risk.
The third explanation is similar: the peak end rule. When thinking about a total experience, people tend to place too much weight on the last part of the experience. In one experiment, people had to hold their hands under cold water for one minute. Then, they had to hold their hands under cold water for one minute again, then keep their hands in the water for an additional 30 seconds while the temperature was gradually raised. When asked about it afterwards, most people preferred the second option to the first, even though the second had more total discomfort. (An intrusive medical device was redesigned along these lines, resulting in a longer period of discomfort but a relatively comfortable final few seconds. People liked it a lot better.) This means, like the second explanation, that the least severe last risk imagined gets greater weight than it deserves.
Fascinating stuff. But the biases produce the reverse effect when it comes to movie-plot threats. The more you think about far-fetched terrorism possibilities, the more outlandish and scary they become, and the less control you think you have. This causes us to overestimate the risks.
Think about this in the context of terrorism. If you’re asked to come up with threats, you’ll think of the significant ones first. If you’re pushed to find more, if you hire science-fiction writers to dream them up, you’ll quickly get into the low-probability movie plot threats. But since they’re the last ones generated, they’re more available. (They’re also more vivid — science fiction writers are good at that — which also leads us to overestimate their probability.) They also suggest we’re even less in control of the situation than we believed. Spending too much time imagining disaster scenarios leads people to overestimate the risks of disaster.
I’m sure there’s also an anchoring effect in operation. This is another cognitive bias, where people’s numerical estimates of things are affected by numbers they’ve most recently thought about, even random ones. People who are given a list of three risks will think the total number of risks are lower than people who are given a list of 12 risks. So if the science fiction writers come up with 137 risks, people will believe that the number of risks is higher than they otherwise would — even if they recognize the 137 number is absurd.
Jørgensen does not believe risk analysis is useless in software projects, and I don’t believe scenario brainstorming is useless in counterterrorism. Both can lead to new insights and, as a result, a more intelligent analysis of both specific risks and general risk. But an over-reliance on either can be detrimental.
Last month, at the 2009 Homeland Security Science & Technology Stakeholders Conference in Washington D.C., science fiction writers helped the attendees think differently about security. This seems like a far better use of their talents than imagining some of the zillions of ways terrorists can attack America.
This essay originally appeared on Wired.com.