Quantifying Human Priors over Social and Navigation Networks

Gecia Bravo-Hermsdorff
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:3063-3105, 2023.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v202-bravo-hermsdorff23a, title = {Quantifying Human Priors over Social and Navigation Networks}, author = {Bravo-Hermsdorff, Gecia}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, pages = {3063--3105}, year = {2023}, editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan}, volume = {202}, series = {Proceedings of Machine Learning Research}, month = {23--29 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v202/bravo-hermsdorff23a/bravo-hermsdorff23a.pdf}, url = {https://proceedings.mlr.press/v202/bravo-hermsdorff23a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Quantifying Human Priors over Social and Navigation Networks %A Gecia Bravo-Hermsdorff %B Proceedings of the 40th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Andreas Krause %E Emma Brunskill %E Kyunghyun Cho %E Barbara Engelhardt %E Sivan Sabato %E Jonathan Scarlett %F pmlr-v202-bravo-hermsdorff23a %I PMLR %P 3063--3105 %U https://proceedings.mlr.press/v202/bravo-hermsdorff23a.html %V 202 %X 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.
APA
Bravo-Hermsdorff, G.. (2023). Quantifying Human Priors over Social and Navigation Networks. Proceedings of the 40th International Conference on Machine Learning, in Proceedings of Machine Learning Research 202:3063-3105 Available from https://proceedings.mlr.press/v202/bravo-hermsdorff23a.html.

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