Statistical Sheaf Modeling Methods for Measuring Qualia Structures
Abstract
Mathematical sheaves are an abstraction that captures the notion that consistency between local properties can lead to a global picture. There are a number of (mathematical) phenomena that sheaves exhibit, which seem tantalizingly close to what one might hope a theory of qualia would explain. Recently, the theory and abstraction of sheaves has been transformed into robust computational tools for understanding complex systems. These tools are now ripe to help qualia researchers forge robust and testable models. Intuitively, as conscious entities, rather than experiencing ``everything all at once,'' our experience is coherent to varying degrees. Sheaves can model this rather nicely. This chapter presents the practical and theoretical considerations of sheaves in data modeling, with a view towards computational models that can support statistical hypothesis testing.
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BibTeX
@misc{dietz2026statistical,
title = {{Statistical Sheaf Modeling Methods for Measuring Qualia Structures}},
author = {Donna A. Dietz and Michael Robinson},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.00361},
eprint = {2611.00361},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.00361},
}
RIS
TY - UNPB
TI - Statistical Sheaf Modeling Methods for Measuring Qualia Structures
AU - Donna A. Dietz
AU - Michael Robinson
PY - 2026
DA - 2026/11/01
PB - arXiv
DO - 10.48550/arxiv.2611.00361
UR - https://arxiv.org/abs/2611.00361
AB - Mathematical sheaves are an abstraction that captures the notion that consistency between local properties can lead to a global picture. There are a number of (mathematical) phenomena that sheaves exhibit, which seem tantalizingly close to what one might hope a theory of qualia would explain. Recently, the theory and abstraction of sheaves has been transformed into robust computational tools for understanding complex systems. These tools are now ripe to help qualia researchers forge robust and testable models. Intuitively, as conscious entities, rather than experiencing ``everything all at once,'' our experience is coherent to varying degrees. Sheaves can model this rather nicely. This chapter presents the practical and theoretical considerations of sheaves in data modeling, with a view towards computational models that can support statistical hypothesis testing.
ER -