HomeLoss AversionUnderstanding Loss Aversion vs. Acquiring Gains

Understanding Loss Aversion vs. Acquiring Gains

Loss aversion, what is it? The lives of 300 people are in your hands.

You’re a doctor dealing with a myserious epidemic. A treatment is available, but it’s risky. If you use it, 70% of the patients will live. Do you try it?

Let’s run things again.

You’re a doctor dealing with a myserious epidemic. A treatment is available, but it’s risky. If you use it, 30% of the patients will die. Do you try it?

Rationally, these two questions are the same: the same number of patients survives in each. Still, the questions probably felt different. The reason is the way they are framed: the first talks about lives saved, and the second talks about lives lost.

This is loss aversion at work. It’s a cognitive bias in all our thinking, and it could be affecting decisions you make every day.

Adding Biases: Classic Economics and Behavioral Economics

Classical economics looks at people as rational actors. Basically, the rational model assumes people are pretty good at making the right, or rational, decision for themselves. More specifically, it assumes that the description of a situation should not influence our decisions.

But, while that assumption makes things more convenient for theorists, that is not how people actually think. Instead of rationally evaluating every situation, we rely on heuristics, or general rules-of-thumb, to make decisions. That decreases the mental effort decisions require.

Luckily, these heuristics work fine in most situations. But in certain cases, they can lead to consistent errors. As Dan Ariely puts it in his book of the same title, we are all Predictably Irrational: we make the same mistakes, in the same ways, over and over.

So, economics needed to expand to include these irrational decisions. Enter behavioral economics, which considers how people actually think with heuristics instead of assuming we are all perfectly rational. Loss aversion is one, and perhaps the most important, of those heuristics.

How We Evaluate Gambles

The first people to really discuss loss aversion were two pioneers of behavioral economics: Daniel Kahneman and Amos Tversky. In a series of papers starting in the late 1970s, they looked at how people make decisions in moments of uncertainty.

They noticed that when considering gambles, people typically think in terms of gains and losses, rather than absolute states of wealth. Imagine you have $1,000 and want to buy $10 of stock that could one day be worth $110, or could lose all its value. Instead of comparing a big chance of $990 with a small chance of $1,100, we tend to think in terms of losing $10 and gaining $100.

The problem is we hate losing more than we like winning. In other words, the joy of finding $20 is not equal to the dismay of losing $20. Because of this loss aversion, we take fewer risks than we rationally should. We are risk-averse. For the same reason, we usually prefer a smaller sure thing over a bigger gamble because a sure thing eliminates the possibility of a loss.

Because people hate losing, they also become risk-seekers when facing a guaranteed loss: we’ll risk worse odds if it means we could eliminate our losses. This is why people go double-or-nothing, or why people buy insurance (it guarantees small loss to prevent a much bigger one).

Loss Aversion and Framing Effects

Contrary to classical economics, our decisions change depending on how a problem is described. In other words, by framing the outcome differently, as either a gain or a loss, you can change what you or someone else chooses.

In the doctor example above, a completely rational decision-maker would see that the situations are the same, and evaluate them both in the same way. However, we are not rational. More people will choose to try the treatment when it is framed as a gain than when it is framed as a loss.

Harvard medical school tried this exact experiment on loss aversion with actual physicians. The experimenters presented two cancer treatments, surgery and radiation. They described the treatments using either the percent of patients who survived, or the percentage of patients who died. The physicians were more likely to choose the treatment that was described