edoc: No conditions. Results ordered -Date Deposited. 2024-10-12T21:46:34ZEPrintshttps://edoc.unibas.ch/images/uni-logo.jpghttps://edoc.unibas.ch/2018-09-24T16:34:41Z2018-09-24T16:34:41Zhttps://edoc.unibas.ch/id/eprint/52915This item is in the repository with the URL: https://edoc.unibas.ch/id/eprint/529152018-09-24T16:34:41ZHow outcome dependencies affect decisions under risk.Many economic theories of decision making assume that people evaluate options independently of other available options. However, recent cognitive theories such as decision field theory suggest that people’s evaluations rely on a relative comparison of the options’ potential consequences such that the subjective value of an option critically depends on the context in which it is presented. To test this prediction, we examined pairwise choices between monetary gambles and varied the degree to which the gambles’ outcomes covaried with one another. When people evaluate options by comparing their outcomes, a high covariance between these outcomes should make a decision easier, as suggested by decision field theory. In line with this prediction, the observed choice proportions in 2 experiments (N = 39 and 24, respectively) depended on the magnitude of the covariance. We call this effect the covariance effect. Our findings are in line with the theoretic predictions and show that the discriminability ratio in decision field theory can reflect the choice difficulty. These results confirm that interdependent evaluations of options play an important role in human decision making under risk and show that covariance is an important aspect of the choice context. Sandra AndraszewiczJörg RieskampBenjamin Scheibehenne2018-06-28T15:06:55Z2018-07-03T12:16:35Zhttps://edoc.unibas.ch/id/eprint/59024This item is in the repository with the URL: https://edoc.unibas.ch/id/eprint/590242018-06-28T15:06:55ZResponse to "A note on the standardized covariance"In a recent paper, Andraszewicz and Rieskamp (2014) proposed the standardized covariance as a measure of association, similarity and co-riskiness. Budescu and Bo (2017) wrote a comment on the proposed measure, in which they interpret the standardized covariance as a measure of additive association, or a "measure of disparity between the ranges of outcomes offered by two lotteries" (Budescu and Bo, 2017). In the reply to this comment, we point out that the statistical interpretation of the standardized covariance provided by Budescu and Bo (2017) is strongly linked with its cognitive interpretation. Also, in this comment, we give a cognitive interpretation to the Budescu and Bo's (2017) analytical findings of the similarity measure for statistically independent gambles proposed by Andraszewicz and Rieskamp (2014). (C) 2016 Elsevier Inc. All rights reserved. Sandra AndraszewiczJörg Rieskamp2014-07-01T13:12:29Z2018-04-22T04:31:44Zhttps://edoc.unibas.ch/id/eprint/33427This item is in the repository with the URL: https://edoc.unibas.ch/id/eprint/334272014-07-01T13:12:29ZQuntitative [i.e. Quantitative] analysis of risky decision making in economic environmentsRisky economic decisions play an important role in everyone's life. This dissertation presents mathematical approaches to the analysis of these decisions. It discusses how statistical measures can describe properties of choice options, and how these properties can be used to describe the decision context. Also, this dissertation includes a practical tutorial on a Bayesian approach to the hierarchical regression analysis in management science. Therefore, the combined dissertation presents mathematical and statistical tools in, and for better research of, decision making under risk. The first manuscript proposes standardized covariance, a measure that can quantitatively describe the strength of the association and similarity between choice options' outcomes. The standardized covariance can also describe how risky one option is with respect to another. It can influence predictions of choice models. The second manuscript shows experimentally how association measured with the standardized covariance can influence people's choices. The third manuscript proposes applying the expected shortfall of an option's outcomes as a measure of risk in the standard risk-value models. In an experiment, the risk-value shortfall model successfully predicted people's preference for options with higher expected value, lower variance and more positively skewed distributions of outcomes, and outperformed competing models. The fourth manuscript proposes a new version of a reinforcement learning model, which can be applied in a social context. The proposed model can account for the behavior of other people competing for a common pool resource. As experimentally tested, the model could successfully predict human behavior and correlated with the brain activity measured with an fMRI method. The last manuscript outlines advantages of using Bayes factors instead of p-values for interpretation of results from hierarchical regression analysis. As the results in the manuscript show, the Bayesian approach and the standard null-hypothesis statistical testing can lead to different conclusions. Sandra Andraszewicz