Autoencoders for strategic decision support

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

Cost-efficient staffing under annualized hours

We study how flexibility in workforce capacity can be used to efficiently match capacity and demand. Flexibility in workforce capacity is introduced by the annualized hours regime. Annualized hours allow organizations to measure working time per year, instead of per month or per week. An additional source of flexibility is hiring employees with different contract types, like full-time, part-time, and min-max, and by hiring subcontractors. We propose a mathematical programming formulation that incorporates annualized hours and shows to be very flexible with regard to modeling various contract types. The objective of our model is to minimize salary cost, thereby covering workforce demand, and using annualized hours. Our model is able to address various business questions regarding tactical workforce planning problems, e.g., with regard to annualized hours, subcontracting, and vacation planning. In a case study for a Dutch hospital two of these business questions are addressed, and we demonstrate that applying annualized hours potentially saves up to 5.2% in personnel wages annually

Effects of antiandrogens on transformation and transcription activation of wild-type and mutated (LNCaP) androgen receptors

LNCaP cells contain androgen receptors with a mutation in the steroid binding domain (Thr 868 changed to Ala) resulting in a changed hormone specificity. Both the wild-type and mutated androgen receptors were transfected into COS cells. Transcription activation was studied in cells co-transfected with an androgen sensitive reporter (CAT) gene. The wild-type androgen receptor was activated by the agonist R1881, but the antiandrogens did not enhance transcription apart from a partial agonistic effect at high concentrations of cyproterone acetate. The mutated androgen receptor was fully activated by R1881, cypoterone acetate and hydroxyflutamide, but not by ICI 176,334. Receptor transformation to a tight nuclear binding state was studied by preparation of detergent washed nuclei and Western blotting with a specific antibody against the androgen receptor. Nuclei of COS cells transfected with wild-type receptor retained the receptor when the cells had been treated with the agonist R1881, partially retained receptors when treated with antiandrogen cyproterone acetate, but did not retain receptor when treated with hydroxyflutamide or ICI 176,334. The cells transfected with the mutated receptor additionally retained nuclear receptors after treatment with hydroxyflutamide. We conclude that each one of the three antiandrogens tested displayed different characteristics with respect to its effect on transformation and transcription activation

Differentiable and Transportable Structure Learning

Directed acyclic graphs (DAGs) encode a lot of information about a particular distribution in their structure. However, compute required to infer these structures is typically super-exponential in the number of variables, as inference requires a sweep of a combinatorially large space of potential structures. That is, until recent advances made it possible to search this space using a differentiable metric, drastically reducing search time. While this technique -- named NOTEARS -- is widely considered a seminal work in DAG-discovery, it concedes an important property in favour of differentiability: transportability. To be transportable, the structures discovered on one dataset must apply to another dataset from the same domain. We introduce D-Struct which recovers transportability in the discovered structures through a novel architecture and loss function while remaining fully differentiable. Because D-Struct remains differentiable, our method can be easily adopted in existing differentiable architectures, as was previously done with NOTEARS. In our experiments, we empirically validate D-Struct with respect to edge accuracy and structural Hamming distance in a variety of settings.Comment: Accepted at the International Conference on Machine Learning (ICML) 202