A formal model for analyzing manager’s performance during stress

Managers who are exposed to stress have the risk of taking insufficient decisions, which will affect their performance levels. The affect could be either positive or negative, depending on the individual’s perception on stress. Many inadequate conventional studies have been conducted for analyzing the complicated relationship of stress and performance. Hence this study introduces a formal model supports managers’ performance during stress. This model can be encapsulated within an intelligent agent or robots that can be used to support managers. The methodology was used to explore human cognitive processes during stress consisted of four phases: identification of local and non-local properties, conceptualization of the model of these properties, formalization, and evaluation. Deferential equations have been used in formalizing the properties. The developed model has been simulated by applying it to different scenarios. Mathematical analysis has been used for the evaluation of the model. Results showed that the formal model was able to show the effects of different levels of stress on managers’ performance

Explaining Predictions from Tree-based Boosting Ensembles

Understanding how "black-box" models arrive at their predictions has sparked significant interest from both within and outside the AI community. Our work focuses on doing this by generating local explanations about individual predictions for tree-based ensembles, specifically Gradient Boosting Decision Trees (GBDTs). Given a correctly predicted instance in the training set, we wish to generate a counterfactual explanation for this instance, that is, the minimal perturbation of this instance such that the prediction flips to the opposite class. Most existing methods for counterfactual explanations are (1) model-agnostic, so they do not take into account the structure of the original model, and/or (2) involve building a surrogate model on top of the original model, which is not guaranteed to represent the original model accurately. There exists a method specifically for random forests; we wish to extend this method for GBDTs. This involves accounting for (1) the sequential dependency between trees and (2) training on the negative gradients instead of the original labels

Un Modèle de prédiction de la performance des processus basé sur les réseaux Bayésiens

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