Learning Something Right from Models That Are Wrong: Epistemology of Simulation

Abstract

Epistemology is the branch of philosophy that deals with gaining knowledge. It is closely related to ontology, the branch that deals with questions such as ``What is real?'' and ``What do we know?'' When using modeling and simulation (M&S), we usually do so to either apply knowledge, in particular when we are using them for training and teaching, or to gain knowledge, for example, when doing analysis or conducting virtual experiments. But none of our models represents reality as it is. They are only valid within their limitations, which leads to the famous quote of Box that ``all models are wrong.'' The question is therefore: how can we learn something from these models? What are the epistemological foundations for us simulationists? To guide the reader on this path, we will start with an introduction to the philosophical fields of ontology and epistemology, leading to a short history of science, both from a simulationist view. These views shape our discussion on the use of models and resulting limits and constraints. The outcome of these analyses, as will be demonstrated, will lead to a grand challenge for the simulation community to evolve within the community of scientists by including epistemological perspectives in curricula for simulationists as a pillar of our profession.