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Human decision-making is increasingly supported by artificial intelligence (AI) systems. From medical imaging analysis to self-driving vehicles, AI systems are becoming organically embedded in a host of different technologies. However, incorporating such advice into decision-making entails a human rationalization of AI outputs for supporting beneficial outcomes. Recent research suggests intermediate judgments in the first stage of a decision process can interfere with decisions in subsequent stages. For this reason, we extend this research to AI-supported decision-making to investigate how intermediate judgments on AI-provided advice may influence subsequent decisions. In an online experiment (N = 192), we found a consistent bolstering effect in trust for those who made intermediate judgments and over those who did not. Furthermore, violations of total probability were observed at all timing intervals throughout the study. We further analyzed the results by demonstrating how quantum probability theory can model these types of behaviors in human–AI decision-making and ameliorate the understanding of the interaction dynamics at the confluence of human factors and information features. [ABSTRACT FROM AUTHOR]

Human decision-making is increasingly supported by artificial intelligence (AI) systems. From medical imaging analysis to self-driving vehicles, AI systems are becoming organically embedded in a host of different technologies. However, incorporating such advice into decision-making entails a human rationalization of AI outputs for supporting beneficial outcomes. Recent research suggests intermediate judgments in the first stage of a decision process can interfere with decisions in subsequent stages. For this reason, we extend this research to AI-supported decision-making to investigate how intermediate judgments on AI-provided advice may influence subsequent decisions. In an online experiment (N = 192), we found a consistent bolstering effect in trust for those who made intermediate judgments and over those who did not. Furthermore, violations of total probability were observed at all timing intervals throughout the study. We further analyzed the results by demonstrating how quantum probability theory can model these types of behaviors in human–AI decision-making and ameliorate the understanding of the interaction dynamics at the confluence of human factors and information features.

How burdens of proof are allocated in science has an important bearing on how new knowledge develops. Usually, the burden is on new theories to prove their worth relative to a default, baseline of knowledge that is considered established and secure. However, in the case of classical vs. quantum social science matters are not that simple because the long-standing classical default has itself already failed to pass crucial tests, which has spurred the search for quantum solutions instead. Part I of this paper, therefore, tries to 're-balance' the burdens of proof with Quantum Mind and Social Science' critics, by highlighting two significant limits to date of classical thinking about the mind and society: the philosophical problem of finding a place for consciousness in the universe, and the scientific problem of explaining the Kahneman–Tversky anomalies in psychology. Acknowledging these outstanding problems does not equalize the burdens of proof, but it does mean that as we head into the more substantive discussion in Part II there is no secure default position. Just burdens of proof all around. [ABSTRACT FROM AUTHOR]

This paper describes an approach to economics that is inspired by quantum computing, and is motivated by the need to develop a consistent quantum mathematical framework for economics. The traditional neoclassical approach assumes that rational utility-optimisers drive market prices to a stable equilibrium, subject to external perturbations or market failures. While this approach has been highly influential, it has come under increasing criticism following the financial crisis of 2007/8. The quantum approach, in contrast, is inherently probabilistic and dynamic. Decision-makers are described, not by a utility function, but by a propensity function which specifies the probability of transacting. We show how a number of cognitive phenomena such as preference reversal and the disjunction effect can be modelled by using a simple quantum circuit to generate an appropriate propensity function. Conversely, a general propensity function can be quantized, via an entropic force, to incorporate effects such as interference and entanglement that characterise human decision-making. Applications to some common problems and topics in economics and finance, including the use of quantum artificial intelligence, are discussed.

We present the first calibration of quantum decision theory (QDT) to a dataset of binary risky choice. We quantitatively account for the fraction of choice reversals between two repetitions of the experiment, using a probabilistic choice formulation in the simplest form without model assumption or adjustable parameters. The prediction of choice reversal is then refined by introducing heterogeneity between decision makers through their differentiation into two groups: ‘majoritarian’ and ‘contrarian’ (in proportion 3:1). This supports the first fundamental tenet of QDT, which models choice as an inherent probabilistic process, where the probability of a prospect can be expressed as the sum of its utility and attraction factors. We propose to parameterize the utility factor with a stochastic version of cumulative prospect theory (logit-CPT), and the attraction factor with a constant absolute risk aversion function. For this dataset, and penalising the larger number of QDT parameters via the Wilks test of nested hypotheses, the QDT model is found to perform significantly better than logit-CPT at both the aggregate and individual levels, and for all considered fit criteria for the first experiment iteration and for predictions (second ‘out-of-sample’ iteration). The distinctive QDT effect captured by the attraction factor is mostly appreciable (i.e. most relevant and strongest in amplitude) for prospects with big losses. Our quantitative analysis of the experimental results supports the existence of an intrinsic limit of predictability, which is associated with the inherent probabilistic nature of choice. The results of the paper can find applications both in the prediction of choice of human decision makers as well as for organizing the operation of artificial intelligence.

