Hostname: page-component-8448b6f56d-mp689 Total loading time: 0 Render date: 2024-04-18T02:43:24.030Z Has data issue: false hasContentIssue false
  • English
  • Français

What are the appropriate axioms of rationality for reasoning under uncertainty with resource-constrained systems?

Published online by Cambridge University Press:  11 March 2020

Harald Atmanspacher ,
Irina Basieva ,
Jerome R. Busemeyer Open the ORCID record for Jerome R. Busemeyer [Opens in a new window] ,
Andrei Y. Khrennikov ,
Emmanuel M. Pothos ,
Richard M. Shiffrin  and
Zheng Wang
Harald Atmanspacher
Affiliation:
Collegium Helveticum, Zürich, 8006Switzerlandatmanspacher@collegium.ethz.chhttps://collegium.ethz.ch/en/about-us/staff/pd-dr-harald-atmanspacher/
Irina Basieva
Affiliation:
Department of Psychology, City University London, LondonEC1V 0HB, United Kingdomirina.basieva@gmail.comEmmanuel.Pothos.1@city.ac.ukhttps://uk.linkedin.com/in/irina-basieva-3182b1108https://www.city.ac.uk/people/academics/emmanuel-pothos
Jerome R. Busemeyer
Affiliation:
Psychological Brain Sciences, Indiana University, IN47405jbusemey@indiana.edushiffrin@indiana.eduhttp://mypage.iu.edu/~jbusemey/home.htmlhttp://shiffrin.cogs.indiana.edu
Andrei Y. Khrennikov
Affiliation:
Department of Mathematics at Linnaeus University, Linnaeus University, 351 95Växjö, Sweden. andrei.khrennikov@lnu.sehttps://lnu.se/en/staff/andrei.khrennikov/
Emmanuel M. Pothos
Affiliation:
Department of Psychology, City University London, LondonEC1V 0HB, United Kingdomirina.basieva@gmail.comEmmanuel.Pothos.1@city.ac.ukhttps://uk.linkedin.com/in/irina-basieva-3182b1108https://www.city.ac.uk/people/academics/emmanuel-pothos
Richard M. Shiffrin
Affiliation:
Psychological Brain Sciences, Indiana University, IN47405jbusemey@indiana.edushiffrin@indiana.eduhttp://mypage.iu.edu/~jbusemey/home.htmlhttp://shiffrin.cogs.indiana.edu
Zheng Wang
Affiliation:
Department of Communication, The Ohio State University, Columbus, OH43210. wang.1243@osu.eduhttps://comm.osu.edu/people/wang.1243
Get access Rights & Permissions [Opens in a new window]

Abstract

When constrained by limited resources, how do we choose axioms of rationality? The target article relies on Bayesian reasoning that encounter serious tractability problems. We propose another axiomatic foundation: quantum probability theory, which provides for less complex and more comprehensive descriptions. More generally, defining rationality in terms of axiomatic systems misses a key issue: rationality must be defined by humans facing vague information.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 43 , 2020 , e2
Check for updates
Copyright
Copyright © Cambridge University Press 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aerts, D., Gabora, L. & Sozzo, S. (2013) Concepts and their dynamics: A quantum–theoretic modeling of human thought. Topics in Cognitive Science 5:737–72.Google ScholarPubMed
Atmanspacher, H. & Römer, H. (2012) Order effects in sequential measurements of non-commuting psychological observables. Journal of Mathematical Psychology 56(4):274–80.CrossRefGoogle Scholar
Basieva, I., Khrennikova, P., Pothos, E. M., Asano, M. & Khrennikov, A. (2018) Quantum-like model of subjective expected utility. Journal of Mathematical Economics 78:150–62.CrossRefGoogle Scholar
Bruza, P. D., Wang, Z. & Busemeyer, J. R. (2015) Quantum cognition: A new theoretical approach to psychology. Trends in Cognitive Sciences 19(7):383–93.CrossRefGoogle ScholarPubMed
Gleason, A. M. (1957) Measures on the closed subspaces of a Hilbert space. Journal of Mathematics and Mechanics 6(6):885–93.Google Scholar
Khrennikov, A., Basieva, I., Pothos, E. M. & Yamato, I. (2018) Quantum probability in decision making from quantum information representation of neuronal states. Scientific Reports 8(1):16225.CrossRefGoogle ScholarPubMed
Lake, B. M., Salakhutdinov, R. & Tenenbaum, J. B. (2015) Human-level concept learning through probabilistic program induction. Science 350:1332–38.CrossRefGoogle ScholarPubMed
Pothos, E. M. & Busemeyer, J. R. (2013) Can quantum probability provide a new direction for cognitive modeling? Behavioral and Brain Sciences 36(3):255–74.CrossRefGoogle ScholarPubMed
Sanborn, A. N., Griffiths, T. L. & Navarro, D. J. (2010) Rational approximations to rational models: Alternative algorithms for category learning. Psychological Review 117(4):1144–67. doi:10.1037/a0020511.CrossRefGoogle ScholarPubMed
Tenenbaum, J. B., Kemp, C., Griffiths, T. L. & Goodman, N. (2011) How to grow a mind: Statistics, structure, and abstraction. Science 331:1279–85.CrossRefGoogle Scholar
Wang, Z., Solloway, T., Shiffrin, R. M. & Busemeyer, J. R. (2014) Context effects produced by question orders reveal quantum nature of human judgments. Proceedings of the National Academy of Sciences 111(26):9431–36.CrossRefGoogle ScholarPubMed
Yukalov, V. I. & Sornette, D. (2011) Decision theory with prospect interference and entanglement. Theory and Decision 70(3):283–328.CrossRefGoogle Scholar