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Quantum stochastic walks on networks for decision-making

Overview of attention for article published in Scientific Reports, March 2016
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43 Mendeley
Title
Quantum stochastic walks on networks for decision-making
Published in
Scientific Reports, March 2016
DOI 10.1038/srep23812
Pubmed ID
Authors

Ismael Martínez-Martínez, Eduardo Sánchez-Burillo

Abstract

Recent experiments report violations of the classical law of total probability and incompatibility of certain mental representations when humans process and react to information. Evidence shows promise of a more general quantum theory providing a better explanation of the dynamics and structure of real decision-making processes than classical probability theory. Inspired by this, we show how the behavioral choice-probabilities can arise as the unique stationary distribution of quantum stochastic walkers on the classical network defined from Luce's response probabilities. This work is relevant because (i) we provide a very general framework integrating the positive characteristics of both quantum and classical approaches previously in confrontation, and (ii) we define a cognitive network which can be used to bring other connectivist approaches to decision-making into the quantum stochastic realm. We model the decision-maker as an open system in contact with her surrounding environment, and the time-length of the decision-making process reveals to be also a measure of the process' degree of interplay between the unitary and irreversible dynamics. Implementing quantum coherence on classical networks may be a door to better integrate human-like reasoning biases in stochastic models for decision-making.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 2%
Taiwan 1 2%
Unknown 41 95%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 16%
Researcher 6 14%
Student > Ph. D. Student 5 12%
Student > Bachelor 4 9%
Professor 3 7%
Other 6 14%
Unknown 12 28%
Readers by discipline Count As %
Economics, Econometrics and Finance 6 14%
Psychology 5 12%
Physics and Astronomy 4 9%
Computer Science 4 9%
Mathematics 2 5%
Other 7 16%
Unknown 15 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 March 2022.
All research outputs
#13,753,838
of 23,317,888 outputs
Outputs from Scientific Reports
#62,680
of 126,060 outputs
Outputs of similar age
#148,599
of 302,193 outputs
Outputs of similar age from Scientific Reports
#1,659
of 3,215 outputs
Altmetric has tracked 23,317,888 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 126,060 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.3. This one is in the 48th percentile – i.e., 48% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 302,193 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 3,215 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.