Bayesian Communication in the Social Sciences

本项目将制定和实施程序，以加强报告结果的研究人员与其受众（即其他研究人员、决策者和学生）之间的沟通。这些用户友好的程序将使客户能够改变调查人员的先验，检查替代后验矩，并结合模型。软件将以标准文件结构开发，以方便软件与用户软件之间的控制。该软件将使客户能够评估重新加权输出的准确性，绘制相应的后验密度，并在模型之间进行平均，以获得模型上无条件的后验矩。预计该项目将在社会科学领域产生两个重大影响。首先，它将使所有定量社会科学家都能接触到贝叶斯分析的许多固有优势，因为它消除了以前所需要的不合理的计算需求。其次，它将使客户能够通过修改他们的先验、检查替代后验矩和更新样本来重做调查人员的分析，而这比阅读一份典型的研究报告所需的时间要短。这些影响加在一起，将极大地鼓励对假设方面的政策建议的稳健性进行分析，并加速将新的研究应用于政策问题。

This project will develop and implement procedures that will enhance communication between investigators who report results and their audience (i.e., other investigators, decision makers, and students). These user-friendly procedures will enable clients to alter investigators' priors, examine alternative posterior moments, and combine models. Software will be developed with standard file structures to facilitate control between it and user software. This software will enable clients to evaluate the accuracy of reweighted output, plot corresponding posterior densities, and average across models to obtain posterior moments unconditional on models. It is expected that this project will have two significant impacts in the social sciences. First, it will place within the reach of all quantitative social scientists many of the inherent advantages of Bayesian analysis by removing the unreasonable computational demands that have previously been required. Second, it will enable clients to rework investigators' analyses by modifying their priors, examining alternative posterior moments, and updating samples, in less time than is required to read a typical research report. Together these impacts should greatly encourage analysis of the robustness of policy recommendations with respect to assumptions and accelerate the application of new research to policy problems.