Developments in digitalization are in actual scientific discussion with leading focus on the automatizing of tasks especially in managers´ decision making, but also cover the view on the daily managerial task-fulfilment with digitalized solutions. Challenge for the managers is hereby the design of the digitalized work in a human-task-technology system by using advanced working models, methods and solutions. This compels to rethink causal-deterministic approaches and find new solutions, which especially occur in tasks of the strategic management. The purpose of this study was twofold. The first target was to identify and analyse the models and characteristics of strategic management decisions using approaches of the system theory, cybernetic management, and quantum theory. The second aim was to outline a framework of design parameters for creating causal-deterministic and holistic approaches in complex and complementary decision situations, so that they can be designed and processed digitally and implemented into human-task-technology systems, with outline the way how managerial decision-making will be changed.

The review presents the basics of quantum decision theory, with an emphasis on temporary processes in decision making. The aim is to explain the principal points of the theory. How an operationally-testable, rational choice between alternatives differs from a choice decorated by irrational feelings is elucidated. Quantum-classical correspondence is emphasized. A model of quantum intelligence network is described. Dynamic inconsistencies are shown to be resolved in the frame of the quantum decision theory.

The article reads Alexander Wendt's Quantum Mind and Social Science as a work of social theory in which crucial element is its account of action or agency. It argues that the success of quantum decision theory is implying that human behaviour is quantum that is likely to have been produced by a quantum brain. It states that Wendt combines quantum measurement, quantum linguistics and quantum decision theory into a contextual account of human agency.

Abstract: This comment addresses some of the critiques of “Quantum Mind and Social Science” by the other contributors, with special reference to the challenge that the mind‐body problem poses to conventional, classical thinking about social science. A quantum social ontology transcends those challenges, and in the process could transform social scientific thinking across the board. As an example the essay concludes with a consideration of the physics of social structure. [ABSTRACT FROM AUTHOR]

Abstract: Alexander Wendt suggests that an emerging field of research on quantum consciousness may have important implications for our understanding of the social world as well. He wants us to consider that the operations of the brain are not “classical” but rather quantum‐mechanical, and that this sheds light on the social worker as well. This article casts doubt on these claims. First, the idea of panpsychism (all physical things possess features of consciousness) is implausible on its face. Second, the findings of “quantum decision theory” are much more limited than Wendt recognizes. Third, the biological quantum effects currently subject to investigation are limited in their application to brains and persons by unavoidable limits of time and space scale on quantum effects. The “brain as quantum computer” or “brain as wave function” theory is very implausible given current knowledge. [ABSTRACT FROM AUTHOR]

We suggest a model of a multi-agent society of decision makers taking decisions being based on two criteria, one is the utility of the prospects and the other is the attractiveness of the considered prospects. The model is the generalization of quantum decision theory, developed earlier for single decision makers realizing one-step decisions, in two principal aspects. First, several decision makers are considered simultaneously, who interact with each other through information exchange. Second, a multistep procedure is treated, when the agents exchange information many times. Several decision makers exchanging information and forming their judgment, using quantum rules, form a kind of a quantum information network, where collective decisions develop in time as a result of information exchange. In addition to characterizing collective decisions that arise in human societies, such networks can describe dynamical processes occurring in artificial quantum intelligence composed of several parts or in a cluster of quantum computers. The practical usage of the theory is illustrated on the dynamic disjunction effect for which three quantitative predictions are made: (i) the probabilistic behavior of decision makers at the initial stage of the process is described; (ii) the decrease of the difference between the initial prospect probabilities and the related utility factors is proved; (iii) the existence of a common consensus after multiple exchange of information is predicted. The predicted numerical values are in very good agreement with empirical data. [ABSTRACT FROM AUTHOR]

