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Robustness and idealization in models of cognitive labor

机译：认知劳动模型的鲁棒性和理想化

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Scientific research is almost always conducted by communities of scientists of varying size and complexity. Such communities are effective, in part, because they divide their cognitive labor: not every scientist works on the same project. Philip Kitcher and Michael Strevens have pioneered efforts to understand this division of cognitive labor by proposing models of how scientists make decisions about which project to work on. For such models to be useful, they must be simple enough for us to understand their dynamics, but faithful enough to reality that we can use them to analyze real scientific communities. To satisfy the first requirement, we must employ idealizations to simplify the model. The second requirement demands that these idealizations not be so extreme that we lose the ability to describe real-world phenomena. This paper investigates the status of the assumptions that Kitcher and Strevens make in their models, by first inquiring whether they are reasonable representations of reality, and then by checking the models’ robustness against weakenings of these assumptions. To do this, we first argue against the reality of the assumptions, and then develop a series of agent-based simulations to systematically test their effects on model outcomes. We find that the models are not robust against weakenings of these idealizations. In fact we find that under certain conditions, this can lead to the model predicting outcomes that are qualitatively opposite of the original model outcomes.
机译：科学研究几乎总是由规模和复杂程度各异的科学家社区进行的。这样的社区之所以有效，部分是因为它们分散了他们的认知工作：并非每个科学家都从事同一项目。菲利普·基彻（Philip Kitcher）和迈克尔·史蒂文斯（Michael Strevens）通过提出关于科学家如何决定要开展的项目的决策模型的努力，开创了理解认知劳动分工的先河。为了使此类模型有用，它们必须足够简单，以使我们能够理解它们的动力学，但必须对现实足够忠实，以便我们可以使用它们来分析实际的科学共同体。为了满足第一个要求，我们必须采用理想化方法来简化模型。第二个要求要求这些理想化不能太极端，以至于我们失去了描述现实世界现象的能力。本文首先通过询问它们是否是现实的合理表示，然后通过检查模型对这些假设的弱化的稳健性，来研究Kitcher和Strevens在其模型中做出的假设的状况。为此，我们首先反对这些假设的真实性，然后开发一系列基于主体的模拟以系统地测试其对模型结果的影响。我们发现，这些模型对于这些理想化的弱化是不稳健的。实际上，我们发现在某些条件下，这可能导致模型预测的结果与原始模型的结果在质量上相反。

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