Why a Multi-Representational Approach is Necessary to Facilitate Prediction, Understanding & Scientific Discovery

Abstract: This talk examines the question “How can we use Models to facilitate Scientific Discovery?”, as opposed to the manner in which models are normally used, which is to generate “Predictions” or to aid (we hope) in the development of “Understanding”. As a starting point, we recognize that the key to the analysis of any data, or development of any model, is the selection of an appropriate “representational system”. The problem, however, is that the representation we chose then completely determine the “questions we can ask”, the nature of “analyses and inferences we can perform”, and the “answers we can obtain”. Consequently, any model we develop using that representational approach may be highly suitable for learning certain things about a system, but completely unsuitable for learning other (possibly equally important) things.

To explore how different representational frameworks can affect what we learn from data, we present a case study in which three complementary models approaches (one physics/process-based and two based in machine-learning methods) are simultaneously used to develop an improved understanding of how catchment-scale hydrological processes vary across the diverse hydro-geo-climatology of Chile.  Based (in part) on these results, we argue that if our goal truly is “Scientific Discovery”, then we would be well served to adopt a “general multi-representational framework” when analyzing data, so that the models we build have the possibility of converging towards accurate and comprehensive representations of the underlying data generating process.  Our view is that such an approach is key to the use of Models in support of improved Prediction, Understanding and Discovery in the Earth & Environmental Sciences.