Loss Aversion & Prospect Theory

Here’s a bet. I flip a fair coin: heads, you win $110; tails, you lose $100. The math is friendly — on average you make $5 every time you play, a better edge than any casino will ever offer you. Take it as often as you like. And yet almost nobody will play even once. The thought of losing $100 looms so much larger than the thought of winning $110 that the extra ten dollars can’t close the gap. You are not being irrational about money. You are being loss-averse — and that single asymmetry, it turns out, quietly reshapes a staggering range of the choices you make.

In 1979, two psychologists, Daniel Kahneman and Amos Tversky, took that gut feeling and turned it into the most influential theory of decision-making of the last half-century: prospect theory, the work that later won Kahneman a Nobel Prize. Their central discovery is deceptively simple. We don’t evaluate outcomes as final states of wealth, the way the tidy textbook of expected value assumes. We evaluate them as gains and losses measured from a reference point — and losses loom larger than gains, by a factor of roughly two. Lose $100 and you’ll feel it about twice as hard as you’d enjoy finding $100 on the pavement. That number — the loss-aversion coefficient, around 2.25 in their experiments — is one of the most robust findings in all of behavioral science.

This course takes that asymmetry and follows it everywhere it leads, because it leads almost everywhere. You’ll meet the value function — the kinked, S-shaped curve that makes us cautious about gains and reckless about losses in the same breath. You’ll see why the reference point you happen to adopt silently decides whether you feel rich or robbed, and why someone else gets to choose it for you. You’ll watch a single medical program flip from popular to unpopular when it’s described in terms of lives saved versus lives lost — the notorious framing effect — even though the numbers never change. You’ll learn why you irrationally cling to things you already own (the endowment effect), why you’d rather do nothing than risk an active mistake (status-quo bias), and why the same person rationally buys both a lottery ticket and an insurance policy (probability weighting). And because a model you can’t defend against is just a nicer name for a weakness, you’ll finish with the practical craft of debiasing: widening the frame, aggregating your bets, and asking the question that defuses half of these traps at once — what’s my reference point, and did someone else pick it?

It builds directly on two earlier ideas: thinking in probabilities (so the “rational” expected-value baseline we’re departing from is already in hand) and incentives (because framing is the cheapest incentive of all — it changes behavior without changing a single payoff). By the end, you won’t just know that losses hurt more. You’ll catch the asymmetry in the act — in your portfolio, your negotiations, your insurance, your sunk costs, and the carefully worded choices other people keep putting in front of you — and you’ll know what to do about it.