Summary and Wrap

Complex systems contain linear and nonlinear interacting components.

Self-Organisation: A system has self-organising behaviour when emergent properties are being examined over time.

Emergence is what may be observed at one level can be fundamentally different at a sub or microscopic level.

ABM’s have been used in a wide variety of disciplines, yet there are limited applications in Pharmacy.

Steps: Hypothesis Specification -> Model Development -> Model Validation

Design principle: Start simple and build to complexity.

NetLogo is simple to use, well supported, and a great starting point for agent-based modelling.

NetLogo simulations need to be ‘setup’, then they ‘go’

Ticks can be an arbitrary unit of time.

Plots, graphs, and numbers can be used to output the results of your model.

Can run multiple simulations using the NetLogo BehaviourSpace feature.

Multi-agent behaviour in pharmacy is a complex system which may be modelled using ABM.

How multiple pharmacies interact within a market can be modelled as a complex system.

Adjustable inputs are geographic location and pricing strategy.

Dask builds on numpy and pandas APIs but operates in a parallel manner

Computations are by default lazy and must be triggered - this reduces unneccessary computation time