- Mind the assumptions: assess uncertainty and sensitivity as their role in predictions is substantially larger that originally asserted
- Mind the hubris: complexity can be the enemy of relevance; there is a trade-off between the usefulness of a model and the breadth it tries to capture; complexity is too often seen as an end in itself. Instead, the goal must be finding the optimum balance with error
- Mind the framing: match purpose and context; no one model can serve all purposes; modellers know that the choice of tools will influence, and could even determine, the outcome of the analysis, so the technique is never neutral; shared approaches to assessing quality need to be accompanied by a shared commitment to transparency. Examples of terms that promise uncontested precision include: ‘cost–benefit’, ‘expected utility’, ‘decision theory’, ‘life-cycle assessment’, ‘ecosystem services’, and ‘evidence-based policy’. Yet all presuppose a set of values about what matters — sustainability for some, productivity or profitability for others; the best way to keep models from hiding their assumptions, including political leanings, is a set of social norms. These should cover how to produce a model, assess its uncertainty and communicate the results. International guidelines for this have been drawn up for several disciplines. They demand that processes involve stakeholders, accommodate multiple views and promote transparency, replication and analysis of sensitivity and uncertainty. Whenever a model is used for a new application with fresh stakeholders, it must be validated and verified anew.
- Mind the consequences: quantification can backfire. Excessive regard for producing numbers can push a discipline away from being roughly right towards being precisely wrong; once a number takes centre-stage with a crisp narrative, other possible explanations and estimates can disappear from view. This might invite complacency, and the politicization of quantification, as other options are marginalized; opacity about uncertainty damages trust (…) Full explanations are crucial.
- Mind the unknowns: acknowledge ignorance; communicating what is not known is at least as important as communicating what is known; Experts should have the courage to respond that “there is no number-answer to your question.”
Mathematical models are a great way to explore questions. They are also a dangerous way to assert answers. Asking models for certainty or consensus is more a sign of the difficulties in making controversial decisions than it is a solution, and can invite ritualistic use of quantification. Models’ assumptions and limitations must be appraised openly and honestly. Process and ethics matter as much as intellectual prowess. It follows, in our view, that good modelling cannot be done by modellers alone. It is a social activity. The French movement of statactivistes has shown how numbers can be fought with numbers, such as in the quantification of poverty and inequalities (…) We are calling not for an end to quantification, nor for apolitical models, but for full and frank disclosure. Following these five points will help to preserve mathematical modelling as a valuable tool. Each contributes to the overarching goal of billboarding the strengths and limits of model outputs. Ignore the five, and model predictions become Trojan horses for unstated interests and values. Model responsibly.
Saltelli, A. et al., (2020). Five ways to ensure that models serve society: a manifesto, article available here