Computational modelling for decision-making: where, why, what, who and how

Abstract

In order to deal with an increasingly complex world, we need ever more sophisticated computational models that can help us make decisions wisely and understand the potential consequences of choices. But creating a model requires far more than just raw data and technical skills: it requires a close collaboration between model commissioners, developers, users and reviewers. Good modelling requires its users and commissioners to understand more about the whole process, including the different kinds of purpose a model can have and the different technical bases. This paper offers a guide to the process of commissioning, developing and deploying models across a wide range of domains from public policy to science and engineering. It provides two checklists to help potential modellers, commissioners and users ensure they have considered the most significant factors that will determine success. We conclude there is a need to reinforce modelling as a discipline, so that misconstruction is less likely; to increase understanding of modelling in all domains, so that the misuse of models is reduced; and to bring commissioners closer to modelling, so that the results are more useful.

General practice work and workforce: Interdependencies between demand, supply and quality

“…Modelling results in systemwide evaluations of 'political, economic and/or service model dogma'. 24 To fulfil its remit of meeting each person's care needs in their community, general practice ultimately must have the right workforce numbers with a distribution to match a communities' needs, backed by the right kind of support services and financial resourcing. Only then will the profession be able to exercise its capabilities to maintain population health in the most effective, efficient and equitable way.…”

Approaching complexity—Start with awareness

“…System dynamics modelling involves two steps; firstly, model building, which establishes the relationship between various agents in a system and secondly, model testing, which explores the dynamic interactions amongst agents over time . System dynamics model building is particularly helpful if it involves everyone concerned with the problem—it facilitates learning as it provides an opportunity to contemplate its inherent multiple dimensions and interdependencies …”

Making Sense of the World: Framing Models for Trustworthy Sensor-Driven Systems

“…Models are used extensively across science and engineering, providing insight into the behaviours of what already exists, predicting future behaviours of what might exist, and exploring the implications of the specifications and designs of what we want to exist and how and if they meet system requirements [Calder et al, 2018]. We do not repeat the arguments in support of modelling here, but note that recent research in pervasive and cyber-physical systems that tackles modelling, implementation, and deployment of artificial systems integrated within real-world environments, e.g.…”