Mathematical Psychology
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Value Function

The value function in Prospect Theory is defined over gains and losses relative to a reference point, exhibiting concavity for gains, convexity for losses, and a steeper slope for losses than for gains.

v(x) = xᵅ for x ≥ 0; v(x) = −λ(−x)ᵝ for x < 0

The value function is the central component of Prospect Theory, replacing the utility function of expected utility theory. Three key properties distinguish it: reference dependence (outcomes are evaluated as changes from a reference point, not as final states), diminishing sensitivity (marginal value decreases with distance from the reference point), and loss aversion (losses loom larger than gains of equal magnitude).

Functional Form

Tversky & Kahneman (1992) Parameters v(x) = xᵅ for x ≥ 0
v(x) = −λ(−x)ᵝ for x < 0

α = β ≈ 0.88 (diminishing sensitivity)
λ ≈ 2.25 (loss aversion coefficient)

The exponents α and β (both less than 1) produce concavity for gains and convexity for losses — reflecting diminishing sensitivity in both domains. A gain of $100 feels less than twice as good as $50, and a loss of $100 feels less than twice as bad as $50. The loss aversion parameter λ ≈ 2.25 means that losses are weighted about 2.25 times more heavily than gains of equal size.

Empirical Evidence

The value function's properties have been confirmed across many cultures, stimulus types (money, goods, time), and elicitation methods. Loss aversion explains the endowment effect (people demand more to sell an object than they would pay to buy it), the status quo bias, and the equity premium puzzle in finance. The exact value of λ varies across studies (1.5 to 2.5) but the qualitative pattern of loss aversion is remarkably robust.

Interactive Calculator

Each row represents a gamble outcome: outcome (monetary value, can be negative for losses) and probability. The calculator applies Kahneman & Tversky's prospect theory value function and probability weighting to compute subjective value.

Click Calculate to see results, or Animate to watch the statistics update one record at a time.

Related Topics

References

  1. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185
  2. Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297–323. https://doi.org/10.1007/BF00122574
  3. Booij, A. S., van Praag, B. M. S., & van de Kuilen, G. (2010). A parametric analysis of prospect theory's functionals for the general population. Theory and Decision, 68(1–2), 115–148. https://doi.org/10.1007/s11238-009-9144-4

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