来る10月21日（土）に、U Penn哲学教授で近刊『科学とモデル』の著者でもあるMichael Weisbergを招いて以下のように講演をします。
またWeisbergはBiology & Philosophy誌のChief editorでもあり、同日は同誌を含め国際論文投稿にむけたワークショップを午前中から開催いたしますので、こちらも奮ってご参加下さい。
Speaker: Prof. Michael Weisberg (University of Pennsylvania)
Date: October 21st 2017
Venue: Large conference room in the basement, Faculty of Letters MainBuilding, Yoshida Campus, Kyoto University. [map]
Title: Confirmation Theory for Idealized Models
When a flu pandemic strikes, who should get vaccinated first? What’s our best strategy for minimizing the damage of global climate change? Why is Philadelphia racially segregated? Why do most sexually reproducing species have only two sexes, in roughly even proportions? These and many other scientific and practical problems are studied with highly idealized mathematical and computational models. When should we believe these models and follow the advice they suggest? Philosophy of science tells us that we should believe models when they are well-confirmed, but this simple answer isn’t very helpful here. Traditional confirmation theory explains how empirical evidence bears on the truth of hypotheses and theories, but the highly idealized models at the heart of the life and social sciences are known to be false from the outset. Moreover, classical ideas about confirmation have been developed for relatively simple hypotheses, while many contemporary models have thousands of variables.
Despite these challenges, it is possible to develop an account of model confirmation that can speak to the reliability of models and their results. I will sketch a theory that has two parts: First, theorists validate models, confirming hypotheses about model/target system relations. Second, they employ robustness analysis to investigate the stability of model results. Taken together, validation and robustness tell us when models are reliable and help us understand the appropriate domain of their application. Not only does this theory better align our accounts of scientific method with modern theoretical practice, it also helps us understand when to believe the results of models.