# AHP.Beta: Probabilistic AHP using Beta PERT distributions In CPP: Composition of Probabilistic Preferences (CPP)

## Description

This function computes criteria weights, using AHP and randomic pair-wise evaluations by Beta PERT distributions.

## Usage

 `1` ```AHP.Beta(n, s, list) ```

## Arguments

 `n` Random numbers created from Beta PERT distributions, using the parameters 'min', 'mean' and 'max' of each pair-wise criteria comparison elicited from the experts. `s` Shape of a Beta PERT distribution, as described in package "mc2d". There is no default value, however the higher the shape the higher the kurtosis, which emulates the precision of data elicited from experts. `list` List of pair-wise comparison matrices of expert opinions. The function 'list' is embedded in R.

## Value

Weights returned from a simulation of AHP with Beta PERT distributions. The weights are driven from the simulated matrix that gives the minimum AHP Consistent Ratio.

## References

Saaty, Thomas L. (1980). The analytic hierarchy process: planning, priority setting, resource allocation, McGraw-Hill.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ```n=5000 # simulation s=6 # shape of Beta PERT distribution # Expert pair-wise evaluations Exp.1 = matrix(c(1,0.2,0.3,5,1,0.2,3,5,1),3,3) Exp.2 = matrix(c(1,2,8,0.5,1,6,0.12,0.16,1),3,3) Exp.3 = matrix(c(1,0.5,0.5,2,1,6,2,0.16,1),3,3) Exp.4 = matrix(c(1,3,4,0.3,1,0.5,0.25,0.3,1),3,3) Exp.5 = matrix(c(1,4,5,0.25,1,1,0.2,1,1),3,3) list = list(Exp.1,Exp.2,Exp.3,Exp.4,Exp.5) AHP.Beta(n,s,list) ```

CPP documentation built on May 2, 2019, 1:34 p.m.