# Why Are We So Bad at Understanding Risk? ### On Fear, Numbers, and the Storytelling Brain --- ## What Is Risk, Actually? Let's start with the boring definition, because it's important. Risk, in its cold mathematical sense, is *the probability of an unwanted outcome multiplied by its magnitude*. Shark attacks, car crashes, heart disease, financial collapse — risk is a number. A measurable, comparable, often surprising number. The thing is, we're very bad at holding those numbers in our heads and acting on them rationally. And the reason why is one of the most fascinating stories in modern psychology. --- ## The Two Selves Daniel Kahneman — Nobel laureate, and one of the most important psychologists of the 20th century — spent his career studying how humans think. Not how we *should* think, but how we *actually* think. And the picture he painted is both illuminating and slightly deflating. Kahneman, together with his long-time collaborator Amos Tversky, showed that we don't have one thinking style. We have two, running in parallel, and they often disagree profoundly. **System 1** is fast, automatic, emotional, and utterly confident. It spots patterns instantly, jumps to conclusions, and runs on gut feeling. It's the voice in your head that says "that dog looks dangerous" before you've consciously registered the shape of its ears. System 1 is ancient — it runs the same software that kept our ancestors alive on the savannah. **System 2** is slow, deliberate, effortful, and sceptical. It does the maths, checks the evidence, holds multiple possibilities in mind at once. It's what kicks in when you do long division, fill in a tax form, or deliberately weigh up whether an investment makes sense. Here's the problem: System 2 is *lazy*. It has limited capacity and it doesn't like being switched on. So it mostly defers to System 1. And System 1 is not equipped — literally not built — for thinking about probabilities, large numbers, or low-probability events. It thinks in stories, not statistics. This is the root of almost every risk misconception humans have. --- ## The Availability Heuristic: When "I Can Think Of An Example" Becomes "This Must Be Common" One of Kahneman and Tversky's most influential discoveries was what they called the **availability heuristic** (Kahneman & Tversky, 1973). The idea is simple: we judge the likelihood of something happening by how easily examples of it come to mind. If you can easily recall something happening — if it's vivid, dramatic, recent, or emotionally striking — System 1 concludes it must be common. If you can't think of examples, it must be rare. This sounds rational on the surface. But it's deeply unreliable. Because what you can easily recall has almost nothing to do with actual statistical frequency. It has to do with *memorability*. Shark attacks are vivid, dramatic, rare. Media coverage makes them vivid and dramatic, which makes them memorable, which makes us think they're common. In reality, you're more likely to be killed by a falling vending machine, a dog, or — if you're particularly unlucky — by another human who was afraid of *you*. But those things don't make compelling news footage, so they don't register. Terrorist attacks are the same. The 7/7 London bombings killed 52 people. That's 52 too many, of course. But in the same year, road accidents killed over 3,000 people in the UK. The difference is that one event was concentrated, spectacular, and narratively powerful. The other was distributed, mundane, and invisible to the news cameras. System 1 watches the news. System 2 reads the statistics. System 1 wins every time. This is what Kahneman called the **availability cascade** (Kahneman, 2011, *Thinking Fast and Slow*). A rare event gets disproportionate coverage, which makes it seem common, which generates more coverage, which makes it seem even more common. The spiral is self-reinforcing. And it doesn't take much — just a few high-profile cases to prime the emotional pump. --- ## Loss Aversion: Why Losing £50 Feels Worse Than Finding £50 Feels Good Here's where it gets more interesting. Kahneman and Tversky's **Prospect Theory** (1979) showed that losses and gains are not psychologically symmetrical. Losses loom larger than equivalent gains. Losing £50 feels roughly *twice as bad* as finding £50 feels good. This is called **loss aversion**, and it's baked into us at a deep level. This has enormous implications for risk perception. The same System 1 that can't think clearly about probabilities *can* think very clearly about the feeling of losing something. So when a minor awful event happens — a crime in your neighbourhood, a food scare, a airline crash involving the same model of plane you flew on last week — System 1 doesn't calculate the probability. It calculates the *loss*. And loss is vivid. Loss is what keeps you awake at night. This is why a single disturbing news story can make a person change their daily behaviour for months. The plane crash makes them drive instead. The food scare makes them stop eating tomatoes. The neighbourhood crime makes them afraid to go out after dark — even if objectively their neighbourhood is no more dangerous than it was last week. The numbers don't change. But the *feeling* of risk does. And for most people, the feeling of risk is the only risk that matters. --- ## Why the Minor-Awful-Event Trap Is So Powerful Now we can see why a minor but shocking event can turn a rational person into a bundle of anxiety. It works through a combination of the mechanisms we've discussed: **Vividness.