hyperbolic_logarithm_pwf: The hyperbolic-logarithm probability weighting function.

In gary-au/pt: Computational models for prospect theory and other theories of risky decision making

Description

The hyperbolic-logarithm probability weighting function is given by

w(p) = (1 - alpha * log(p))^(-beta/alpha)

where p is the probability constrained by

w(0) = 0, w(1) = 1, 0 < p < 1,

and the two parameters alpha and beta are constrained by

alpha > 0, beta > 0.

Usage

hyperbolic_logarithm_pwf(par, p)

Arguments

par	vector, contains the alpha and beta parameters for the pwf
p	numeric, the probability

References

Prelec, D. (1998). The probability weighting function. Econometrica, 60(3), 497-528.

p. 176, Luce, R. D. (2001). Reduction invariance and Prelec's weighting functions. Journal of Mathematical Psychology, 45(1), 167-179.

Footnote 3, p. 105, Stott, H. P. (2006). Cumulative prospect theory's functional menagerie. Journal of Risk and Uncertainty, 32(2), 101-130.

See Also

plotProbW, plotTwoParProbWFam

gary-au/pt documentation built on May 16, 2019, 5:41 p.m.