From known to unknown unknowns through pattern-oriented modelling: Driving research towards the Medawar zone

Ming Wang, Hsiao-Hsuan Wang, Tomasz E. Koralewski, William E. Grant, Neil White, Jim Hanan and Volker Grimm

Ecological Modelling, 2024, vol. 497, issue C

Abstract: The metaphor of the Medawar zone describes the relationship between the difficulty of a scientific problem and the potential payoff of solving it. This zone represents the realm where questions offer high benefits relative to the effort required to address them. By harnessing the power of mechanistic modelling, scientists can navigate towards this zone, moving beyond known unknowns to discover unknown unknowns. This requires models to be realistic and reliable. Model usefulness, impact, and predictive power can be enhanced by achieving intermediate model complexity, where the trade-off between the realism and tractability of a model is optimised. To achieve these goals, we use the pattern-oriented modelling strategy (POM) to direct research into the Medawar zone by steering model structure towards intermediate complexity. We illustrate this strategy with a detailed conceptual process. Using example models from agri-ecological systems, we demonstrate how intermediate complexity can be attained through POM, and how pattern-oriented models of intermediate complexity that reproduce multiple patterns can uncover both known unknowns and unknown unknowns, which ultimately advances our understanding of complex systems and facilitates groundbreaking discoveries. In addition, we discuss the multidimensionality of the Medawar zone in the context of modelling philosophy and highlight the challenges and imperatives for achieving coherence in the modelling discipline. We emphasize the need for collaboration between end-users and modellers and the adoption of systematic modelling strategies such as POM.

Keywords: Computational modelling; Modelling strategy; Modelling practice; Computer simulation; Model complexity; Black Swan event (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:497:y:2024:i:c:s0304380024002412

DOI: 10.1016/j.ecolmodel.2024.110853

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