Abstract

Modern scientific cosmology pushes the boundaries of knowledge and the knowable. This is prompting questions on the nature of scientific knowledge, and the emergence of the new field “Philosophy of Cosmology.” One central issue is what defines a “good” model. I discuss how “good” models are conventionally chosen, and how those methods operate in data-sparse situations: enabling the implicit introduction of value judgments, which can determine inference and lead to inferential polarization, e.g., on the question of ultimate explanation. Additional dimensions for comparing models are needed. A three-legged comparison is proposed: evidence, elegance and beneficence. This explicitly considers the categories of criteria that are always at least implicitly used. A tentative path to an implementation of the proposed model comparison framework is presented. This extends the Bayesian statistical framework. Model comparison methodology is fertile ground for dialogue between the sciences and the humanities. The proposed framework might facilitate such a dialogue.