Title  Calibration of quantum decision theory: aversion to large losses and predictability of probabilistic choices 
Author  
Corresponding Author  Yukalov, V.I. 
Publication Years  20230301

DOI  
Source Title  
EISSN  2632072X

Volume  4 
Abstract  We present the first calibration of quantum decision theory (QDT) to a dataset of binary risky choice. We quantitatively account for the fraction of choice reversals between two repetitions of the experiment, using a probabilistic choice formulation in the simplest form without model assumption or adjustable parameters. The prediction of choice reversal is then refined by introducing heterogeneity between decision makers through their differentiation into two groups: ‘majoritarian’ and ‘contrarian’ (in proportion 3:1). This supports the first fundamental tenet of QDT, which models choice as an inherent probabilistic process, where the probability of a prospect can be expressed as the sum of its utility and attraction factors. We propose to parameterize the utility factor with a stochastic version of cumulative prospect theory (logitCPT), and the attraction factor with a constant absolute risk aversion function. For this dataset, and penalising the larger number of QDT parameters via the Wilks test of nested hypotheses, the QDT model is found to perform significantly better than logitCPT at both the aggregate and individual levels, and for all considered fit criteria for the first experiment iteration and for predictions (second ‘outofsample’ iteration). The distinctive QDT effect captured by the attraction factor is mostly appreciable (i.e. most relevant and strongest in amplitude) for prospects with big losses. Our quantitative analysis of the experimental results supports the existence of an intrinsic limit of predictability, which is associated with the inherent probabilistic nature of choice. The results of the paper can find applications both in the prediction of choice of human decision makers as well as for organizing the operation of artificial intelligence. © 2023 The Author(s). Published by IOP Publishing Ltd. 
Indexed By  
Language  English

SUSTech Authorship  Others

Funding Project  We are grateful to R O Murphy and R H W ten Brincke for the provided experimental data and useful discussions, and to M Siffert for important remarks. This work was partially supported by the Swiss National Foundation, under Grant for the project on ‘Quantum Decision Theory’. One of the authors (V I Y ) thanks E P Yukalova for discussions.We are grateful to R O Murphy and R H W ten Brincke for the provided experimental data and useful discussions, and to M Siffert for important remarks. This work was partially supported by the Swiss National Foundation, under Grant 105218 _ 1 59461 for the project on ‘Quantum Decision Theory’. One of the authors (V I Y ) thanks E P Yukalova for discussions.

WOS Accession No  WOS:000941054800001

Publisher  
EI Accession Number  20231113697030

EI Keywords  Calibration
; Decision making
; Forecasting
; Quantum theory
; Statistical tests
; Stochastic systems

ESI Classification Code  Control Systems:731.1
; Management:912.2
; Mathematical Statistics:922.2
; Quantum Theory; Quantum Mechanics:931.4
; Systems Science:961

Data Source  EV Compendex

Citation statistics 
Cited Times [WOS]:1

Document Type  Journal Article 
Identifier  http://kc.sustech.edu.cn/handle/2SGJ60CL/519670 
Department  Southern University of Science and Technology 
Affiliation  1.ETH Zürich, Department of Management, Technology and Economics, Scheuchzerstrasse 7, Zürich; 8092, Switzerland 2.Bogolubov Laboratory of Theoretical Physics, Joint Institute for Nuclear Research, Dubna; 141980, Russia 3.Swiss Finance Institute, c/o University of Geneva, 40 blvd. Du Pont d’Arve, Geneva 4; CH 1211, Switzerland 4.Institute of Risk Analysis, Prediction and Management (RisksX), Southern University of Science and Technology, Shenzhen, China 
Recommended Citation GB/T 7714 
Kovalenko, T.,Vincent, S.,Yukalov, V.I.,et al. Calibration of quantum decision theory: aversion to large losses and predictability of probabilistic choices[J]. Journal of Physics: Complexity,2023,4.

APA 
Kovalenko, T.,Vincent, S.,Yukalov, V.I.,&Sornette, D..(2023).Calibration of quantum decision theory: aversion to large losses and predictability of probabilistic choices.Journal of Physics: Complexity,4.

MLA 
Kovalenko, T.,et al."Calibration of quantum decision theory: aversion to large losses and predictability of probabilistic choices".Journal of Physics: Complexity 4(2023).

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