Simple modeling tool to generate what if scenarios representing rate of change in technology versus ability of workers to adapt to change.
The purpose of this tool is to model the impact of potentially disruptive technological changes affecting the labor market. Workers either adapt to and learn to use new technologies or are displaced.
- Time is required for workers to up-skill or re-skill in order to remain relevant in the labor market, i.e., to remain employable and ultimately to remain in the workforce.
- The magnitude and complexity of technological change influence how long workers require to up-skill or re-skill.
- The timespan over which technological change occurs can range from (1) long enough to reasonably expect virtually all workers to adapt, to (2) too short a time to reasonably expect any worker to adapt.
In the project directory, you can run:
python adaptation_vectors.py <p1> <p2>- p1 = time (in months) in which disruptive technology change occurs (max time available for workers to adapt)
- p2 = time (in months) needed for workers to adapt to technology change, i.e., to up-skill or re-skill
The tool distributes randomly generated points in a 3D field, divides points into five 20% tranches along the Y axis (percent of workforce), producing 5 tranches of workers, and draws a cross-cutting line (demarcating the top 20% of workers by default).
The position of the cross-cutting line relative to the array of points varies depending on input values representing time (in months) over which technological change occurs (time available for workers to adapt) (p1) versus time needed for workers to adapt to change (p2). Both time available and time needed for workers to adapt are equal to 36 months by default.
Different input values result in greater or lesser proportions of the workforce falling below the line. The resulting scenarios (visualized as 3D graphs) represent employability versus unemployability
- Python
- numpy
- matplotlib
A Jupyter notebook, adaptation_vectors.ipynb, s included and contains the above Python program.
Python 3 installed on your system. Jupyter Notebook installed (can be installed via pip: pip install notebook).
Clone or download the repository containing the Jupyter notebook file (adaptation_vectors.ipynb). Change directories to the directory where the notebook file is located then launch Jupyter Notebook
jupyter notebook <adaptation_vectors.ipynp>Once the notebook is open, run each cell by selecting it and pressing Shift + Enter. Alternatively, you can use the Run button from the toolbar. The notebook will provide fields for entering the total months and maximum months to adapt.
After executing all the cells, you will find a generated CSV file (adaptation_vectors.csv) containing the data and a PNG file (adaptation_vectors.png) containing the 3D graph in the same directory as the notebook.
Ron Herardian
Contributions, issues, and feature requests are welcome!
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