Operator sum representation for Markov transition models of human inference processes

Ji Woong Lee, Shashi Phoha

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The standard Bayesian framework does not account for the decision maker's errors, such as the conjunction and disjunction fallacies, in updating the belief state upon measuring the uncertain quantities. Moreover, the classical probability theory does not distinguish a mixed state (e.g., being of two minds) from a superposed state (e.g., being of neutral mind). The operator sum representation of completely positive linear maps can address these limitations in a unified manner, and allows for numerical determination of a general belief-state update rule. In particular, examples show that the operator sum approach yields an improvement over the existing quantum model of human inference, and that it can be used to explain the mixing and superposition effects of gossip interactions.

Original languageEnglish (US)
Title of host publication2016 American Control Conference, ACC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1590-1595
Number of pages6
ISBN (Electronic)9781467386821
DOIs
StatePublished - Jul 28 2016
Event2016 American Control Conference, ACC 2016 - Boston, United States
Duration: Jul 6 2016Jul 8 2016

Publication series

NameProceedings of the American Control Conference
Volume2016-July
ISSN (Print)0743-1619

Other

Other2016 American Control Conference, ACC 2016
CountryUnited States
CityBoston
Period7/6/167/8/16

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Lee, J. W., & Phoha, S. (2016). Operator sum representation for Markov transition models of human inference processes. In 2016 American Control Conference, ACC 2016 (pp. 1590-1595). [7525143] (Proceedings of the American Control Conference; Vol. 2016-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2016.7525143