Summary of The Delusional Hedge Algorithm As a Model Of Human Learning From Diverse Opinions, by Yun-shiuan Chuang et al.
The Delusional Hedge Algorithm as a Model of Human Learning from Diverse Opinions
by Yun-Shiuan Chuang, Jerry Zhu, Timothy T. Rogers
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper investigates how humans learn which opinions to trust when they don’t have direct experience with an event or its outcome. The study builds upon the hedge algorithm, a classic method for learning from diverse information sources, and extends it to include semi-supervised learning. The authors examine human judgments in two experiments, comparing predictions from the standard hedge, the delusional hedge, and a heuristic baseline model. Results show that humans effectively incorporate both labeled and unlabeled information, aligning with the delusional hedge algorithm. This suggests that people not only evaluate the accuracy of information sources but also their consistency with other reliable sources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how we learn from what others tell us without knowing the truth ourselves. It’s like trying to figure out who’s telling the truth when there are many different opinions. The researchers took a well-known method for learning from different sources and made it better by adding a new way of learning that includes both labeled (with correct answers) and unlabeled information. They tested this on two groups of people and found that they used both kinds of information to make good judgments, just like the computer algorithm did. |
Keywords
» Artificial intelligence » Semi supervised
