scholarly journals Quantum Bayesian perspective for intelligence reservoir characterization, monitoring and management

2017 ◽  
Vol 375 (2106) ◽  
pp. 20160398 ◽  
Author(s):  
Miguel Ángel Lozada Aguilar ◽  
Andrei Khrennikov ◽  
Klaudia Oleschko ◽  
María de Jesús Correa
Keyword(s):  
Decision Making ◽  
Quantum Theory ◽  
Reservoir Characterization ◽  
Quantum Probability ◽  
Geophysical Data ◽  
Interpretation Of Quantum Mechanics ◽  
Review Of The Literature ◽  
Classical Probability ◽  
Hydrocarbon Reservoir ◽  
Quantum Revolution

The paper starts with a brief review of the literature about uncertainty in geological, geophysical and petrophysical data. In particular, we present the viewpoints of experts in geophysics on the application of Bayesian inference and subjective probability. Then we present arguments that the use of classical probability theory (CP) does not match completely the structure of geophysical data. We emphasize that such data are characterized by contextuality and non-Kolmogorovness (the impossibility to use the CP model), incompleteness as well as incompatibility of some geophysical measurements. These characteristics of geophysical data are similar to the characteristics of quantum physical data. Notwithstanding all this, contextuality can be seen as a major deviation of quantum theory from classical physics. In particular, the contextual probability viewpoint is the essence of the Växjö interpretation of quantum mechanics. We propose to use quantum probability (QP) for decision-making during the characterization, modelling, exploring and management of the intelligent hydrocarbon reservoir . Quantum Bayesianism (QBism), one of the recently developed information interpretations of quantum theory, can be used as the interpretational basis for such QP decision-making in geology, geophysics and petroleum projects design and management. This article is part of the themed issue ‘Second quantum revolution: foundational questions’.

scholarly journals Quantum Bayesianism as the basis of general theory of decision-making

2016 ◽  
Vol 374 (2068) ◽  
pp. 20150245 ◽  
Author(s):  
Andrei Khrennikov
Keyword(s):  
Decision Making ◽  
Probability Theory ◽  
Subjective Probability ◽  
Quantum Probability ◽  
Total Probability ◽  
Interpretation Of Quantum Mechanics ◽  
Classical Probability ◽  
Probability Interpretation ◽  
Formula Of Total Probability

We discuss the subjective probability interpretation of the quantum-like approach to decision making and more generally to cognition. Our aim is to adopt the subjective probability interpretation of quantum mechanics, quantum Bayesianism (QBism), to serve quantum-like modelling and applications of quantum probability outside of physics. We analyse the classical and quantum probabilistic schemes of probability update, learning and decision-making and emphasize the role of Jeffrey conditioning and its quantum generalizations. Classically, this type of conditioning and corresponding probability update is based on the formula of total probability—one the basic laws of classical probability theory.

scholarly journals From axiomatics of quantum probability to modelling geological uncertainty and management of intelligent hydrocarbon reservoirs with the theory of open quantum systems

2018 ◽  
Vol 376 (2118) ◽  
pp. 20170225 ◽  
Author(s):  
Miguel Ángel Lozada Aguilar ◽  
Andrei Khrennikov ◽  
Klaudia Oleschko
Keyword(s):  
Decision Making ◽  
Hilbert Problem ◽  
Open Quantum Systems ◽  
Quantum Probability ◽  
Quantum Systems ◽  
Belief State ◽  
Hydrocarbon Reservoirs ◽  
Continue Development ◽  
Open Quantum ◽  
Hydrocarbon Reservoir

As was recently shown by the authors, quantum probability theory can be used for the modelling of the process of decision-making (e.g. probabilistic risk analysis) for macroscopic geophysical structures such as hydrocarbon reservoirs. This approach can be considered as a geophysical realization of Hilbert's programme on axiomatization of statistical models in physics (the famous sixth Hilbert problem). In this conceptual paper , we continue development of this approach to decision-making under uncertainty which is generated by complexity, variability, heterogeneity, anisotropy, as well as the restrictions to accessibility of subsurface structures. The belief state of a geological expert about the potential of exploring a hydrocarbon reservoir is continuously updated by outputs of measurements, and selection of mathematical models and scales of numerical simulation. These outputs can be treated as signals from the information environment E . The dynamics of the belief state can be modelled with the aid of the theory of open quantum systems: a quantum state (representing uncertainty in beliefs) is dynamically modified through coupling with E ; stabilization to a steady state determines a decision strategy. In this paper, the process of decision-making about hydrocarbon reservoirs (e.g. ‘explore or not?'; ‘open new well or not?’; ‘contaminated by water or not?’; ‘double or triple porosity medium?’) is modelled by using the Gorini–Kossakowski–Sudarshan–Lindblad equation. In our model, this equation describes the evolution of experts' predictions about a geophysical structure. We proceed with the information approach to quantum theory and the subjective interpretation of quantum probabilities (due to quantum Bayesianism). This article is part of the theme issue ‘Hilbert's sixth problem’.

