presimulation

Presimulation refers to the set of activities conducted before a simulation run to prepare the model, data, and participants, with the aim of ensuring that the simulation outputs are meaningful, reliable, and reproducible. It serves to verify the problem framing, ensure data quality, tune parameters, and align the experimental design with the study objectives.

Typical activities include problem framing and objective specification; model specification and implementation checks; data collection, preprocessing, and calibration; parameter estimation and planning of sensitivity analyses; experimental design, randomization, and replication plans; pre-simulation validation through code reviews, verification runs, and pilot simulations; and training or briefing of participants to ensure consistent scenario understanding.

Presimulation sits within the broader simulation life cycle, which also includes execution, post-processing, and interpretation. Its emphasis on verification, validation, and readiness helps reduce the risk of corrupted results and uninformative outcomes.

Applications span engineering, computer science, environmental and traffic modeling, pharmacometrics, healthcare simulations, and education and training. In each field, presimulation aims to improve trustworthiness by ensuring data quality, model fidelity, and appropriate performance metrics. Common challenges include data gaps, parameter identifiability, computational cost, and the need for transparent documentation. Because presimulation is preparatory, its outcomes influence study scope and interpretation of results, and it is often documented in preregistration, protocols, or standard operating procedures.