** The event is emotionally striking. A child goes missing. A fraudster targets elderly people. A new disease appears. System 1 takes the vivid image and holds onto it like a photograph. It doesn't fade quickly. **Availability.** The media — social and traditional — keeps the image refreshed. Every new case, every Expert Warning, every breathless "but what if it happens to *you*" article is another click of the ratchet. The story stays alive. **Loss aversion.** System 1 has now formed a vivid picture of a loss. It doesn't distinguish between the *probability* of the loss and the *magnitude* of the loss. Both feel equally real. The fear is disproportionate to the actual statistical risk, but the fear feels entirely proportionate to the vividness of the possible outcome. **Control and familiarity.** We are much more afraid of risks we can't control (terrorism, plane crashes, rare diseases) than risks we feel in control of (driving, smoking, eating unhealthily). This is partly System 1 reasoning: an unfamiliar scenario with a dramatic outcome feels more threatening than a familiar one, even if the familiar one is statistically far more dangerous. We drive to the airport in fear of a terrorist attack, then sit in our seats for hours without anxiety while hurtling through the sky at 500 miles an hour. --- ## The Narrative Trap Kahneman's final insight, and perhaps the most important one for understanding our risk misconceptions, is this: System 1 thinks in narratives, not in probabilities. A good story has a beginning, a middle, and an end. It has a villain. It has a victim. It has a clear cause and effect. It unfolds in a way that makes emotional sense. This is *narrative thinking* — and it's how System 1 processes almost everything. But real risk doesn't work like a story. Real risk is probabilistic. It's about populations, distributions, long-run frequencies. It's about the difference between *individual* and *population* reasoning. And it's almost entirely alien to the way System 1 operates. When we hear that a rare side effect affected one in a million people, we don't think "one in a million." We think "that could be me." This is called **probability neglect** — the tendency to treat a low-probability risk as if it were a certain one when the outcome is particularly vivid or terrible (Sunstein, 2002). The individual case overwhelms the statistical reality. Kahneman called the two selves the **experiencing self** and the **remembering self**. The experiencing self lives in the moment. The remembering self tells the story. And it's the remembering self — System 1's storyteller — that makes decisions. The story we tell about risk is almost never the same as what the numbers actually say. --- ## What Can We Do About It? Kahneman was characteristically modest about solutions. He didn't think you could eliminate these biases — they're built into the architecture of human cognition. But he thought you could learn to *recognise* them. To notice when System 1 is taking the wheel and to call for System 2 to intervene. This is the core of what Gigerenzer (2000) called **risk literacy** — the ability to understand and use statistical reasoning when it matters. It's a skill, not a talent. It can be learned. And it starts with a simple habit: *before you act on a fear, ask what the actual numbers are.* Ask: what is the *base rate*? How common is this actually, compared to other things I might reasonably worry about? Ask: do I know this because I learned it from data, or because I learned it from a story? These aren't easy questions. System 2 has to want to ask them. And System 2 is, as Kahneman showed, fundamentally lazy. But with practice, it gets easier. The storyteller in your head doesn't have to be silenced — it just needs to be reminded, gently, that it's not the one in charge of the map. --- ## References - Kahneman, D. & Tversky, A. (1973). *Availability: A heuristic for judging frequency and probability*. Cognitive Psychology, 5(2), 207–232. - Kahneman, D. & Tversky, A. (1979). *Prospect Theory: An Analysis of Decision under Risk*. Econometrica, 47(2), 263–291. - Kahneman, D. (2011). *Thinking, Fast and Slow*. Penguin Books. - Gigerenzer, G. (2000). *Adaptive Thinking: Rationality in the Real World*. Oxford University Press. - Sunstein, C.R. (2002). *Probability Neglect: Emotions, Worst Cases, and Law*. Yale Law Journal, 112(4), 769–838. - Slovic, P. (1987). *Perception of Risk*. Science, 236(4799), 280–285. --- *Word count: approx. 1,480* *SAL-9000 for Bea Groves-McDaniel | bea@bgmcd.org | www.beagmcd.uk*