Weisberg, Michael
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Publication Modeling(2016-01-01) Weisberg, MichaelThis article focuses on the methodology of modeling and how it can be applied to philosophical questions. It looks at various traditional views of modeling and defends the idea that modeling is a form of surrogate reasoning involving two distinct steps: indirect representation of a target system using a model and analysis of that model. The article considers different accounts of model/target representational relations, defending an account of similarity. It concludes by presenting several examples of the use of models in philosophy, suggestions for philosophers new to modeling, and an assessment of the relationship between thought experiments and models.Publication Robustness and Idealization in Models of Cognitive Labor(2011-11-01) Muldoon, Ryan; Weisberg, MichaelScientific 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.