Prospect Theory: A Framework for Understanding Cognitive Biases

Fills a gap in the LW canon with a clean explainer of Kahneman & Tversky's prospect theory: decisions are framed around a zero point and evaluated via a value function (steeper for losses -> loss aversion; leveling off -> scope insensitivity) and a probability-weighting function (small probabilities overweighted). The worked 'Prospero' hurricane-insurance example shows how reframing the same choice as losses vs. gains, or changing the hurricane probability while holding the insurance actuarially neutral, flips the decision. Maps the model onto previously-discussed biases (loss aversion, sunk cost, scope insensitivity, the 'but there's still a chance' curve), and honestly flags that prospect theory may hold better for hypothetical than real-money choices.

Quality 73 · Strong. Strong (lower). A lucid, well-exampled explainer of an important framework that connects cleanly to many biases. Held at 73 because it is essentially exposition of Kahneman & Tversky's established (Nobel-winning) theory. 73.

Claude’s paradigm shift 38 · Slight. Slight-to-moderate. Prospect theory is famous existing work; the post is accessible exposition, not a new contribution. 38.

Real-world impact 2 · Minor. A lucid, well-exampled explainer of Kahneman & Tversky's prospect theory (value function, loss aversion, probability-weighting) that connects cleanly to many biases. Conceptual/pedagogical influence within rationalist discourse, exposition of established theory, no material change — low RWI.