A Unified Theory of Human Judgements and Decision-Making under Uncertainty (original) (raw)
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A quantum theoretical explanation for probability judgment errors
Psychological Review, 2011
A quantum probability model is introduced and used to explain human probability judgment errors including the conjunction and disjunction fallacies, averaging effects, unpacking effects, and order effects on inference. On the one hand, quantum theory is similar to other categorization and memory models of cognition in that it relies on vector spaces defined by features, and similarities between vectors to determine probability judgments. On the other hand, quantum probability theory is a generalization of Bayesian probability theory because it is based on a set of (von Neumann) axioms that relax some of the classic (Kolmogorov) axioms. The quantum model is compared and contrasted with other competing explanations for these judgment errors including the anchoring and adjustment model for probability judgments. The quantum model introduces a new fundamental concept to cognition --the compatibility versus incompatibility of questions and the effect this can have on the sequential order of judgments.
Quantum Probability Explanations for Probability Judgment'Errors
2009
A quantum probability model is introduced and used to explain human probability judgment errors including the conjunction, disjunction, inverse, and conditional fallacies, as well as unpacking effects and partitioning effects. Quantum probability theory is a general and coherent theory based on a set of (von Neumann) axioms which relax some of the constraints underlying classic (Kolmogorov) probability theory. The quantum model is compared and contrasted with other competing explanations for these judgment errors including the representativeness heuristic, the averaging model, and a memory retrieval model for probability judgments. The quantum model also provides ways to extend Bayesian, fuzzy set, and fuzzy trace theories. We conclude that quantum information processing principles provide a viable and promising new way to understand human judgment and reasoning.
Modeling Human Decision-Making: An Overview of the Brussels Quantum Approach
Foundations of Science
We present the fundamentals of the quantum theoretical approach we have developed in the last decade to model cognitive phenomena that resisted modeling by means of classical logical and probabilistic structures, like Boolean, Kolmogorovian and, more generally, set theoretical structures. We firstly sketch the operational-realistic foundations of conceptual entities, i.e. concepts, conceptual combinations, propositions, decision-making entities, etc. Then, we briefly illustrate the application of the quantum formalism in Hilbert space to represent combinations of natural concepts, discussing its success in modeling a wide range of empirical data on concepts and their conjunction, disjunction and negation. Next, we naturally extend the quantum theoretical approach to model some long-standing 'fallacies of human reasoning', namely, the 'conjunction fallacy' and the 'disjunction effect'. Finally, we put forward an explanatory hypothesis according to which human reasoning is a defined superposition of 'emergent reasoning' and 'logical reasoning', where the former generally prevails over the latter. The quantum theoretical approach explains human fallacies as the consequence of genuine quantum structures in human reasoning, i.e. 'contextuality', 'emergence', 'entanglement', 'interference' and 'superposition'. As such, it is alternative to the Kahneman-Tversky research programme, which instead aims to explain human fallacies in terms of 'individual heuristics and biases'.
Quantum Models of Cognition and Decision
Quantum Models of Cognition and Decision
Much of our understanding of human thinking is based on probabilistic models. This innovative book by Jerome R. Busemeyer and Peter D. Bruza argues that, actually, the underlying mathematical structures from quantum theory provide a much better account of human thinking than traditional models. They introduce the foundations for modelling probabilistic-dynamic systems using two aspects of quantum theory. The first, “contextuality,” is away to understand interference effects found with inferences and decisions under conditions of uncertainty. The second, “quantum entanglement,” allows cognitive phenomena to be modelled in non-reductionist ways. Employing these principles drawn from quantum theory allows us to view human cognition and decision in a totally new light. Introducing the basic principles in an easy-to-follow way, this book does not assume a physics background or a quantum brain and comes complete with a tutorial and fully worked-out applications in important areas of cognition and decision
Uncertainty about the value of quantum probability for cognitive modeling
[This is a BBS commentary] I argue that the overly simplistic scenarios discussed by Pothos & Busemeyer (P&B) establish at best that quantum probability theory (QPT) is a logical possibility allowing distinct predictions from classical probability theory (CPT). But the article fails to provide convincing evidence for the proposal that QPT offers unique insights regarding cognition and the nature of human rationality.
