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Quantum Decision-Making: From Cognitive Psychology and Social Science to Artificial Intelligent Systems

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Quantum Information".

Deadline for manuscript submissions: closed (1 December 2023) | Viewed by 25765

Special Issue Editor

Dr. Irina Basieva

E-Mail Website
Guest Editor
International Center for Mathematical Modeling in Physics and Cognitive Science, Linnaeus University, S-35195 Växjö, Sweden
Interests: quantum mechanics; probability; laser physics; telecommunications; artificial intelligence; cognitive psychology; decision making; rationality

Special Issue Information

Dear Colleagues,

The aim of this issue is to provide an overview of the latest challenges and achievements in the domain of the application of quantum models in a wide range of sciences, including but not limited to cognitive psychology, behavioral economics, biology, and artificial intelligence.

While being perfectly linear, quantum probabilistic models naturally cover such unorthodox phenomena as the question-order effect, conjunction and disjunction effects, positive or negative interference, update on zero prior, etc. At the same time, in certain aspects quantum models may be more restrictive than classical ones.

Variability and randomness are of a different nature in classical and quantum models. 

There are known cases when quantum model research in cognitive psychology stimulated research in quantum physics, in particular, on the problem of combining the question-order effect and response replicability.

How exactly the electrochemical processes in the brain lead to specific rumination outcomes is an immensely difficult "micro-macro" problem. Potentially, we can analyze the relative success of the different learning techniques to better understand what is going on in the mysterious black box of the intellect, artificial or human.

We hope that this issue will advance awareness and stimulate further development of the quantum model applications to decision-making under uncertainty.

Dr. Irina Basieva
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • quantum probability approach in social sciences
  • quantum-like models
  • quantum inspired algorithms
  • cognitive psychology
  • behavioral economics
  • QQ equality
  • open systems

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Published Papers (7 papers)

