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First, they are and must be a simplified representation of the modelled system. To be useful, models need to provide a cognitively-mediated environment to explain the systemic behaviour. If a model keeps growing in complexity, it will be difficult to understand and use. Yet, in many applications, large models are inevitable to support system understanding, and decision making at the appropriate and acceptable level of detail This has been long recognized as the 'modelling paradox' (Bonini, 1963). Second, models are limited to the collective cognitive complexity of the "mental models" of those involved in model development. Our mental models (another type of models) are a flawed, incomplete, and sometimes inconsistent, representation especially of dynamic and complex systems (Rouse and Morris, 1986). These challenges are escalated on using models in deep uncertainty situations where stakeholders do not know or cannot agree on the system structure, future scenarios, and model parameters (Kwakkel and Pruyt, 2013).
In an increasingly dynamic and complex world, we therefore need to develop and use models in novel and cost-effective ways that improve our decision-making capacity. This raises the following questions:
Charles P. Bonini (1963) Simulation of information and decision systems in the firm, Englewood Cliffs, N. J.: Prentice-Hall
Kwakkel, J. H., & Pruyt, E. (2013). Exploratory Modeling and Analysis, an approach for model-based foresight under deep uncertainty.Technological Forecasting and Social Change, 80(3), 419-431. Rouse, W. B., & Morris, N. M. (1986). On looking into the black box: Prospects and limits in the search for mental models. Psychological bulletin, 100(3), 349.