Autoencoders for strategic decision support
This addresses the need for data-driven tools in executive domains to improve strategic decisions, though it is incremental in applying existing autoencoder methods to new data.
The paper tackled the problem of supporting strategic decision-making by introducing autoencoders to provide granular feedback, and found that experts are inconsistent in decisions, with the method validated on industry datasets for ranking accuracy and synergy with humans.
In the majority of executive domains, a notion of normality is involved in most strategic decisions. However, few data-driven tools that support strategic decision-making are available. We introduce and extend the use of autoencoders to provide strategically relevant granular feedback. A first experiment indicates that experts are inconsistent in their decision making, highlighting the need for strategic decision support. Furthermore, using two large industry-provided human resources datasets, the proposed solution is evaluated in terms of ranking accuracy, synergy with human experts, and dimension-level feedback. This three-point scheme is validated using (a) synthetic data, (b) the perspective of data quality, (c) blind expert validation, and (d) transparent expert evaluation. Our study confirms several principal weaknesses of human decision-making and stresses the importance of synergy between a model and humans. Moreover, unsupervised learning and in particular the autoencoder are shown to be valuable tools for strategic decision-making.