# A Quantum-like Model for Predicting Human Decisions in the Entangled Social Systems

arXiv: https://arxiv.org/abs/2111.13902 · HTML: https://mlsanity.com/paper/2111.13902/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 88 references that are in the map.
- First posted on arXiv 27 Nov 2021.

## Builds on (most cited first, 9 of 9)

- [Statistical physics of human cooperation](https://mlsanity.com/paper/1705.07161/) (2017)
- [Evolutionary games on multilayer networks: A colloquium](https://mlsanity.com/paper/1504.04359/) (2015)
- [Testing quantum-like models of judgment for question order effect](https://mlsanity.com/paper/1501.04901/) (2015)
- [Uncertainty measurement with belief entropy on interference effect in Quantum-Like Bayesian Networks](https://mlsanity.com/paper/1709.02844/) (2017)
- [Grand challenges in social physics: In pursuit of moral behavior](https://mlsanity.com/paper/1810.05516/) (2018)
- [Explaining versus Describing Human Decisions. Hilbert Space Structures in Decision Theory](https://mlsanity.com/paper/1904.02523/) (2019)
- [Interference Effects in Quantum Belief Networks](https://mlsanity.com/paper/1409.8470/) (2014)
- [Asynchronous distributed algorithms for seeking generalized Nash equilibria under full and partial decision information](https://mlsanity.com/paper/1801.02967/) (2018)
- [Quantum Structures in Human Decision-making: Towards Quantum Expected Utility](https://mlsanity.com/paper/1811.00875/) (2018)

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

- [Quantum Adjoint Convolutional Layers for Effective Data Representation](https://mlsanity.com/paper/2404.17378/) (2024)

## Abstract (arXiv)

Human-centered systems of systems such as social networks, Internet of Things, or healthcare systems are growingly becoming major facets of modern life. Realistic models of human behavior in such systems play a significant role in their accurate modeling and prediction. Yet, human behavior under uncertainty often violates the predictions by the conventional probabilistic models. Recently, quantum-like decision theories have shown a considerable potential to explain the contradictions in human behavior by applying quantum probability. But providing a quantum-like decision theory that could predict, rather than describe the current, state of human behavior is still one of the unsolved challenges. The main novelty of our approach is introducing an entangled Bayesian network inspired by the entanglement concept in quantum information theory, in which each human is a part of the entire society. Accordingly, society's effect on the dynamic evolution of the decision-making process, which is less often considered in decision theories, is modeled by the entanglement measures. The proposed predictive entangled quantum-like Bayesian network (PEQBN) is evaluated on 22 experimental tasks. Results confirm that PEQBN provides more realistic predictions of human decisions under uncertainty, when compared with classical Bayesian networks and three recent quantum-like approaches.