Epistemology of Modeling and Simulation: How can we gain Knowledge from Simulations?

TL;DR

This paper explores how modeling and simulation contribute to knowledge acquisition by examining their epistemological foundations, mathematical underpinnings, and implications for validation, verification, and federated systems.

Contribution

It provides a philosophical and mathematical framework for understanding how simulations serve as hypotheses for gaining knowledge and addresses challenges in federation development.

Findings

Simulations are viewed as computable hypotheses for knowledge gain.

Validation is interpreted as hypothesis testing and theory building.

Mathematical frameworks help address contradictions in federated simulations.

Abstract

Epistemology is the branch of philosophy that deals with gaining knowledge. It is closely related to ontology. The branch that deals with questions like "What is real?" and "What do we know?" as it provides these components. When using modeling and simulation, we usually imply that we are doing so to either apply knowledge, in particular when we are using them for training and teaching, or that we want to gain new knowledge, for example when doing analysis or conducting virtual experiments. This paper looks at the history of science to give a context to better cope with the question, how we can gain knowledge from simulation. It addresses aspects of computability and the general underlying mathematics, and applies the findings to validation and verification and development of federations. As simulations are understood as computable executable hypotheses, validation can be understood as hypothesis testing and theory building. The mathematical framework allows furthermore addressing some challenges when developing federations and the potential introduction of contradictions when composing different theories, as they are represented by the federated simulation systems.