Gamble

Gamble constructs a gamble object with probability vector p and outcome vector v. Vectors p and v are sorted seperately for gains and losses in ascending order of the absolute value of v. Subarrays for p and v for gains and losses are stored for internal calculations for prospect theory.

• p: probability vector
• v: outcome vector
• pg: probability vector for gains
• vg: outcome vector for gains
• pl: probability vector for losses
• vl: outcome vector for losses

Constructor

Gamble(;p=[.5,.5], v=[10.0,0.0])

ProspectTheory

ProspectTheory constructs a model object for cummulative prospect theory. By default, parameters for utility curvature and probability weigting are equal gains and losses.

• α: utility curvature for gains
• β: utility curvature for losses
• γg: probability weighting parameter for gains
• γl: probability weighting parameter for losses
• λ: loss aversion parameter

Constructor

ProspectTheory(;α=.80, β=α, γg=.70, γl=γg, λ=2.25)

References

Fennema, H., & Wakker, P. (1997). Original and cumulative prospect theory: A discussion of empirical differences. Journal of Behavioral Decision Making, 10(1), 53-64.

Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and uncertainty, 5(4), 297-323.

compute_weights

compute_weights computes decision weights based on cummulative outcomes

• p: a probability vector
• γ: parameter that controls weighting of low and high probabilities

Function Signature

compute_weights(p, γ)

expected_utility

expected_utility computes the expected utility given a model and a gamble

• model: a model object for prospect theory
• gamble: a gamble object

Function Signature

expected_utility(model::ProspectTheory, gamble)