# Quantifying Human Priors over Social and Navigation Networks

arXiv: https://arxiv.org/abs/2402.18651 · HTML: https://mlsanity.com/paper/2402.18651/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 81 references that are in the map.
- First posted on arXiv 28 Feb 2024.

## Builds on (most cited first, 13 of 13)

- [Gibbs Sampling with People](https://mlsanity.com/paper/2008.02595/) (2020)
- [Mixing time estimation in reversible Markov chains from a single sample path](https://mlsanity.com/paper/1708.07367/) (2017)
- [Probing BERT's priors with serial reproduction chains](https://mlsanity.com/paper/2202.12226/) (2022)
- [How humans learn and represent networks](https://mlsanity.com/paper/1909.07186/) (2019)
- [What is important about the No Free Lunch theorems?](https://mlsanity.com/paper/2007.10928/) (2020)
- [The Statistical Physics of Real-World Networks](https://mlsanity.com/paper/1810.05095/) (2018)
- [Representational efficiency outweighs action efficiency in human program induction](https://mlsanity.com/paper/1807.07134/) (2018)
- [Individual Differences in Learning Social and Non-Social Network Structures](https://mlsanity.com/paper/1709.02852/) (2017)
- [Exposure theory for learning complex networks with random walks](https://mlsanity.com/paper/2202.11262/) (2022)
- [Analyzing Diffusion as Serial Reproduction](https://mlsanity.com/paper/2209.14821/) (2022)
- [Self-Sustaining Iterated Learning](https://mlsanity.com/paper/1609.03960/) (2016)
- [Introducing Graph Cumulants: What is the Variance of Your Social Network?](https://mlsanity.com/paper/2002.03959/) (2020)
- [Quantifying Network Similarity using Graph Cumulants](https://mlsanity.com/paper/2107.11403/) (2021)

## Built on by (most cited first, 1 of 1)

- [Causal Models for Growing Networks](https://mlsanity.com/paper/2504.01012/) (2025)

## Abstract (arXiv)

Human knowledge is largely implicit and relational -- do we have a friend in common? can I walk from here to there? In this work, we leverage the combinatorial structure of graphs to quantify human priors over such relational data. Our experiments focus on two domains that have been continuously relevant over evolutionary timescales: social interaction and spatial navigation. We find that some features of the inferred priors are remarkably consistent, such as the tendency for sparsity as a function of graph size. Other features are domain-specific, such as the propensity for triadic closure in social interactions. More broadly, our work demonstrates how nonclassical statistical analysis of indirect behavioral experiments can be used to efficiently model latent biases in the data.