Earlier this month I, along with George Perry, finished a review on multi-model inference for the journal Geography Compass. Geography Compass publishes state-of-the-art reviews aimed at students, researchers and non-specialist scholars. The manuscript is currently under review so we’ll have to see what the reviewers think before the paper is published. Once it’s available I’ll re-post here. In the meantime, here’s the abstract to whet your appetite;
Multi-model inference (MMI) aims to produce scientific knowledge by simultaneously comparing the evidence data provide for multiple hypotheses, each represented as a model. Stemming from the method of ‘multiple working hypotheses’, MMI techniques have been advocated as an alternative to null hypothesis significance testing. These techniques will likely be particularly useful in research fields such as biogeography where formal experimentation is difficult and data are often influenced by uncontrolled factors. In this paper we review two complementary MMI techniques – model selection and model averaging – and highlight examples of their use in the biogeography literature. Both techniques can be implemented in a Bayesian framework and we outline the debate about different interpretations of probability. We summarise recommendations for avoiding philosophical and methodological pitfalls, and suggest circumstances in which each technique will likely be most useful in. We finish by advocating a pragmatic approach to MMI, one that emphasises the ‘thoughtful, science-based, a priori’ modelling others have argued is vital to ensure valid scientific inference.