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A field of simulation modelling using stocks and flows. These two entities form the building blocks, while feedback loops are a defining feature of an SD model. Identifying loops is a key goal of SD modelling, helping enterprises mitigate the effects of unwanted feedback loops and increase the dominance of favourable loops by identifying relevant policies.
To explain the behaviour over time of any organisation or complex system, a set of standard behaviour-over-time modular structures is used, including goal-seeking dynamics, rise and collapse, and exponential rise and oscillatory dynamics.
Any modelling cycle begins by stating the purpose of the model, a dynamic hypothesis, creating the model, testing and validation, and, if needed, revising the problem statements and hypothesis until stakeholders are satisfied and validation is achieved.
This modelling paradigm is suitable for modelling process flows through entities. An example is a queueing system such as a bank or a health clinic, where customers/patients arrive, join the queue, and return after being served/treated.
The key is to create appropriate statistical arrival models for customers/patients and service times at serving stations/doctors. A systems perspective is then appropriate to create additional processes using these principles.
Agent-based simulation is suitable for modelling individual agent behaviour by incorporating heterogeneity that exists across a population of agents. Agents then interact in an environment, producing emergent patterns through collective dynamics. As an example, each individual has an age, gender, proclivity for certain diseases, or a proclivity to use a certain product. In the case of infectious disease, individuals interact in environments such as homes, workplaces, schools, and communities, leading to the spread of the disease. Mitigation strategies can be designed based on where interactions occur most often, and vaccination can also be targeted to the age group that emerges as most at risk.