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Subjective Importance of a Common Feature Decides Its Consideration in Multi-attribute Decision-making.

ABSTRACT: One of the interesting research questions in multi-attribute decision-making is what affects the consideration of shared information (i.e., common features) between two alternatives. Previous studies have suggested two approaches (bottom-up and top-down) in finding what characteristics of common features affect their consideration. Two bottom-up factors (salience and interdependence) were found, but no top-down factors were discovered. In the current study, we followed the top-down approach and investigated how subjective importance (SI) of a common feature affects its consideration. In two studies, we consistently found that, on both the general and individual level, the level of consideration increased with the SI of the common feature. This result provided a new explanation for the effect of common feature consideration and its individual difference; it also provided insights in explaining the underlying process of multi-attribute decision making.

SUBMITTER: Wang Z

PROVIDER: S-EPMC7841338 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Subjective Importance of a Common Feature Decides Its Consideration in Multi-attribute Decision-making.

Wang Ziyi Z He Guibing G

Frontiers in psychology 20210114

One of the interesting research questions in multi-attribute decision-making is what affects the consideration of shared information (i.e., common features) between two alternatives. Previous studies have suggested two approaches (bottom-up and top-down) in finding what characteristics of common features affect their consideration. Two bottom-up factors (salience and interdependence) were found, but no top-down factors were discovered. In the current study, we followed the top-down approach and ...[more]

PMID: 33519602

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Project description:BackgroundNutritional manipulation with functional nutrients like polyunsaturated fatty acids can boost milk production efficiency in dairy farming. It is important to consider the animal's physiological periods, especially the second half of the first pregnancy for mammary gland development.ObjectivesBy considering multiple factors and comparing them, multi-attribute decision-making (MADM) can be utilized to conduct further assessments and select the best diet for the animals.MethodsForty primiparous Saanen does, from the last 2 months of pregnancy up to 4 months of lactation, have been assigned to four iso-energetic and iso-nitrogenous diets. Four dietary groups included: no external sources of fat (negative control, CT), saturated palm oil (positive control), roasted soybeans (omega-6, SB) and extruded flaxseed (omega-3, FS). Twenty-two performance criteria such as feed intake, milk yield and composition, body weight, blood metabolites and hormones, the milk fatty acid profile, as well as morphological and histological measurements of the mammary gland, in the form of least-square means, were considered.ResultsA decision-making tool was used to select the best form of fat supplements in late pregnancy and early lactation diets, to improve lactation performance in Saanen goats. For this purpose, a MADM method was applied to determine the order of preference similarity to the ideal solution. According to the score of this method, the FS group had the highest coefficients (0.689), and the CT group had the lowest coefficients (0.281).ConclusionsIncorporating flaxseed into the diets of Saanen goats during late pregnancy and early lactation is a valuable strategy for enhancing milk performance. This supplement is recommended as a source of fat. Additionally, the implementation of decision-making tools, such as the MADM method in animal science, can significantly improve management decision-making processes by reducing both time and cost. This presents a new avenue for making well-informed decisions.

Dataset Information

Subjective Importance of a Common Feature Decides Its Consideration in Multi-attribute Decision-making.

Publications

Subjective Importance of a Common Feature Decides Its Consideration in Multi-attribute Decision-making.

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