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Research

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18 pages, 3774 KiB  
Article
A Quantum Model of Trust Calibration in Human–AI Interactions
by Luisa Roeder, Pamela Hoyte, Johan van der Meer, Lauren Fell, Patrick Johnston, Graham Kerr and Peter Bruza
Entropy 2023, 25(9), 1362; https://doi.org/10.3390/e25091362 - 20 Sep 2023
Cited by 5 | Viewed by 2603
Abstract
This exploratory study investigates a human agent’s evolving judgements of reliability when interacting with an AI system. Two aims drove this investigation: (1) compare the predictive performance of quantum vs. Markov random walk models regarding human reliability judgements of an AI system and [...] Read more.
This exploratory study investigates a human agent’s evolving judgements of reliability when interacting with an AI system. Two aims drove this investigation: (1) compare the predictive performance of quantum vs. Markov random walk models regarding human reliability judgements of an AI system and (2) identify a neural correlate of the perturbation of a human agent’s judgement of the AI’s reliability. As AI becomes more prevalent, it is important to understand how humans trust these technologies and how trust evolves when interacting with them. A mixed-methods experiment was developed for exploring reliability calibration in human–AI interactions. The behavioural data collected were used as a baseline to assess the predictive performance of the quantum and Markov models. We found the quantum model to better predict the evolving reliability ratings than the Markov model. This may be due to the quantum model being more amenable to represent the sometimes pronounced within-subject variability of reliability ratings. Additionally, a clear event-related potential response was found in the electroencephalographic (EEG) data, which is attributed to the expectations of reliability being perturbed. The identification of a trust-related EEG-based measure opens the door to explore how it could be used to adapt the parameters of the quantum model in real time. Full article
(This article belongs to the Special Issue Quantum Decision-Making: From Cognitive Psychology and Social Science to Artificial Intelligent Systems)
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12 pages, 1236 KiB  
Article
On Laws of Thought—A Quantum-like Machine Learning Approach
by Lizhi Xin, Kevin Xin and Houwen Xin
Entropy 2023, 25(8), 1213; https://doi.org/10.3390/e25081213 - 15 Aug 2023
Cited by 1 | Viewed by 2216
Abstract
Incorporating insights from quantum theory, we propose a machine learning-based decision-making model, including a logic tree and a value tree; a genetic programming algorithm is applied to optimize both the logic tree and value tree. The logic tree and value tree together depict [...] Read more.
Incorporating insights from quantum theory, we propose a machine learning-based decision-making model, including a logic tree and a value tree; a genetic programming algorithm is applied to optimize both the logic tree and value tree. The logic tree and value tree together depict the entire decision-making process of a decision-maker. We applied this framework to the financial market, and a “machine economist” is developed to study a time series of the Dow Jones index. The “machine economist” will obtain a set of optimized strategies to maximize profits, and discover the efficient market hypothesis (random walk). Full article
(This article belongs to the Special Issue Quantum Decision-Making: From Cognitive Psychology and Social Science to Artificial Intelligent Systems)
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17 pages, 306 KiB  
Article
Contextuality in Collective Intelligence: Not There Yet
by William Sulis and Ali Khan
Entropy 2023, 25(8), 1193; https://doi.org/10.3390/e25081193 - 11 Aug 2023
Cited by 2 | Viewed by 1369
Abstract
Type I contextuality or inconsistent connectedness is a fundamental feature of both the classical as well as the quantum realms. Type II contextuality (true contextuality or CHSH-type contextuality) is frequently asserted to be specific to the quantum realm. Nevertheless, evidence for Type II [...] Read more.
Type I contextuality or inconsistent connectedness is a fundamental feature of both the classical as well as the quantum realms. Type II contextuality (true contextuality or CHSH-type contextuality) is frequently asserted to be specific to the quantum realm. Nevertheless, evidence for Type II contextuality in classical settings is slowly emerging (at least in the psychological realm). Sign intransitivity can be observed in preference relations in the setting of decision making and so intransitivity in decision making may also yield examples of Type II contextuality. Previously, it was suggested that a fruitful setting in which to search for such contextuality is that of decision making by collective intelligence systems. An experiment was conducted by using a detailed simulation of nest emigration by workers of the ant Temnothorax albipennis. In spite of the intransitivity, these simulated colonies came close to but failed to violate Dzhafarov’s inequality for a 4-cyclic system. Further research using more sophisticated simulations and experimental paradigms is required. Full article
(This article belongs to the Special Issue Quantum Decision-Making: From Cognitive Psychology and Social Science to Artificial Intelligent Systems)
37 pages, 428 KiB  
Article
The No-Cloning Life: Uniqueness and Complementarity in Quantum and Quantum-like Theories
by Arkady Plotnitsky
Entropy 2023, 25(5), 706; https://doi.org/10.3390/e25050706 - 24 Apr 2023
Cited by 2 | Viewed by 1860
Abstract
This article considers a rarely discussed aspect, the no-cloning principle or postulate, recast as the uniqueness postulate, of the mathematical modeling known as quantum-like, Q-L, modeling (vs. classical-like, C-L, modeling, based in the mathematics adopted from classical physics) and the corresponding Q-L theories [...] Read more.
This article considers a rarely discussed aspect, the no-cloning principle or postulate, recast as the uniqueness postulate, of the mathematical modeling known as quantum-like, Q-L, modeling (vs. classical-like, C-L, modeling, based in the mathematics adopted from classical physics) and the corresponding Q-L theories beyond physics. The principle is a transfer of the no-cloning principle (arising from the no-cloning theorem) in quantum mechanics (QM) to Q-L theories. My interest in this principle, to be related to several other key features of QM and Q-L theories, such as the irreducible role of observation, complementarity, and probabilistic causality, is connected to a more general question: What are the ontological and epistemological reasons for using Q-L models vs. C-L ones? I shall argue that adopting the uniqueness postulate is justified in Q-L theories and adds an important new motivation for doing so and a new venue for considering this question. In order to properly ground this argument, the article also offers a discussion along similar lines of QM, providing a new angle on Bohr’s concept of complementarity via the uniqueness postulate. Full article
(This article belongs to the Special Issue Quantum Decision-Making: From Cognitive Psychology and Social Science to Artificial Intelligent Systems)
22 pages, 825 KiB  
Article
Quantum Circuit Components for Cognitive Decision-Making
by Dominic Widdows, Jyoti Rani and Emmanuel M. Pothos
Entropy 2023, 25(4), 548; https://doi.org/10.3390/e25040548 - 23 Mar 2023
Cited by 11 | Viewed by 9536
Abstract
This paper demonstrates that some non-classical models of human decision-making can be run successfully as circuits on quantum computers. Since the 1960s, many observed cognitive behaviors have been shown to violate rules based on classical probability and set theory. For example, the order [...] Read more.
This paper demonstrates that some non-classical models of human decision-making can be run successfully as circuits on quantum computers. Since the 1960s, many observed cognitive behaviors have been shown to violate rules based on classical probability and set theory. For example, the order in which questions are posed in a survey affects whether participants answer ‘yes’ or ‘no’, so the population that answers ‘yes’ to both questions cannot be modeled as the intersection of two fixed sets. It can, however, be modeled as a sequence of projections carried out in different orders. This and other examples have been described successfully using quantum probability, which relies on comparing angles between subspaces rather than volumes between subsets. Now in the early 2020s, quantum computers have reached the point where some of these quantum cognitive models can be implemented and investigated on quantum hardware, by representing the mental states in qubit registers, and the cognitive operations and decisions using different gates and measurements. This paper develops such quantum circuit representations for quantum cognitive models, focusing particularly on modeling order effects and decision-making under uncertainty. The claim is not that the human brain uses qubits and quantum circuits explicitly (just like the use of Boolean set theory does not require the brain to be using classical bits), but that the mathematics shared between quantum cognition and quantum computing motivates the exploration of quantum computers for cognition modeling. Key quantum properties include superposition, entanglement, and collapse, as these mathematical elements provide a common language between cognitive models, quantum hardware, and circuit implementations. Full article
(This article belongs to the Special Issue Quantum Decision-Making: From Cognitive Psychology and Social Science to Artificial Intelligent Systems)
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18 pages, 332 KiB  
Article
On Applicability of Quantum Formalism to Model Decision Making: Can Cognitive Signaling Be Compatible with Quantum Theory?
by Andrei Khrennikov
Entropy 2022, 24(11), 1592; https://doi.org/10.3390/e24111592 - 2 Nov 2022
Cited by 5 | Viewed by 2610
Abstract
This note is devoted to the problem of signaling (marginal inconsistency) in the Bell-type experiments with physical and cognitive systems. It seems that in quantum physics, this problem is still not taken seriously. Only recently have experimenters started to check the signaling hypothesis [...] Read more.
This note is devoted to the problem of signaling (marginal inconsistency) in the Bell-type experiments with physical and cognitive systems. It seems that in quantum physics, this problem is still not taken seriously. Only recently have experimenters started to check the signaling hypothesis for their data. For cognitive systems, signaling was statistically significant in all experiments (typically for decision making) performed up to today. Here, one cannot simply ignore this problem. Since signaling contradicts the quantum theory of measurement for compatible observables, its statistical significance in experiments with humans can be considered as an objection for quantum-like modeling—applications of quantum theory to cognition, decision making, psychology, economics and finance, social and political science. In this paper, we point to two possible sources of signaling generation that are consistent with quantum measurement theory. Thus, the signaling objection for quantum-like modeling is not catastrophic. One of these sources is the direct physical signaling about selection of experimental settings, questions or tasks in quantum-like studies. Another possible source is a state modification dependent on experimental settings. The latter was a rather common source of signaling in quantum physics. Since the physical size of the brain is very small comparing with the light velocity, it seems to be impossible to prevent the direct physical signaling (with electromagnetic waves) between the brain’s areas processing two questions a and b. However, if, for these questions, not the electromagnetic waves, but electrochemical communication plays the crucial role, the experimenter may hope to make signaling weaker by answering the questions faster. The problem of question-dependent mental state modification seems to be solvable via smarter experimental design. This paper can be useful both for physicists interested in quantum foundations and for researchers working in quantum-like studies, e.g., applying the quantum theory to model decision making or psychological effects. This paper is solely about quantum theory. Thus, we do not consider general contextual probabilistic models. Full article
(This article belongs to the Special Issue Quantum Decision-Making: From Cognitive Psychology and Social Science to Artificial Intelligent Systems)

