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.

本文提供了实证科学中模拟数据的第一个系统认识论解释。我们关注建模者在生成模拟数据以解决经验数据集、研究工具或实验问题时所面临的认知问题。我们认为，要使模拟数据在认知上可靠，模拟模型不必模仿其目标。相反，一些模型将经验数据作为目标，即使模型没有成功，模拟数据也可能成功模拟这样的目标。我们展示了如何区分模拟数据和经验数据，并且还提供了可以适应蒙特卡罗模型的模拟定义。我们通过提供四种不同模仿关系的分类法来阐明模拟数据的认识论，这些模仿关系在关系或关联的性质方面有所不同。我们用不同科学的例子来说明模仿关系。我们的主要主张是，模拟数据的认知评估应该从认识模仿关系的多样性开始，而不是假设只存在一种关系。