# Quantum Operation of Affective Artificial Intelligence

arXiv: https://arxiv.org/abs/2305.08112 · HTML: https://mlsanity.com/paper/2305.08112/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 234 references that are in the map.
- First posted on arXiv 14 May 2023.

## Builds on (most cited first, 14 of 14)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Statistical physics of human cooperation](https://mlsanity.com/paper/1705.07161/) (2017)
- [Social physics](https://mlsanity.com/paper/2110.01866/) (2021)
- [Quantum-like models cannot account for the conjunction fallacy](https://mlsanity.com/paper/1606.03430/) (2015)
- [Evolutionary Processes in Quantum Decision Theory](https://mlsanity.com/paper/2006.16763/) (2020)
- [Testing quantum-like models of judgment for question order effect](https://mlsanity.com/paper/1501.04901/) (2015)
- [Quantum Probabilities as Behavioral Probabilities](https://mlsanity.com/paper/1703.05691/) (2017)
- [Information Processing by Networks of Quantum Decision Makers](https://mlsanity.com/paper/1712.05734/) (2017)
- [Tossing Quantum Coins and Dice](https://mlsanity.com/paper/2103.17007/) (2021)
- [A Resolution of St. Petersburg Paradox](https://mlsanity.com/paper/2111.14635/) (2021)
- [A Subjective Model of Human Decision Making Based on Quantum Decision Theory](https://mlsanity.com/paper/2101.05851/) (2021)
- [Quantification of emotions in decision making](https://mlsanity.com/paper/2203.02217/) (2022)
- [Quantum Uncertainty in Decision Theory](https://mlsanity.com/paper/2105.07877/) (2021)
- [Calibration of Quantum Decision Theory: Aversion to Large Losses and Predictability of Probabilistic Choices](https://mlsanity.com/paper/2303.02028/) (2023)

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

- [Discrete Versus Continuous Algorithms in Dynamics of Affective Decision Making](https://mlsanity.com/paper/2309.00357/) (2023)

## Abstract (arXiv)

The review analyzes the fundamental principles which Artificial Intelligence should be based on in order to imitate the realistic process of taking decisions by humans experiencing emotions. Two approaches are compared, one based on quantum theory and the other employing classical terms. Both these approaches have a number of similarities, being principally probabilistic. The analogies between quantum measurements under intrinsic noise and affective decision making are elucidated. It is shown that cognitive processes have many features that are formally similar to quantum measurements. This, however, in no way means that for the imitation of human decision making Affective Artificial Intelligence has necessarily to rely on the functioning of quantum systems. Appreciating the common features between quantum measurements and decision making helps for the formulation of an axiomatic approach employing only classical notions. Artificial Intelligence, following this approach, operates similarly to humans, by taking into account the utility of the considered alternatives as well as their emotional attractiveness. Affective Artificial Intelligence, whose operation takes account of the cognition-emotion duality, avoids numerous behavioural paradoxes of traditional decision making. A society of intelligent agents, interacting through the repeated multistep exchange of information, forms a network accomplishing dynamic decision making. The considered intelligent networks can characterize the operation of either a human society of affective decision makers, or the brain composed of neurons, or a typical probabilistic network of an artificial intelligence.