Learning from others: Inductive reasoning based on human-generated data

我们每天看到的大多数数据，从政治到八卦，都来自其他人。对这样的数据做出推断是困难的，因为提供这些数据的人可能在他们所知的知识中存在偏见或局限性，而我们不知道这些偏见或局限性，必须弄清楚。这个项目使用了一系列与社会推理的标准计算模型捆绑在一起的实验，来探索人们如何解决这个问题。这项工作有可能对理解如何理解和共享信息产生重大影响，特别是当它涉及人们缺乏第一手知识的主题时，比如气候变化。计算模型还可用于开发我们的信息经济所依赖的专家系统。

Most of the data we see every day, from politics to gossip, comes from other people. Making inferences about such data is difficult because the people who provided it may have biases or limitations in their knowledge that we do not know about and must figure out. This project uses a series of experiments tied to normative computational models of social reasoning to explore how people solve this problem. This work has the potential to make a major impact in understanding how information is understood and shared, especially when it is about topics that people lack firsthand knowledge about, like climate change. The computational models also have applications to the development of expert systems upon which our information economy relies.