This paper aims to develop an account of the pursuitworthiness of models based on a view of models as epistemic tools. This paper is motivated by the historical question of why, in the 1960s, when many scientists hardly found QSAR models attractive, some pharmaceutical scientists pursued Quantitative Structure–Activity Relationship (QSAR) models despite the lack of potential for theoretical development or empirical success. This paper addresses this question by focusing on how models perform their heuristic functions as epistemic tools rather than as potential theories. I argue that models perform their heuristic function by “constructing” phenomena from data in the sense that they allow the model users who interact with the medium of the models to recognise the phenomena as such. The constructed phenomena assist model users in identifying which conditional hypotheses that are focused on low-level regularities concerning entities such as chemical compounds are more “testworthy,” a concept that links the costs associated with hypothesis testing with the fertility of the hypothesis.

本文旨在基于将模型视为认知工具的观点，对模型的追求价值进行说明。本文的动机是历史问题，即为什么在 1960 年代，当许多科学家几乎没有发现 QSAR 模型有吸引力时，一些药物科学家在缺乏理论发展或经验成功的潜力的情况下追求定量构效关系 (QSAR) 模型。本文通过关注模型如何作为认知工具而不是潜在理论来执行其启发式功能来解决这个问题。我认为模型通过从数据中“构建”现象来执行它们的启发式功能，因为它们允许与模型媒介交互的模型用户识别现象本身. 构建的现象帮助模型用户确定哪些条件假设侧重于与化合物等实体有关的低水平规律性，这些条件假设更“值得测试”，这一概念将与假设检验相关的成本与假设的有效性联系起来。