Summary of Quantifying Human Priors Over Social and Navigation Networks, by Gecia Bravo-hermsdorff
Quantifying Human Priors over Social and Navigation Networks
by Gecia Bravo-Hermsdorff
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI); Physics and Society (physics.soc-ph); Neurons and Cognition (q-bio.NC); Methodology (stat.ME)
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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 A machine learning approach is proposed to quantify human priors over relational data, leveraging the combinatorial structure of graphs. The method is applied to two domains: social interaction and spatial navigation. Experimental results show consistent features, such as sparsity as a function of graph size, and domain-specific features, like triadic closure in social interactions. The work demonstrates how nonclassical statistical analysis can efficiently model latent biases in behavioral experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how we think about relationships between people or places. It uses math to figure out what our brains are doing when we make decisions about who to talk to or how to get somewhere. The researchers looked at two areas where humans have been doing this for a long time: making friends and navigating the world. They found some patterns that happen often, like being more likely to connect with someone if they already know someone in common. Other patterns are specific to each area. Overall, the study shows how we can use math to understand what’s going on when we make decisions. |
Keywords
* Artificial intelligence * Machine learning
