English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

We present the first calibration of quantum decision theory (QDT) to adataset of binary risky choice. We quantitatively account for the fraction ofchoice reversals between two repetitions of the experiment, using aprobabilistic choice formulation in the simplest form without model assumptionor adjustable parameters. The prediction of choice reversal is then refined byintroducing heterogeneity between decision makers through their differentiationinto two groups: ``majoritarian'' and ``contrarian'' (in proportion 3:1). Thissupports the first fundamental tenet of QDT, which models choice as an inherentprobabilistic process, where the probability of a prospect can be expressed asthe sum of its utility and attraction factors. We propose to parameterise theutility factor with a stochastic version of cumulative prospect theory(logit-CPT), and the attraction factor with a constant absolute risk aversion(CARA) function. For this dataset, and penalising the larger number of QDTparameters via the Wilks test of nested hypotheses, the QDT model is found toperform significantly better than logit-CPT at both the aggregate andindividual levels, and for all considered fit criteria for the first experimentiteration and for predictions (second ``out-of-sample'' iteration). Thedistinctive 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 ofan intrinsic limit of predictability, which is associated with the inherentprobabilistic nature of choice. The results of the paper can find applicationsboth in the prediction of choice of human decision makers as well as fororganizing the operation of artificial intelligence.

Calibration of Quantum Decision Theory: Aversion to Large Losses and Predictability of Probabilistic Choices