Quantum Models of Cognition and Decision

2015 ◽  
Author(s):  
Jerome R. Busemeyer ◽  
Zheng Wang ◽  
Emmanuel Pothos
Keyword(s):  
Decision Making ◽  
Probability Theory ◽  
Dynamic Systems ◽  
Quantum Probability ◽  
Quantum Dynamic ◽  
Classical Probability ◽  
New Formalism ◽  
Probability Judgments ◽  
Advantages And Disadvantages ◽  
Quantum Approach

Quantum probability theory provides a new formalism for constructing probabilistic and dynamic systems of cognition and decision. The purpose of this chapter is to introduce psychologists to this fascinating theory. This chapter is organized into six sections. First, some of the basic psychological principles supporting a quantum approach to cognition and decision are summarized; second, some notations and definitions needed to understand quantum probability theory are presented; third, a comparison of quantum and classical probability theories is presented; fourth, quantum probability theory is used to account for some paradoxical findings in the field of human probability judgments; fifth, a comparison of quantum and Markov dynamic theories is presented; and finally, a quantum dynamic model is used to account for some puzzling findings of decision-making research. The chapter concludes with a summary of advantages and disadvantages of a quantum probability theoretical framework for modeling cognition and decision.

scholarly journals Approaching Bounded Rationality: From Quantum Probability to Criticality

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 745
Author(s):  
Lucio Tonello ◽  
Paolo Grigolini
Keyword(s):  
Decision Making ◽  
Quantum Mechanics ◽  
Bounded Rationality ◽  
Brain Function ◽  
Quantum Probability ◽  
Classical Probability ◽  
Human Behaviors ◽  
Decision Making Model

The bounded rationality mainstream is based on interesting experiments showing human behaviors violating classical probability (CP) laws. Quantum probability (QP) has been shown to successfully figure out such issues, supporting the hypothesis that quantum mechanics is the central fundamental pillar for brain function and cognition emergence. We discuss the decision-making model (DMM), a paradigmatic instance of criticality, which deals with bounded rationality issues in a similar way as QP, generating choices that cannot be accounted by CP. We define this approach as criticality-induced bounded rationality (CIBR). For some aspects, CIBR is even more satisfactory than QP. Our work may contribute to considering criticality as another possible fundamental pillar in order to improve the understanding of cognition and of quantum mechanics as well.

scholarly journals Perspectives on Correctness in Probabilistic Inference from Psychology

2019 ◽  
Vol 22 ◽  
Author(s):  
Emmanuel M. Pothos ◽  
Irina Basieva ◽  
Andrei Khrennikov ◽  
James M. Yearsley
Keyword(s):  
Decision Making ◽  
Information Theory ◽  
Probability Theory ◽  
Probabilistic Inference ◽  
Quantum Probability ◽  
Conjunction Fallacy ◽  
Selection Task ◽  
Classical Probability ◽  
Wason Selection Task ◽  
Classical Probability Theory

Abstract Research into decision making has enabled us to appreciate that the notion of correctness is multifaceted. Different normative framework for correctness can lead to different insights about correct behavior. We illustrate the shifts for correctness insights with two tasks, the Wason selection task and the conjunction fallacy task; these tasks have had key roles in the development of logical reasoning and decision making research respectively. The Wason selection task arguably has played an important part in the transition from understanding correctness using classical logic to classical probability theory (and information theory). The conjunction fallacy has enabled a similar shift from baseline classical probability theory to quantum probability. The focus of this overview is the latter, as it represents a novel way for understanding probabilistic inference in psychology. We conclude with some of the current challenges concerning the application of quantum probability theory in psychology in general and specifically for the problem of understanding correctness in decision making.

scholarly journals Cold and hot cognition: Quantum probability theory and realistic psychological modeling

2013 ◽  
Vol 36 (3) ◽  
pp. 282-283
Author(s):  
Philip J. Corr
Keyword(s):  
Decision Making ◽  
Probability Theory ◽  
Nonlinear Dynamic ◽  
Psychological Theory ◽  
Quantum Probability ◽  
Formal Modeling ◽  
Information Uncertainty ◽  
Dynamic Effects ◽  
Classical Probability ◽  
Human Decision

AbstractTypically, human decision making is emotionally “hot” and does not conform to “cold” classical probability (CP) theory. As quantum probability (QP) theory emphasises order, context, superimposition states, and nonlinear dynamic effects, one of its major strengths may be its power to unify formal modeling and realistic psychological theory (e.g., information uncertainty, anxiety, and indecision, as seen in the Prisoner's Dilemma).

scholarly journals Cognitive architectures combine formal and heuristic approaches

2013 ◽  
Vol 36 (3) ◽  
pp. 285-286 ◽  
Author(s):  
Cleotilde Gonzalez ◽  
Christian Lebiere
Keyword(s):  
Decision Making ◽  
Associative Learning ◽  
Cognitive Processes ◽  
Memory Retrieval ◽  
Quantum Probability ◽  
Cognitive Architectures ◽  
Alternative Account ◽  
Classical Probability ◽  
Partial Matching ◽  
Knowledge Based

AbstractQuantum probability (QP) theory provides an alternative account of empirical phenomena in decision making that classical probability (CP) theory cannot explain. Cognitive architectures combine probabilistic mechanisms with symbolic knowledge-based representations (e.g., heuristics) to address effects that motivate QP. They provide simple and natural explanations of these phenomena based on general cognitive processes such as memory retrieval, similarity-based partial matching, and associative learning.

scholarly journals Role of dashboards in improving decision making in healthcare: Review of the literature

2019 ◽  
Author(s):  
Aleeha Iftikhar ◽  
Raymond Bond ◽  
Victoria McGilligan ◽  
Stephen J Leslie ◽  
Khaled Rjoob ◽  
...  
Keyword(s):  
Decision Making ◽  
Review Of The Literature
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