Learning from discrete-event simulation: exploring the high involvement hypothesis

Abstract

Discussion of learning from discrete-event simulation often takes the form of a hypothesis stating that involving clients in model building provides much of the learning necessary to aid their decisions. Whilst practitioners of simulation may intuitively agree with this hypothesis they are simultaneously motivated to reduce the model building effort through model reuse. As simulation projects are typically limited by time, model reuse offers an alternative learning route for clients as the time saved can be used to conduct more experimentation. We detail a laboratory experiment to test the high involvement hypothesis empirically, identify mechanisms that explain how involvement in model building or model reuse affect learning and explore the factors that inhibit learning from models. Measurement of learning focuses on the management of resource utilisation in a case study of a hospital emergency department and through the choice of scenarios during experimentation. Participants who reused a model benefitted from the increased experimentation time available when learning about resource utilisation. However, participants who were involved in model building simulated a greater variety of scenarios including more validation type scenarios early on. These results suggest that there may be a learning trade-off between model reuse and model building when simulation projects have a fixed budget of time. Further work evaluating client learning in practice should track the origin and choice of variables used in experimentation; studies should also record the methods modellers find most effective in communicating the impact of resource utilisation on queuing.

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ORCID for Thomas Monks: orcid.org/0000-0003-2631-4481

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