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

TL;DR

This paper calibrates quantum decision theory to binary risky choice data, demonstrating its superior predictive power over traditional models by accounting for probabilistic choice and decision-maker heterogeneity, especially in large-loss scenarios.

Contribution

It introduces a calibrated quantum decision theory model incorporating heterogeneity and probabilistic choice, outperforming logit-CPT in predicting risky decisions.

Findings

QDT models choice as an inherent probabilistic process.

QDT outperforms logit-CPT in predictive accuracy.

Attraction factors are most significant for prospects with large losses.

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 parameterise the utility 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 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.