New Methods for Molding Models to Specific Cases to Enhance Policy Predictions

该项目旨在通过关注如何改进用于评估社会政策在当前环境中实施时是否会实现预期结果的模型，以及在该环境中实施的方式，来提高政策的有效性。该项目将提供一种系统的、有理论基础的方法，以决定收集何种证据是好的，并决定如何将证据放在一起，以确定它表明成功的机会。政策结果总是不确定的，而且往往是非常不确定的。这个项目有希望通过提供手头案例的模型方法来大大改善个体案例的预测结果。本研究的结果将有可能提高政策的有效性，并提出新的科学研究，以支持政策预测，它们将在许多政策领域传播。技术摘要本项目是调查各种证据，包括地方和科学证据，可以用来建立足够的模型，以做出合理的，但不确定的政策预测。该方法主要是分析性的，它建立在历史，哲学和科学社会学的证据，客观性和因果建模的研究基础上。它还建立在PI最近的工作证明单一因果索赔和因果机制。该项目涉及深入研究自然科学和社会科学，工程，以及复杂系统中成功的单例预测和事后评估的规律。它将开发证据的类别，并将改进和测试拟议的模型结构，以了解它是否适合成功的案例。这个项目应该有助于对奇异因果关系的哲学理解，这是目前哲学中的一个大话题。它应该在原则上和实践中促进对因果建模的理解，特别是在社会科学中。

General Audience Summary This project aims to enhance policy effectiveness by focusing on ways to improve models that are used to estimate whether a social policy will achieve its intended outcomes when implemented in the setting at hand, in the way it would be implemented in that setting. The project will provide a systematic, theoretically grounded approach for deciding what kinds of evidence are good to collect, and for deciding how to put the evidence together to ascertain what it shows about the chances of success. Policy outcomes will always be uncertain, often greatly so. This project has promise to substantially improve predicting outcomes in individual cases by providing methods for molding models to the cases at hand. The results of this research will have potential to improve policy effectiveness and to suggest new kinds of scientific studies to support policy prediction; they will be disseminated across a number of policy domains.Technical Summary This project is an investigation of the kinds of evidence, both local and scientific, that can be used to build full enough models to make reasonable, albeit uncertain, policy predictions. The methodology is primarily analytic, and it builds on studies in the history, philosophy, and sociology of science on evidence, objectivity, and causal modeling. It also builds on the PI's recent work on evidencing singular causal claims and on causal mechanisms. The project involves in-depth study of cases in the natural and the social sciences, engineering, and the law of successful single-case prediction and post-hoc evaluation in complicated systems. It will develop categories of evidence, and it will refine and test the proposed model structure to see how well it fits successful cases. The project should contribute to the philosophical understanding of singular causation, currently a big topic in philosophy. It should advance understanding of causal modeling, in principle and in practice, especially in the social sciences.