Calibration of quantum decision theory: aversion to large losses and predictability of probabilistic choices
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 a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | Journal of Physics: Complexity |
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| Online Access: | https://doi.org/10.1088/2632-072X/acbd7e |
| _version_ | 1797794925214957568 |
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| author | T Kovalenko S Vincent V I Yukalov D Sornette |
| author_facet | T Kovalenko S Vincent V I Yukalov D Sornette |
| author_sort | T Kovalenko |
| collection | DOAJ |
| description | 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 (logit-CPT), 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 logit-CPT at both the aggregate and individual levels, and for all considered fit criteria for the first experiment iteration and for predictions (second ‘out-of-sample’ 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. |
| first_indexed | 2024-03-13T03:10:06Z |
| format | Article |
| id | doaj.art-a9fd08fa31bd4e41a3f4a72ca65494cb |
| institution | Directory Open Access Journal |
| issn | 2632-072X |
| language | English |
| last_indexed | 2024-03-13T03:10:06Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Journal of Physics: Complexity |
| spelling | doaj.art-a9fd08fa31bd4e41a3f4a72ca65494cb2023-06-26T15:09:29ZengIOP PublishingJournal of Physics: Complexity2632-072X2023-01-014101500910.1088/2632-072X/acbd7eCalibration of quantum decision theory: aversion to large losses and predictability of probabilistic choicesT Kovalenko0S Vincent1V I Yukalov2https://orcid.org/0000-0003-4833-0175D Sornette3ETH Zürich, Department of Management, Technology and Economics , Scheuchzerstrasse 7, 8092 Zürich, SwitzerlandETH Zürich, Department of Management, Technology and Economics , Scheuchzerstrasse 7, 8092 Zürich, SwitzerlandETH Zürich, Department of Management, Technology and Economics , Scheuchzerstrasse 7, 8092 Zürich, Switzerland; Bogolubov Laboratory of Theoretical Physics, Joint Institute for Nuclear Research , Dubna 141980, RussiaETH Zürich, Department of Management, Technology and Economics , Scheuchzerstrasse 7, 8092 Zürich, Switzerland; Swiss Finance Institute, c/o University of Geneva , 40 blvd. Du Pont d’Arve, CH 1211 Geneva 4, Switzerland; Institute of Risk Analysis, Prediction and Management (Risks-X), Southern University of Science and Technology , Shenzhen, People’s Republic of ChinaWe 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 (logit-CPT), 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 logit-CPT at both the aggregate and individual levels, and for all considered fit criteria for the first experiment iteration and for predictions (second ‘out-of-sample’ 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.https://doi.org/10.1088/2632-072X/acbd7equantum decision theoryprospect probabilityutility factorattraction factorstochastic cumulative prospect theorypredictability limit |
| spellingShingle | T Kovalenko S Vincent V I Yukalov D Sornette Calibration of quantum decision theory: aversion to large losses and predictability of probabilistic choices Journal of Physics: Complexity quantum decision theory prospect probability utility factor attraction factor stochastic cumulative prospect theory predictability limit |
| title | Calibration of quantum decision theory: aversion to large losses and predictability of probabilistic choices |
| title_full | Calibration of quantum decision theory: aversion to large losses and predictability of probabilistic choices |
| title_fullStr | Calibration of quantum decision theory: aversion to large losses and predictability of probabilistic choices |
| title_full_unstemmed | Calibration of quantum decision theory: aversion to large losses and predictability of probabilistic choices |
| title_short | Calibration of quantum decision theory: aversion to large losses and predictability of probabilistic choices |
| title_sort | calibration of quantum decision theory aversion to large losses and predictability of probabilistic choices |
| topic | quantum decision theory prospect probability utility factor attraction factor stochastic cumulative prospect theory predictability limit |
| url | https://doi.org/10.1088/2632-072X/acbd7e |
| work_keys_str_mv | AT tkovalenko calibrationofquantumdecisiontheoryaversiontolargelossesandpredictabilityofprobabilisticchoices AT svincent calibrationofquantumdecisiontheoryaversiontolargelossesandpredictabilityofprobabilisticchoices AT viyukalov calibrationofquantumdecisiontheoryaversiontolargelossesandpredictabilityofprobabilisticchoices AT dsornette calibrationofquantumdecisiontheoryaversiontolargelossesandpredictabilityofprobabilisticchoices |