Modelling flows through
In Chapter 1 you learned to distinguish between models representing effects (or influences), logical stages and flows. You were also introduced to system models, which involve both influences and flows. In this chapter and the next you will learn about a number of approaches to modelling systems. Such models can play useful roles in analysing the possible outcomes of different policy options.
In the first part of this chapter you will learn how flows through systems can be represented using simple 'tree' models. These can be useful for questions about the routes that flows of patients may take as they move through the system, if 'outcomes' can be measured at a fixed time after intervention. But if outcome events are spread out over time and can occur more than once, another approach may be needed. In the second part of this chapter you will learn about Markov models.
Another important issue is whether the situation in some 'downstream' parts of a system affects flow rates 'upstream'. The nature and extent of any such feature of a system, called 'feedback', can determine how the system as a whole behaves, and in the third part of the chapter you will learn more about this. Finally, you will learn about the limitations of modelling flows as flows, rather than the sum of the experiences of individuals. In Chapter 11 you will learn about modelling the experiences of individuals, known as microsimulation.
By the end of this chapter, you will be better able to:
• use representations of flows, states, events and influences to describe the
performance of systems
• use tree structures to model flows through a system • explain the Markov property and Markov chains • explain the role of system dynamics and feedback in complex system
Feedback loop The causal loop formed when flow rates out of a process or state influence the
flow rates into it.