Most people are loss averse. Meaning that the pain of losing something is felt about twice as strongly as the pleasure of gaining something.

Most people are also fairly bad at intuitively understanding risk. We understand certainty (0% and 100%) but tend to get everything in between muddled up. Some people err on the downside (thinking things are less likely than they really are) and some people err on the upside (thinking things are more likely than they really are).

If we combine being loss averse with also erring on risk’s downside, we will act extra conservatively.

If we are loss averse and err on the upside with risk, the two might actually manage to cancel themselves out a bit. In other words, even though we continue to be loss averse, we’re also biased to underestimate the chances of losing something and overestimate the chances of gaining something. In this case, two biases make us act, effectively, unbiased.

Important distinction: It would be an error to label people with this double-bias as risk-tolerant. They aren’t necessarily seeing bets and making them, but rather systematically misunderstanding situations to be less risky than they really are.

If this counter-balanced double-bias system does “work” by effectively giving us the “correct” risk analysis of multiple situations, and we notice this, then confidence in our own intuition gets ratcheted up. We believe it’s because we’re good at understanding risk, knowing when to bet and when not to.

Overconfidence will reduce our loss aversion, and make us double down on our intuitive understanding of risk. Thus toppling the perfectly balanced biases.

Then, the biases get out of sync, our hot streak (another cognitive illusion) ends, our confidence falls, loss aversion jumps back up, and maybe we spend a cycle on the side of erring on risk’s downside.

It’s a fragile system where we occasionally reach an optimal state of counter-balanced biases that will only last as long as we don’t acknowledge it as true intuition (aka avoid the illusion of control bias).

Good luck out there!