Information foraging connects optimal foraging theory in ecology withhow humans search for information. The theory suggests that, followingan information scent, the information seeker must optimize the tradeoffbetween exploration by repeated steps in the search space vs.exploitation, using the resources encountered. We conjecture that thistradeoff characterizes how a user deals with uncertainty and its twoaspects, risk and ambiguity in economic theory. Risk is related to theperceived quality of the actually visited patch of information, and canbe reduced by exploiting and understanding the patch to a better extent.Ambiguity, on the other hand, is the opportunity cost of having higherquality patches elsewhere in the search space. The aforementionedtradeoff depends on many attributes, including traits of the user: atthe two extreme ends of the spectrum, analytic and wholistic searchersemploy entirely different strategies. The former type focuses onexploitation first, interspersed with bouts of exploration, whereas thelatter type prefers to explore the search space first and consume later.Our findings from an eye-tracking study of experts' interactions withnovel search interfaces in the biomedical domain suggest that usertraits of cognitive styles and perceived search task difficultyare significantly correlated with eye gaze and search behaviour. Wealso demonstrate that perceived risk shifts the balance betweenexploration and exploitation in either type of users, tilting it againstvs. in favour of ambiguity minimization. Since the pattern of behaviourin information foraging is quintessentially sequential, risk andambiguity minimization cannot happen simultaneously, leading to afundamental limit on how good such a tradeoff can be. This in turnconnects information seeking with the emergent field of quantum decisiontheory.

Information foraging connects optimal foraging theory in ecology with how humans search for information. The theory suggests that, following an information scent, the information seeker must optimize the trade off between exploration by repeated steps in the search space vs. exploitation, using the resources encountered. We conjecture that this trade off characterizes how a user deals with uncertainty and its two aspects, risk and ambiguity in economic theory. Risk is related to the perceived quality of the actually visited patch of information, and can be reduced by exploiting and understanding the patch to a better extent. Ambiguity, on the other hand, is the opportunity cost of having higher quality patches elsewhere in the search space. The aforementioned trade off depends on many attributes, including traits of the user: at the two extreme ends of the spectrum, analytic and wholistic searchers employ entirely different strategies. The former type focuses on exploitation first, interspersed with bouts of exploration, whereas the latter type prefers to explore the search space first and consume later. Our findings from an eye-tracking study of experts’ interactions with novel search interfaces in the biomedical domain suggest that user traits of cognitive styles and perceived search task difficulty are significantly correlated with eye gaze and search behavior. We also demonstrate that perceived risk shifts the balance between exploration and exploitation in either type of users, tilting it against vs. in favor of ambiguity minimization. Since the pattern of behavior in information foraging is quintessentially sequential, risk and ambiguity minimization cannot happen simultaneously, leading to a fundamental limit on how good such a trade off can be. This in turn connects information seeking with the emergent field of quantum decision theory. [ABSTRACT FROM AUTHOR]

Common decision-making models assume that the system would change over time. But when system is in a particular moment, decision-maker has to make a specific judgment which would put the system in a determined state not an uncertain state. Most decision-making models emphasize the determined state of the system after the judgment and ignore the uncertain state prior to the judgment. So, it would be biased in simulating real-life decision experiments if these uncertain information were lost. Besides, some cognitive decision-making experiments have demonstrated that the law of total probability would not be applicable in practice. What is the reason for this phenomenon? Some researchers thought that interference effects in decision-making process is the main reason, which has been verified from the mathematical proof. Moreover, the interference effects is produced due existing of uncertain information. Quantum theory uses probability amplitude to model uncertainty information of system that would exist simultaneously in multiple states. It is able to better model uncertain states before making a judgement, where judgement can be understood as measurement in quantum theory. Hence, the paper proposed an inferable dynamic Markov decision-making model to quantitatively predict and determine the value of interference effects in the decision-making process. The new model used probability amplitude to represent uncertain information in the decision process, and combined it with Markov decision-making model to study the evolution between different moments in the decision process. Additionally, based on quantum theory principles, it is possible to estimate and infer the related parameters reasonably. [ABSTRACT FROM AUTHOR]

Recently, mathematical models based on quantum formalism have been developed in cognitive science. The target articles in this special issue of Topics in Cognitive Science clearly illustrate how quantum theoretical formalism can account for various aspects of human judgment and decision making in a quantitatively and mathematically rigorous manner. In this commentary, we show how future studies in quantum cognition and decision making should be developed to establish theoretical foundations based on physical theory, by introducing Taketani's three-stage theory of the development of science. Also, implications for neuroeconomics (another rapidly evolving approach to human judgment and decision making) are discussed. [ABSTRACT FROM AUTHOR]

In this paper, we extend von Neumann and Morgenstern's expected utility approach to a non-commutative probability theory. We introduce a new representation of the decision maker's set of events which extends the canonical representation. We reformulate von Neumann and Morgenstern's approach to modeling decision maker behavior by non-commutative probability theory. We introduce a set of preference axioms similar to von Neumann and Morgenstern's axioms, and show that they lead to a generalization of the expected utility theorem. Our generalization allows for decision makers to make an intuitive distinction between representations of a set of events. We find that this methodology enables several paradoxes and inconsistencies in traditional expected utility theory (e.g., Allais paradox, etc.) to be solved or better understood. [ABSTRACT FROM AUTHOR]

