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

向他人学习：基于人类生成的数据的归纳推理

• 批准号: DP150103280
• 负责人: Prof Andrew Perfors
• 金额: $ 20.86万
• 依托单位: The University of Melbourne
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2015
• 资助国家: 澳大利亚
• 起止时间: 2015-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP150103280
• 关键词: Learning; others; Inductive; reasoning; based

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.

我们每天看到的大部分数据，从政治到八卦，都来自其他人。对这些数据进行推断是困难的，因为提供这些数据的人可能在他们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。该项目使用一系列与社会推理的规范计算模型相关的实验来探索人们如何解决这个问题。这项工作有可能在理解信息如何被理解和共享方面产生重大影响，特别是当它涉及人们缺乏第一手知识的话题时，如气候变化。计算模型也可以应用于我们的信息经济所依赖的专家系统的开发。