A mechanistic-stochastic model for mapping known psychological factors to employment related stress is developed. The model is tested against independent data for validation. A unique data-based adaptive topological mapping algorithm is used for the prediction of individual stress levels in quantitative units. Organizational stress levels are predicted by synthesizing all or representative cohorts of the workplace population for ranked identification of action for the prevention, mitigation, and recovery using generic organizational responses. The work involves collaboration with qualified clinical psychologists and councilors. "INSTinct " dashboard provides a tool for human resource management with applications in workplace and learning environment management. The large and growing database enables capturing population characteristics such as age, developmental, socio-economic, political and cultural factors.
Finite nested and sequential probability spaces are determined to track scenario trajectories for event prediction within desired confidence intervals. Events are statistically characterized taking their inherent variability into account. The methodology depends on the computational efficiency and speed of using both very large collections of data as well as real time -or dynamic data. Self-training, statistical process specific algorithms discern the quality of data for deconvolution, classification, and characterization for use. Results feed dashboards for real time decision making.
Nearly all data are qualitative. Systems of measurement convert qualitative data into quantitative data. As the result, despite being determinant in nature, numerical data include unintended biases among them characteristic in the original qualitative data. The science of Design of Experiments attempt minimizing these dependencies amongst classes of data or group them so that surrogate parameters can collectively capture the dependencies to enable system modeling. Perception data leading to decision making in particular are the most complex in that, both the influx of information, i.e. data, and the perceptions of their interdependencies are widely individualistic. Probabilistic predictions of phenomena and events with associated confidence quantification are perhaps the most useful.
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The underlying mathematics and statistics for such process characterizations are quite complex and keeping them sufficiently generic for varying applications is challenging. This application addresses the decision maker's perception; the "acumen" factor, that a vast majority of business, political, and defense decisions are made based on. "ECHOthought " is an intelligent and objective desktop listening companion who remembers vocalized thoughts and reasoning and analyzes them to reveal its honest opinions without looking to please the listner.
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"INSTinct " takes a scientific and data-based approach to analyzing individual and organizational behavior to assess the current state and to predict future events and conditions in response to organizational action and inaction. "INSTinct " also suggests what needs attention.
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