Scientific Discovery and Macroeconomic Outcomes

该奖项资助了一项研究，该研究开发了一个新的建模框架，将新想法的发现(由公司、消费者和其他经济决策者)纳入到现代经济学中广泛使用的一类模型中。这些模型假设个体参与者使用贝叶斯推理来随着时间的推移调整他们的预期。在这种学习模式中，随着时间的推移，代理人观察世界，将其与他们可能的解释清单进行比较，排除那些无法解释他们所看到的东西的可能性，同时也调整他们对剩余可能性的信念。这里没有发现一开始完全未知的全新可能性的想法。PI将推广贝叶斯方法，以允许以逻辑一致的方式进行这种发现。他将确定可以在拟议的新框架中分析的动态模型的类别。该项目对宏观经济理论具有变革潜力，因为其结果可能产生一种有用的新方法，用于思考新的科学技术发现如何影响宏观经济行为。更广泛的影响包括提高我们考虑科学发现对经济结果的作用的能力。

This award funds research that develops a new modeling framework for incorporating the discovery of new ideas (by firms, consumers, and other economic decision makers) into a class of models that are widely used in modern economics.These models assume that individual actors use Bayesian inference to adjust their expectations over time. In this model of learning, over time the agents observe the world, compare that to their list of possible explanations, and eliminate possibilities that do not explain what they see while also adjusting their beliefs about the remaining possibilities. There is no idea here of discovering entirely new possibilities that were completely unknown at the start. The PI will generalize the Bayesian method to allow for this kind of discovery in a logically consistant way. He will determine the class of dynamic models that can be analyzed in the proposed new framework.The project has transformative potential for macroeconomic theory, because the results may yield a useful new method for thinking about how new scientific and technological discoveries affect macroeconomic behavior.Broader impacts include the potential to improve our ability to consider the role of scientific discovery on economic outcomes.