Loss Aversion

Theoretical Foundations and Distinction from Risk Aversion

While often conflated, loss aversion and traditional risk aversion are distinct Cognitive Bias mechanisms for decision-making under uncertainty.

Risk aversion, a theory established around 300 years ago in 1738, describes a preference for a certain outcome over a risky prospect with a higher expected value, represented by a utility curve that increases at a decreasing rate.

In this traditional model, the emphasis given to losses relative to gains is a function of the curve's curvature, usually assumed to be constant across different decisions.

Loss aversion, introduced as a central tenet of Prospect Theory in 1979, posits that individuals evaluate outcomes relative to a specific reference point, typically the current asset position. This model identifies a sudden, dramatic increase in the weight assigned to losses to the left of this reference point, creating a sharp corner in the utility function.

Unlike traditional theory, Prospect Theory suggests that a decision-maker behaves as risk-averse when all outcomes are gains relative to the reference point, but becomes risk-seeking when all outcomes are perceived as losses.

The Choice Process: Editing and Evaluation

The human decision-making process under risk consists of two primary phases: editing and evaluation. In the editing phase, prospects are organised and reformulated to simplify the subsequent choice. This phase involves several critical operations:

• Coding: Outcomes are defined as gains or losses relative to a neutral reference point rather than final states of wealth.

• Combination: Identical outcomes are simplified by combining their associated probabilities.

• Segregation: Riskless components are separated from risky ones.

• Cancellation: Components or outcome-probability pairs shared by multiple prospects are discarded, a phenomenon known as the isolation effect.

• Simplification: Extremely unlikely outcomes are often discarded or rounded.

Following the editing phase, the individual evaluates the simplified prospects and selects the one with the highest overall value. This value is determined by two scales: a subjective value function that measures deviations from the reference point and a weighting function that reflects the impact of probabilities on the overall desirability of the prospect.

Mathematical Mechanics: Value and Weighting Functions

The value function is generally concave for gains and convex for losses, meaning the marginal value of both decreases with magnitude. It is characteristically steeper for losses than for gains, reflecting the observation that the aggravation of losing a sum of money is greater than the pleasure of gaining it. Empirical evidence suggest that the pain of a loss is approximately twice as powerful as the joy of a gain.