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Nevertheless, the amount of applications testing for interactions among attributes in environmental valuation is very limited.
A fractional factorial design using the D-efficiency criteria was used to estimate interactions among attributes.
Although several multivariate discretization algorithms overcome the drawback of univariate discretization methods by considering interactions among attributes, there are some weaknesses.
While the three methodologies provide ease of decision-making independently in terms of handling vagueness of judgment, analyzing structures and interactions among attributes, and dealing with the dependence and feedback among attributes, respectively, when applied together as in a hybrid manner, this provides a more comprehensive representation of the decision problem.
The nonadditive set functions (labeled μ) describe such interactions among attributes and model their relative importance.
For example, linear additive scoring models do not include effects of preference interactions among attributes or domains but multiplicative scoring functions include these effects.
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The fact that univariate methods discretize only a single attribute at a time and do not consider interactions among attribute, may lead to important information loss [3] and not getting a global optimal result [22].
Thus, the estimated nonadditive set functions can capture the interaction among attributes and therefore can be used to model their relative importance.
Given the small sample size, the analysis did not assess preference interactions among the attributes.
Why is it important for the STEM education community to better understand the subtle interactions among individual attributes, contextual factors, and instructional practices?
This process involves specifying a particular form for the utility function and the possible preference interactions among the attributes [ 10, 11, 33].
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