AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |
Back to Blog
Ahp decision making4/19/2023 ![]() ![]() Instead of the relative errors, the models with absolute errors similar to (11), (12) can be tried for the odds (14)–(16) or the shares (17)–(19) models too. Let us make several additional remarks useful for numerical modeling. It can help to decision makers to focus on the respondents’ individual features and to increase customer satisfaction, their retention and loyalty to the promoted brands or products. The considered methods are useful for segmentation of the respondents and finding optimum managerial solutions specific for each segment. Numerical results show that the suggested approaches reveal useful features of priority behavior, that can noticeably extend the AHP abilities and applications for numerous multiple-criteria decision making problems. ![]() Then the priorities can be predicted for individual respondents, profiled by each predictor, forecasted in time, studied by the predictor importance, and estimated by the characteristic of significance, fit and quality well-known in regression modeling. For example, in multi-person decision making, the priorities can be obtained in regression modeling by the demographic and socio-economic properties. For multiple experts, or panel data, or other data with varied characteristics of measurements, the priority vectors can be built as functions of the auxiliary predictors. Priority vectors in the Analytic Hierarchy Process (AHP) are commonly estimated as constant values calculated by the pairwise comparison ratios elicited from an expert. ![]()
0 Comments
Read More
Leave a Reply. |