Review

Jump to: Research

24 pages, 397 KiB  
Review
Open Systems, Quantum Probability, and Logic for Quantum-like Modeling in Biology, Cognition, and Decision-Making
by Andrei Khrennikov
Entropy 2023, 25(6), 886; https://doi.org/10.3390/e25060886 - 1 Jun 2023
Cited by 8 | Viewed by 3131
Abstract
The aim of this review is to highlight the possibility of applying the mathematical formalism and methodology of quantum theory to model behavior of complex biosystems, from genomes and proteins to animals, humans, and ecological and social systems. Such models are known as [...] Read more.
The aim of this review is to highlight the possibility of applying the mathematical formalism and methodology of quantum theory to model behavior of complex biosystems, from genomes and proteins to animals, humans, and ecological and social systems. Such models are known as quantum-like, and they should be distinguished from genuine quantum physical modeling of biological phenomena. One of the distinguishing features of quantum-like models is their applicability to macroscopic biosystems or, to be more precise, to information processing in them. Quantum-like modeling has its basis in quantum information theory, and it can be considered one of the fruits of the quantum information revolution. Since any isolated biosystem is dead, modeling of biological as well as mental processes should be based on the theory of open systems in its most general form—the theory of open quantum systems. In this review, we explain its applications to biology and cognition, especially theory of quantum instruments and the quantum master equation. We mention the possible interpretations of the basic entities of quantum-like models with special interest given to QBism, as it may be the most useful interpretation. Full article
(This article belongs to the Special Issue Quantum Decision-Making: From Cognitive Psychology and Social Science to Artificial Intelligent Systems)
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