# The Physics of Preference: Unravelling Imprecision of Human Preferences through Magnetisation Dynamics

arXiv: https://arxiv.org/abs/2310.00267 · HTML: https://mlsanity.com/paper/2310.00267/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 99 references that are in the map.
- First posted on arXiv 30 Sep 2023.

## Builds on (most cited first, 7 of 7)

- [A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware](https://mlsanity.com/paper/2107.03992/) (2021)
- [Mathematical foundations of moral preferences](https://mlsanity.com/paper/2101.08193/) (2021)
- [Analogue and Physical Reservoir Computing Using Water Waves](https://mlsanity.com/paper/2306.09095/) (2023)
- [A perspective on physical reservoir computing with nanomagnetic devices](https://mlsanity.com/paper/2212.04851/) (2022)
- [A Planck Radiation and Quantization Scheme for Human Cognition and Language](https://mlsanity.com/paper/2201.03306/) (2022)
- [Human Perception as a Phenomenon of Quantization](https://mlsanity.com/paper/2208.03726/) (2022)
- [A maximum entropy model of bounded rational decision-making with prior beliefs and market feedback](https://mlsanity.com/paper/2102.09180/) (2021)

## Built on by (most cited first, 5 of 5)

- [Quantum-tunnelling deep neural network for optical illusion recognition](https://mlsanity.com/paper/2407.11013/) (2024)
- [Quantum-Cognitive Neural Networks: Assessing Confidence and Uncertainty with Human Decision-Making Simulations](https://mlsanity.com/paper/2412.08010/) (2024)
- [Constructive Alignment: Governing Preference Dynamics in Human-AI Interaction](https://mlsanity.com/paper/2607.00001/) (2026)
- [Transforming Traditional Neural Networks into Neuromorphic Quantum-Cognitive Models: A Tutorial with Applications](https://mlsanity.com/paper/2503.07681/) (2025)
- [Quantum-Cognitive Tunnelling Neural Networks for Military-Civilian Vehicle Classification and Sentiment Analysis](https://mlsanity.com/paper/2507.18645/) (2025)

## Abstract (arXiv)

Paradoxical decision-making behaviours such as preference reversal often arise from imprecise or noisy human preferences. Harnessing the physical principle of magnetisation reversal in ferromagnetic nanostructures, we developed a model that closely reflects human decision-making dynamics. Tested against a spectrum of psychological data, our model adeptly captures the complexities inherent in individual choices. This blend of physics and psychology paves the way for fresh perspectives on understanding the imprecision of human decision-making processes, extending the reach of the current classical and quantum physical models of human behaviour and decision-making.