Abstract
Quantum decision theory (QDT) is a novel theory of decision making based on the mathematics of Hilbert spaces, a framework known in physics for its application to quantum mechanics. This framework formalizes the concept of uncertainty and other effects that are particularly manifest in cognitive processes, which makes it well suited for the study of decision making. QDT describes a decision maker's choice as a stochastic event occurring with a probability that is the sum of an objective utility factor and a subjective attraction factor. This article offers a practical guide to researchers who are interested in applying QDT to a data set of binary lotteries in the domain of gains. We find that our results are in good agreement with the quarter law, a quantitative prediction of QDT. We examine gender differences in our sample in order to illustrate how QDT can be used to differentiate between different groups. We find that women in our sample are on average more risk-averse than men, but stress that our sample is too small to generalize this result to the population outside our sample.
Highlights
In this article, we apply quantum decision theory (QDT) to a dataset of choices between a certain and a risky lottery, both in the domain of gains
Each decision task consists in a choice between (1) a risky option in which the decision maker can win a fixed amount, here y = 50 CHF with probability p, or nothing with probability 1 − p, and (2) a certain option in which the decision maker gets an amount x with certainty, where 0 < x < y
It is apparent in this panel that the majority of decision tasks were given only once, based on markers area as well as the fact that most points are situated at p(L1) = 0 or p(L1) = 1
Summary
We apply quantum decision theory (QDT) to a dataset of choices between a certain and a risky lottery, both in the domain of gains. Each decision task consists in a choice between (1) a risky option in which the decision maker can win a fixed amount, here y = 50 CHF with probability p, or nothing with probability 1 − p, and (2) a certain option in which the decision maker gets an amount x with certainty, where 0 < x < y. A typical question could be to choose between the following options: “option 1: get CHF 50 with a 40% chance (or nothing with a 60% chance); option 2: get CHF 10 for sure.”. Our dataset includes the 200 decisions performed by each of 27 subjects with various values of p and x. A typical question could be to choose between the following options: “option 1: get CHF 50 with a 40% chance (or nothing with a 60% chance); option 2: get CHF 10 for sure.” Our dataset includes the 200 decisions performed by each of 27 subjects with various values of p and x.
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