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Currently, the predicted effects of a manager's actions are set to values that, heuristically, appear to work in the genetic algorithm. This is adjusted with the manager_sense parameter, which has a default of 0.1, such that the manager assumes that if they set costs to increase culling by 100 percent, it will actually only increase by 10 percent (as not all users are going to necessarily cull if given the opportunity). Like real-world management, this is heuristic and results in uncertainty, but future versions of GMSE could dynamically modify this value during the course of the simulation based on real knowledge of how policy changes have affected user actions in previous time steps.
The text was updated successfully, but these errors were encountered:
This is perhaps best done through a function of some sort update_manage_impact, which could regress the change in user actions against the cost set by the manager. This could update the relevant parameters vector elements so that the manager learns how to improve management.
Currently, the predicted effects of a manager's actions are set to values that, heuristically, appear to work in the genetic algorithm. This is adjusted with the
manager_sense
parameter, which has a default of 0.1, such that the manager assumes that if they set costs to increase culling by 100 percent, it will actually only increase by 10 percent (as not all users are going to necessarily cull if given the opportunity). Like real-world management, this is heuristic and results in uncertainty, but future versions of GMSE could dynamically modify this value during the course of the simulation based on real knowledge of how policy changes have affected user actions in previous time steps.The text was updated successfully, but these errors were encountered: