Calibration of quantum decision theory: aversion to large losses and predictability of probabilistic choices

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