There are versions of environment dynamics which may not be too onerous to implement.
For example, consider a GP in-hours service. A simple rules-based model could capture:
- Low capacity / long wait times, between the hours of 8am-10am and 3pm-6pm, Mon-Fri
- High capacity / short wait times, between the hours of 10am-3pm, Mon-Fri
- And no capacity otherwise (including bank holidays).
Each Environment has capacity and wait_time placeholder attributes which could be used to store such information, and this could
then be used by the interaction function to decide on whether the patient needs to wait an amount of wait_time before creating the
first record entry. Also, if the interaction layer has access to other environments, it could use this information to decide on the time of next interaction or to schedule an appointment.
More complex versions where more patients or other external events are taken into consideration could be employed, but this would require far more engineering.
There are versions of environment dynamics which may not be too onerous to implement.
For example, consider a GP in-hours service. A simple rules-based model could capture:
Each Environment has capacity and wait_time placeholder attributes which could be used to store such information, and this could
then be used by the interaction function to decide on whether the patient needs to wait an amount of wait_time before creating the
first record entry. Also, if the interaction layer has access to other environments, it could use this information to decide on the time of next interaction or to schedule an appointment.
More complex versions where more patients or other external events are taken into consideration could be employed, but this would require far more engineering.