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Journal of Mathematical Psychology

The dynamics of decision making when probabilities are vaguely specified

Emmanuel M. Pothos, Richard M. Shiffrin, Jerome R. Busemeyer

Published Oct 5, 2013• 4 citations
Publisher
DOI
10.1016/j.jmp.2013.09.001

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What connects minimal decision-making to contextual probability? We address this question through the Tug-of-War (TOW) principle, a resource-coupled mechanism for multi-armed bandit decision-making. Starting from classical probability, we show that when reward-learning functionality and specified information requirements are fixed, minimizing the necessary relaxation of common event semantics can lead to nonclassical probability. We derive the exact minimum semantic repair and identify the conditions under which information efficiency, calibration, and compositional consistency select contextual statistics. We then construct a single quantum Tug-of-War (QTOW) instrument in which the same decision--reward process both improves stationary decision performance and drives the unconditional learned state beyond the Klyachko--Can--Binicioğlu--Shumovsky (KCBS) noncontextuality bound. A finite diagnostic based on the instrument's recovery operations also yields a corrected contextuality witness. These results connect minimal decision-making, operational economy, and the emergence of contextual probability.
The sure thing principle and the law of total probability are basic laws in classic probability theory. A disjunction fallacy leads to the violation of these two classical probability laws. In this paper, a new quantum dynamic belief decision making model based on quantum dynamic modelling and Dempster-Shafer (D-S) evidence theory is proposed to address this issue and model the real human decision-making process. Some mathematical techniques are borrowed from quantum mathematics. Generally, belief and action are two parts in a decision making process. The uncertainty in belief part is represented by a superposition of certain states. The uncertainty in actions is represented as an extra uncertainty state. The interference effect is produced due to the entanglement between beliefs and actions. Basic probability assignment (BPA) of decisions is generated by quantum dynamic modelling. Then BPA of the extra uncertain state and an entanglement degree defined by an entropy function named Deng entropy are used to measure the interference effect. Compared the existing model, the number of free parameters is less in our model. Finally, a classical categorization decision-making experiment is illustrated to show the effectiveness of our model.
Quantum interference effects play a fundamental role in social decision-making, offering a novel perspective beyond classical decision theories. This study explores the theoretical foundations of quantum decision-making, emphasizing the role of quantum probability in explaining cognitive biases, preference reversals, and decision anomalies. The research presents a quantum probability-based decision model and contrasts it with classical probability models. Through theoretical modeling and experimental design, we demonstrate how quantum interference alters decision probabilities and impacts group decision dynamics. The study further examines the applications of quantum decision theory in game theory, policy-making, financial markets, and artificial intelligence. Our findings suggest that quantum probability provides a more comprehensive and realistic framework for understanding human decision-making processes. Future research should focus on empirical validation, interdisciplinary integration, and the application of quantum computing to social decision modeling.