Analytic Hierarchy Process Suggesting Wrong Solution?

[spacecadet333]

I am working with a data frame:

LPA<- Censustracts[,c("Native_NH", "Black_NH", "White_NH", "Asian_NH", "Hispanic_tot",
                      "FB_Asia", "FB_Europe", "FB_Africa", "FB_Latin", "Und18", "Median_Age",
                      "MarriedwChild", "MarriednChild", "FemaleHHChild", "Highschool",
                      "NoHS","Bachelors", "Graduates", "Construction", "Management",
                      "Transportation", "HomeValue", "RentValue", "OwnerOcc",
                      "Rntocc", "Built39", "Built4049", "Built5059", "Built6069",
                      "Built7079", "Built8089", "Built9099", "Built2004", "Built2005",
                      "House1year", "SameCounty", "DifferentCounty", "DifferentState",
                      "FromAbroad")] %>% single_imputation() %>%
  estimate_profiles(1:5)

LPA %>% compare_solutions(statistics = c("AIC","AWE","BIC,"CLC","KIC"))

#I'm not too familiar with Bayesian Models/Statistics, but I can't understand how my compare solutions method could print these two separate results:

Compare tidyLPA solutions:

Model Classes AIC        AWE        BIC        CLC        KIC       
 1     1       -54508.582 -53419.085 -54157.834 -54662.582 -54427.582
 1     2       -60437.295 -58787.992 -59906.675 -60671.359 -60316.295
 1     3       -62870.178 -60661.126 -62159.687 -63184.249 -62709.178
 1     4       -64116.094 -61347.277 -63225.732 -64510.188 -63915.094
 1     5       -65391.322 -62062.782 -64321.089 -65865.397 -65150.322

Best model according to AIC is Model 1 with 5 classes.
Best model according to AWE is Model 1 with 5 classes.
Best model according to BIC is Model 1 with 5 classes.
Best model according to CLC is Model 1 with 5 classes.
Best model according to KIC is Model 1 with 5 classes.

An analytic hierarchy process, based on the fit indices AIC, AWE, BIC, CLC, and KIC (Akogul & Erisoglu, 2017), suggests the best solution is Model 1 with 1 classes. ```