Abstract
We analyse a quantum-like Bayesian Network that puts together cause/effect relationships and semantic similarities between events. These semantic similarities constitute acausal connections according to the Synchronicity principle and provide new relationships to like probabilistic graphical models. As a consequence, beliefs (or any other event) can be represented in vector spaces, in which quantum parameters are determined by the similarities that these vectors share between them. Events attached by a semantic meaning do not need to have an explanation in terms of cause and effect.
This work was supported by national funds through Fundação para a Ciência e Tecnologia (FCT) with reference UID/CEC/50021/2013 and through the PhD grant SFRH/BD/92391/2013.
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References
Aerts, D.: Quantum structures: an attempt to explain the origin of their appearance in nature. Int. J. Theor. Phys. 34, 1–22 (1995)
Aerts, D.: Quantum theory and human perception of the macro-world. Front. Psychol. 5, 1–19 (2014)
Aerts, D., Broekaert, J., Gabora, L.: A case for applying an abstracted quantum formalism to cognition. New Ideas Psychol. 29, 136–146 (2011)
Bordley, R.: Quantum mechanical and human violations of compound probability principles: toward a generalized heisenberg uncertainty principle. Oper. Res. 46, 923–926 (1998)
Busemeyer, J.: Cognitive science contributions to decision science. Cognition 135, 43–46 (2015)
Busemeyer, J., Bruza, P.: Quantum Model of Cognition and Decision. Cambridge University Press, Cambridge (2012)
Busemeyer, J., Wang, Z.: Quantum cognition: key issues and discussion. Topics Cogn. Sci. 6, 43–46 (2014)
Busemeyer, J., Wang, Z., Townsend, J.: Quantum dynamics of human decision making. J. Math. Psychol. 50, 220–241 (2006)
Busemeyer, J.R., Wang, Z., Trueblood, J.S.: Hierarchical Bayesian estimation of quantum decision model parameters. In: Busemeyer, J.R., Dubois, F., Lambert-Mogiliansky, A., Melucci, M. (eds.) QI 2012. LNCS, vol. 7620, pp. 80–89. Springer, Heidelberg (2012)
Busemeyer, J., Wang, Z., Shiffrin, R.: Bayesian model comparison favours quantum over standard decision theory account of dynamic inconsistency. Decision 2, 1–12 (2015)
Caves, C., Fuchs, C., Schack, R.: Quantum probabilities as Bayesian probabilities. Phys. Rev. A 65, 022305 (2002)
Feynman, R., Leighton, R., Sands, M.: The Feynman Lectures on Physics: Quantum Mechanics. Addison-Wesley, Reading (1965)
Griffiths, T., Kemp, C., Tenenbaum, J.: Bayesian models of inductive learning. In: Proceedings of the Annual Conference of the Cognitive Science Society (2008)
Haven, E., Khrennikov, A.: Quantum Social Science. Cambridge University Press, Cambridge (2013)
Jung, C., Pauli, W.: The Interpretation of Nature and the Psyche. Ishi Press, USA (2012)
Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge (2009)
Lambert-Mogiliansky, A., Zamir, S., Zwirn, H.: Type indeterminacy: a model for the KT(Kahneman-Tversky)-man. J. Math. Psychol. 53, 349–361 (2009)
Leifer, M., Poulin, D.: Quantum graphical models and belief propagation. Ann. Phys. J. 323, 1899–1946 (2008)
Lindorff, D.: Pauli and Jung: The Meeting of Two Great Minds. Quest Books, Wheaton (2004)
Martin, F., Carminati, F., Carminati, G.: Synchronicity, quantum information and the psyche. J. Cosmol. 3, 580–589 (2009)
Moreira, C., Wichert, A.: Interference effects in quantum belief networks. Appl. Soft Comput. 25, 64–85 (2014)
Moreira, C., Wichert, A.: The synchronicity principle under quantum probabilistic inferences. NeuroQuantology 13, 111–133 (2014)
Mura, P.L.: Projective expected utility. J. Math. Psychol. 53, 408–414 (2009)
Murphy, K.: Machine Learning: A Probabilistic Perspectives. MIT Press, Cambridge (2012)
Nielsen, M.A., Chuang, I.L.: Quantum Computation and Quantum Information. Cambridge University Press, Cambridge (2000)
Osherson, D.: Thinking. MIT Press, Cambridge (1995)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, San Francisco (1988)
Pothos, E., Busemeyer, J.: A quantum probability explanation for violations of rational decision theory. Proc. Roy. Soc. B 276, 2171–2178 (2009)
Russel, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Pearson Education, London (2010)
Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction and Search. MIT Press, Cambridge (2000)
Tucci, R.: Quantum Bayesian nets. Int. J. Mod. Phys. B 9, 295–337 (1995)
Tversky, A., Kahneman, D.: Extension versus intuitive reasoning: the conjunction fallacy in probability judgment. Psychol. Rev. 90, 293–315 (1983)
Tversky, A., Kahnenman, D.: Judgment under uncertainty: heuristics and biases. Science 185, 1124–1131 (1974)
Tversky, A., Shafir, E.: The disjunction effect in choice under uncertainty. Psychol. Sci. 3, 305–309 (1992)
Yukalov, V., Sornette, D.: Decision theory with prospect interference and entanglement. Theor. Decis. 70, 283–328 (2011)
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Moreira, C., Wichert, A. (2016). The Relation Between Acausality and Interference in Quantum-Like Bayesian Networks. In: Atmanspacher, H., Filk, T., Pothos, E. (eds) Quantum Interaction. QI 2015. Lecture Notes in Computer Science(), vol 9535. Springer, Cham. https://doi.org/10.1007/978-3-319-28675-4_10
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