afsd.test: Compares prospects based on AFSD

In RSD: Compares Random Distributions using Stochastic Dominance

Compares prospects based on AFSD

Description

It compares two prospects using AFSD criteria, that is the prospect having the minimum violation area from a classic FSD.

Usage

afsd.test(sd.obj)

Arguments

sd.obj

StochasticDominance object.

Details

The returned list has six elements: 'winner' indicates the dominant prospect index. It will be zero if neither dominates the other. 'epsilon' is the ratio of violated area to the total area between the CDFs. 'area' is a vector, where the values show the area between the CDFs correspond to each segment. 'total.area' is the total area between the CDFs. 'positive.area' is the amount of area where the 'area' vector is positive, meaning the 'cdf1' is larger than 'cdf2'. 'negative.area' is like 'positive.area' for negative values.

If neither distribution dominates the other by AFSD, the 'winner' output will be zero, and it happens only when the expected value of distributions are equal.

The 'epsilon' and 'winner' output parameters are the ones that should be taken most. The others are the calculation details and are provided for further investigation. A lower the 'epsilon', lower the violation ratio of the dominant distribution, lower the eliminated extreme utilities, higher the number of decision-makers who agree on the dominant distribution.

Value

A list, including all the calculation details.

See Also

[area.btwn.cdfs.calc()] for area calculations.

Examples

sd = createStochasticDominance(outcome1 = c(1,4,7),
                               outcome2 = c(2,3,5),
                               prob1 = c(1/3,1/3,1/3),
                               prob2 = c(1/6,1/6,2/3))
afsd.test(sd)

RSD documentation built on June 21, 2025, 5:09 p.m.