  Machine learning has recently been widely adopted to address the managerial
decision making problems, in which the decision maker needs to be able to
interpret the contributions of individual attributes in an explicit form.
However, there is a trade-off between performance and interpretability. Full
complexity models are non-traceable black-box, whereas classic interpretable
models are usually simplified with lower accuracy. This trade-off limits the
application of state-of-the-art machine learning models in management problems,
which requires high prediction performance, as well as the understanding of
individual attributes' contributions to the model outcome. Multiple criteria
decision aiding (MCDA) is a family of analytic approaches to depicting the
rationale of human decision. It is also limited by strong assumptions. To meet
the decision maker's demand for more interpretable machine learning models, we
propose a novel hybrid method, namely Neural Network-based Multiple Criteria
Decision Aiding, which combines an additive value model and a fully-connected
multilayer perceptron (MLP) to achieve good performance while capturing the
explicit relationships between individual attributes and the prediction.
NN-MCDA has a linear component to characterize such relationships through
providing explicit marginal value functions, and a nonlinear component to
capture the implicit high-order interactions between attributes and their
complex nonlinear transformations. We demonstrate the effectiveness of NN-MCDA
with extensive simulation studies and three real-world datasets. To the best of
our knowledge, this research is the first to enhance the interpretability of
machine learning models with MCDA techniques. The proposed framework also sheds
light on how to use machine learning techniques to free MCDA from strong
assumptions.
