Simulated Data in Empirical Science

Abstract

This paper provides the first systematic epistemological account of simulated data in empirical science. We focus on the epistemic issues modelers face when they generate simulated data to solve problems with empirical datasets, research tools, or experiments. We argue that for simulated data to count as epistemically reliable, a simulation model does not have to mimic its target. Instead, some models take empirical data as a target, and simulated data may successfully mimic such a target even if the model does not. We show how to distinguish between simulated and empirical data, and we also offer a definition of simulation that can accommodate Monte Carlo models. We shed light on the epistemology of simulated data by providing a taxonomy of four different mimicking relations that differ concerning the nature of the relation or relata. We illustrate mimicking relations with examples from different sciences. Our main claim is that the epistemic evaluation of simulated data should start with recognizing the diversity of mimicking relations rather than presuming that only one relation existed.

Popperian ecology is a delusion

“…Since Zhang (2020) has already discussed other virtues and vices of null models that are not related to falsificationism, I shall not repeat these here. There is, however, one topic that has gone undiscussed in this context, namely, what is the epistemic or evidential relevance of simulated data null models produce (see Lehtinen & Raerinne, 2023 for a general discussion on simulated data in empirical science).…”