SE Simulate seeks to simulate the self-efficacy of given networks of populations, given initial parameters of the coaching effectiveness, number, and social network clustering. Of interest were the time effects of SE development both in individuals and the entire network as a whole, inspiring the outputs provided. Given these parameters, thus, the following graphical outputs are provided:

• Network depictions (20 time step intervals): Portrays individuals (red denoting those lacking coaches and blue for those with them) with their transparency further corresponding to their SE level
• Graph of the individual changes in exercise and SE levels
• Graph depicting the relation between SE and exercise level as a result of changing the following parameters;
  • Coaching effectiveness
  • Coach count
  • Time decay constants
  • Different network clustering algorithsm

The results of SE Simulate are outputted in the Results folder (originally blank) and are further sorted by the time results (graphical displays for steps of time) and sensitivity (for sensitivity analysis).

Note: The package uses Python 3, with the Numpy, NetworkX, and Matplotlib libraries.