Should I Trust that Model?
How do those building and using models decide whether a model should be trusted? While my thinking has evolved through modelling to predict the impacts of land use on losses of nutrients to the environment – such models are central to land use policy development – this under-discussed question applies to any model.
In principle, model development is a straightforward series of steps:
Specification: what will be included in the model is determined conceptually and/or quantitatively by peers, experts and/or stakeholders and the underlying equations are decided Coding: the concepts and equations are translated into computer code and the code is tested using appropriate software development processes Parameterisation: here the values that go into the equations are determined by a variety of methods Testing: the model is compared against data using any of a wide range of metrics, the comparisons are examined and the fitness of the model for the intended purpose or scope is decided. Bennett and colleagues (2013) give an excellent position on the variety of statistical approaches that can be used for this purpose.
In reality, of course, these steps do not take place in an orderly progression, there are many loops backward, some of the parameterisation and testing occurs in parallel to the coding and the first step is often re-visited many times.
It is mostly assumed that assessment of ‘trust’ or ‘confidence’ in a particular model should be based on the metrics or statistics resulting from the comparison of the model outputs against experimental datasets. Sometimes, however, the scope of the testing data and whether the model has been published in a good journal are also taken to imply confidence in the model. These criteria largely refer to that last testing step and this focus is understandable. Of the steps above, testing is the one mostly readily documented against accepted standards with the results made available externally. However, even with a quantitative approach to testing, Bennett and colleagues note that the actual values of the statistics that are considered to be acceptable are a subjective decision.
While I agree with the approach and need for quantitative testing, the testing results themselves have very little to do with my confidence or trust in a model. My confidence will evolve over time as I become more familiar with the model. By the time I am prepared to make any statements about the specific reasons for my degree of trust, the reasons for that trust will largely have become tacit knowledge – and that makes it very difficult for me to explain to someone else why I have confidence (or not) in that model.
Here I have attempted to tease out the factors that influence my confidence in a model. I should note that my trust in the models I have been involved in developing, or that I use at an expert level, can fluctuate quite widely and wildly over time so, for me, the process of developing trust is not a linear process and is subject to continual revision.
Biography: Val Snow is a systems modeller at AgResearch in New Zealand and comes from a soil physics and agricultural science background. Her research focuses on the development and use of simulation models to support technological innovation in pastoral agricultural systems and assessment of the impacts of land use. Application areas include land use policy, future farming systems, greenhouse gas mitigation and climate change adaptation.
This blog post is one of a series resulting from the first meeting in March 2016 of the Core Modelling Practices Pursuit http://sesync.org/project/enhancing-socio-environmental-research-education/model-process-practices. This pursuit is part of the theme Building Resources for Complex, Action-Oriented Team Science http://www.sesync.org/theme-11 funded by the National Socio-Environmental Synthesis Center (SESYNC) http://www.sesync.org/ .