Quantum cognition: a new theoretical approach to psychology
What type of probability theory best describes the way humans make judgments under uncertainty and decisions under conflict? Although rational models of cognition have become prominent and have achieved much success, they adhere to the laws of classical probability theory despite the fact that human reasoning does not always conform to these laws. For this reason we have seen the recent emergence of models based on an alternative probabilistic framework drawn from quantum theory. These quantum models show promise in addressing cognitive phenomena that have proven recalcitrant to modeling by means of classical probability theory. This review compares and contrasts probabilistic models based on Bayesian or classical versus quantum principles, and highlights the advantages and disadvantages of each approach.
Effectiveness of the quantum-mechanical formalism in cognitive modeling
Soft Computing, 2015
Traditional approaches to cognitive psychology are founded on a classical vision of logic and probability theory. According to this perspective, the probabilistic aspects of human reasoning can be formalized in a Kolmogorovian probability framework and reveal underlying Boolean-type logical structures. This vision has been seriously challenged by various discoveries in experimental psychology in the last three decades. Meanwhile, growing research indicates that quantum theory provides the conceptual and mathematical framework to deal with these classically problematical situations. In this paper we apply a general quantum-based modeling scheme to represent two types of cognitive situations where deviations from classical probability occur in human decisions, namely, 'conceptual categorization' and 'decision making'. We show that our quantum-theoretic modeling faithfully describes different sets of experimental data, explaining the observed deviations from classicality in terms of genuine quantum effects. These results may contribute to the development of applied disciplines where cognitive processes are involved, such as natural language processing, semantic analysis, and information retrieval.
Quantum Structures in Human Decision-Making: Towards Quantum Expected Utility
International Journal of Theoretical Physics, 2019
Ellsberg thought experiments and empirical confirmation of Ellsberg preferences pose serious challenges to subjective expected utility theory (SEUT). We have recently elaborated a quantum-theoretic framework for human decisions under uncertainty which satisfactorily copes with the Ellsberg paradox and other puzzles of SEUT. We apply here the quantum-theoretic framework to the Ellsberg two-urn example, showing that the paradox can be explained by assuming a state change of the conceptual entity that is the object of the decision (decision-making, or DM, entity) and representing subjective probabilities by quantum probabilities. We also model the empirical data we collected in a DM test on human participants within the theoretic framework above. The obtained results are relevant, as they provide a line to model real life, e.g., financial and medical, decisions that show the same empirical patterns as the two-urn experiment.
Explaining versus describing human decisions: Hilbert space structures in decision theory
Soft Computing, 2019
Despite the impressive success of quantum structures to model long-standing human judgement and decision puzzles, the quantum cognition research programme still faces challenges about its explanatory power. Indeed, quantum models introduce new parameters, which may fit empirical data without necessarily explaining them. Also, one wonders whether more general non-classical structures are better equipped to model cognitive phenomena. In this paper, we provide a realistic-operational foundation of decision processes using a known decision-making puzzle, the Ellsberg paradox, as a case study. Then, we elaborate a novel representation of the Ellsberg decision situation applying standard quantum correspondence rules which map realistic-operational entities into quantum mathematical terms. This result opens the way towards an independent, foundational rather than phenomenological, motivation for a general use of quantum Hilbert space structures in human cognition.
2013
We support the authors’ claims, except that we point out that also quantum structure different from quantum probability abundantly plays a role in human cognition. We put forward several elements to illustrate our point, mentioning entanglement, contextuality, interference, and emergence as effects, and states, observables, complex numbers, and Fock space as specific mathematical structures. The authors convincingly demonstrate the greater potential of quantum probability as compared with classical probability in modeling situations of human cognition, giving various examples to illustrate their analysis. In our commentary, we provide additional arguments to support their claim and approach. We want to point out, however, that it is not just quantum probability, but also much more specific quantum structures, quantum states, observables, complex numbers, and typical quantum spaces – for example, Fock space – that on a deep level provide a modeling of the structure of human thought i...