CPP.mb {CPP}

CPP with multiple perspectives for decision-making, based on the 'Moneyball' principle.

Description

The algorithm evaluates alternatives by integrating the CPP-Tri, the CPP-Malmquist, the CPP-Gini, the alternatives' market values and the CPP by axes. The CPP-mb was originally applied in sports science to evaluate players' performance.

Usage

CPP.mb(t1, t2, m, q, s)

Arguments

t1

Decision matrix of Alternatives (rows) and Criteria (columns) in the moment '1'. Benefit criteria must be positive and cost criteria negative.

t2

Decision matrix of Alternatives (rows) and Criteria (columns) in the following moment '2'. Benefit criteria must be positive and cost criteria negative.

m

Vector of alternatives' market values.

q

Vector of quantiles, indicating the classes' profiles.

s

Shape of a Beta PERT distribution, as described in the package 'mc2d'. There is no default value, however the higher the shape the higher the kurtosis, which emulates the precision of data.

Value

Class assigns the alternatives to classes, defined by the indicated profiles. The list of classes also shows the decision matrices to be modeled by CPP-PP. CPP-mb indicates the final scores per class.

References

Sant'Anna, Annibal P. (2015). Probabilistic Composition of Preferences: Theory and Applications, Springer.

Lewis, Michael. (2004) Moneyball: The art of winning an unfair game. WW Norton & Company.

Gaviao, Luiz O. & Lima, Gilson B.A. (2017) Support decision to player selection: an application of the CPP in soccer, Novas Edições Acadêmicas [in Portuguese].

Examples

## Decision matrix of the previous moment '1'.
Alt.1 = c(2,30,86,-5)
Alt.2 = c(4,26,77,-12)
Alt.3 = c(3,22,93,-4)
Alt.4 = c(6,34,65,-10)
Alt.5 = c(5,31,80,-8)
Alt.6 = c(6,29,79,-9)
Alt.7 = c(8,37,55,-15)
Alt.8 = c(10,21,69,-11)
t1 = rbind(Alt.1,Alt.2,Alt.3,Alt.4,Alt.5,Alt.6,Alt.7,Alt.8)
## Decision matrix of the following moment '2'.
Alt.1 = c(3,29,82,-3)
Alt.2 = c(6,28,70,-8)
Alt.3 = c(2,20,99,-8)
Alt.4 = c(5,31,62,-14)
Alt.5 = c(9,27,73,-5)
Alt.6 = c(4,33,85,-13)
Alt.7 = c(9,39,59,-10)
Alt.8 = c(8,19,77,-9)
t2 = rbind(Alt.1,Alt.2,Alt.3,Alt.4,Alt.5,Alt.6,Alt.7,Alt.8)
m = c(100,120,150,140,90,70,110,130) # Market values
q = c(0.65,0.35) # quantiles of class profiles
s = 4 # Shape
CPP.mb(t1,t2,m,q,s)