Sophisticated models of human social behavior are fast becoming highly desirable in an increasingly complex and interrelated world. Here, we propose that rather than taking established theories from the physical sciences and naively mapping them into the social world, the advanced concepts and theories of social psychology should be taken as a starting point, and used to develop a new modeling methodology. In order to illustrate how such an approach might be carried out, we attempt to model the low elaboration attitude changes of a society of agents in an evolving social context. We propose a geometric model of an agent in context, where individual agent attitudes are seen to self-organize to form ideologies, which then serve to guide further agent-based attitude changes. A computational implementation of the model is shown to exhibit a number of interesting phenomena, including a tendency for a measure of the entropy in the system to decrease, and a potential for externally guiding a population of agents toward a new desired ideology. [ABSTRACT FROM AUTHOR]

Abstract: We consider the dating market decision problem under the quantum mechanics point of view. Quantum states whose associated amplitudes are modified by men strategies are used to represent women. The Grover quantum search algorithm is used as a playing strategy. Success is more frequently obtained by playing quantum than playing classic. [Copyright &y& Elsevier]

Abstract: We present a general theory of quantum information processing devices, that can be applied to human decision makers, to atomic multimode registers, or to molecular high-spin registers. Our quantum decision theory is a generalization of the quantum theory of measurement, endowed with an action ring, a prospect lattice and a probability operator measure. The algebra of probability operators plays the role of the algebra of local observables. Because of the composite nature of prospects and of the entangling properties of the probability operators, quantum interference terms appear, which make actions noncommutative and the prospect probabilities nonadditive. The theory provides the basis for explaining a variety of paradoxes typical of the application of classical utility theory to real human decision making. The principal advantage of our approach is that it is formulated as a self-consistent mathematical theory, which allows us to explain not just one effect but actually all known paradoxes in human decision making. Being general, the approach can serve as a tool for characterizing quantum information processing by means of atomic, molecular, and condensed-matter systems. [Copyright &y& Elsevier]

This paper describes an approach to economics that is inspired by quantum computing, and is motivated by the need to develop a consistent quantum mathematical framework for economics. The traditional neoclassical approach assumes that rational utility-optimisers drive market prices to a stable equilibrium, subject to external perturbations or market failures. While this approach has been highly influential, it has come under increasing criticism following the financial crisis of 2007/8. The quantum approach, in contrast, is inherently probabilistic and dynamic. Decision-makers are described, not by a utility function, but by a propensity function which specifies the probability of transacting. We show how a number of cognitive phenomena such as preference reversal and the disjunction effect can be modelled by using a simple quantum circuit to generate an appropriate propensity function. Conversely, a general propensity function can be quantized, via an entropic force, to incorporate effects such as interference and entanglement that characterise human decision-making. Applications to some common problems and topics in economics and finance, including the use of quantum artificial intelligence, are discussed., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Orrell and Houshmand.)

The review presents the basics of quantum decision theory, with an emphasis on temporary processes in decision making. The aim is to explain the principal points of the theory. How an operationally-testable, rational choice between alternatives differs from a choice decorated by irrational feelings is elucidated. Quantum-classical correspondence is emphasized. A model of quantum intelligence network is described. Dynamic inconsistencies are shown to be resolved in the frame of the quantum decision theory. [ABSTRACT FROM AUTHOR]

Information foraging connects optimal foraging theory in ecology with how humans search for information. The theory suggests that, following an information scent, the information seeker must optimize the tradeoff between exploration by repeated steps in the search space vs. exploitation, using the resources encountered. We conjecture that this tradeoff characterizes how a user deals with uncertainty and its two aspects, risk and ambiguity in economic theory. Risk is related to the perceived quality of the actually visited patch of information, and can be reduced by exploiting and understanding the patch to a better extent. Ambiguity, on the other hand, is the opportunity cost of having higher quality patches elsewhere in the search space. The aforementioned tradeoff depends on many attributes, including traits of the user: at the two extreme ends of the spectrum, analytic and wholistic searchers employ entirely different strategies. The former type focuses on exploitation first, interspersed with bouts of exploration, whereas the latter type prefers to explore the search space first and consume later. Our findings from an eye-tracking study of experts' interactions with novel search interfaces in the biomedical domain suggest that user traits of cognitive styles and perceived search task difficulty are significantly correlated with eye gaze and search behavior. We also demonstrate that perceived risk shifts the balance between exploration and exploitation in either type of users, tilting it against vs. in favor of ambiguity minimization. Since the pattern of behavior in information foraging is quintessentially sequential, risk and ambiguity minimization cannot happen simultaneously, leading to a fundamental limit on how good such a tradeoff can be. This in turn connects information seeking with the emergent field of quantum decision theory.