Andraszewicz, S., & Rieskamp, J. (2017). Response to "A note on the standardized covariance". Journal of Mathematical Psychology, 77, 185-186. Download paper

In a recent paper, Andraszewicz and Rieskamp (2014) proposed the standardized covariance as a measure of association, similarity and co-riskiness. Budescu and Bo (in press) 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, in press). In the reply to this comment, we point out that the statistical interpretation of the standardized covariance provided by Budescu and Bo (in press) is strongly linked with its cognitive interpretation. Also, in this comment, we give a cognitive interpretation to the Budescu and Bo's (in press) analytical findings of the similarity measure for statistically independent gambles proposed by Andraszewicz and Rieskamp (2014).

Murphy, R.O., Andraszewicz, S. & Knaus, S.D. (2016). Real options in the laboratory: An experimental study of sequential investment decisions. Journal of Behavioural and Experimental Finance, 12, 23-39. Download paper

Many real-life risky decisions in finance and management are dynamic and decision policies can be adapted as uncertainty is reduced by the arrival of new information. In this type of situation, called a real options problem, a decision maker must choose how much of his finite resources to invest in a dynamic risky environment. In two laboratory experiments, we test a well-defined decision problem with the central characteristics of a real options framework and do so in such a way that it is amendable to formal modeling. We find that people choose differently than the expected value maximizing policy, consistent with risk aversion and non-linear probability weighting. We conclude that although real options analysis is useful as a normative valuation method, its recommendations are sometimes contrary to people's innate tendencies when making risky choices and this counterintuitiveness should be considered when implementing real options analysis in training and practice.

Andraszewicz, S., Scheibehenne, B., Rieskamp, J., Grasman, R., Verhagen, J., & Wagenmakers, E-J. (2015). An introduction to Bayesian hypothesis testing for management research. Special Issue: Bayesian Probability and Statistics in Management Research. 41 (2), 521-543. Download paper Download R-code

In management research, empirical data are often analyzed using p-value null hypothesis significance testing (pNHST). Here we outline the conceptual and practical advantages of an alternative analysis method: Bayesian hypothesis testing and model selection using the Bayes factor. In contrast to pNHST, Bayes factors allow researchers to quantify evidence in favor of the null hypothesis. Also, Bayes factors do not require adjustment for the intention with which the data were collected. The use of Bayes factors is demonstrated through an extended example for hierarchical regression based on the design of an experiment recently published in the Journal of Management. This example also highlights the fact that p values overestimate the evidence against the null hypothesis, misleading researchers into believing that their findings are more reliable than is warranted by the data.

Andraszewicz, S., Rieskamp, J., & Scheibehenne, B. (2015). How outcome dependencies affect decisions under risk. Decision, 2(2), 127-144. Download paper Download supplementary materials

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.

Andraszewicz, S., & Rieskamp, J. (2014). Standardized covariance - a measure of association, similarity and co-riskiness between choice options. Journal of Matethematical Psychology, 61, 25-37. Download paper Download data

Predictions of prominent theories of decision making, such as decision field theory and regret theory, strongly depend on the association between outcomes of choice options. In the present work, we show that these associations reflect the similarity of two choice options and riskiness of one option with respect to the other. We propose a measure labeled standardized covariance that can capture the strength of the association, similarity and co-riskiness between two choice options. We describe the properties and interpretation of this measure and show its similarities to and differences from the correlation measure. Finally, we show how the predictions of different models of decision making vary depending on the value of the standardized covariance, which can have implications for research on decision making under risk.

Andraszewicz, S., Yamagishi, J., & King, S. (2011). Vocal attractiveness of statistical speech synthesisers. In Icassp (p. 5368-5371). Prag, Czech Republic: IEEE. Download paper

Our previous analysis of speaker-adaptive HMM-based speech syn- thesis methods suggested that there are two possible reasons why average voices can obtain higher subjective scores than any individual adapted voice: 1) model adaptation degrades speech quality proportionally to the distance "moved" by the transforms, and 2) psychoa- coustic effects relating to the attractiveness of the voice. This paper is a follow-on from that analysis and aims to separate these effects out. Our latest perceptual experiments focus on attractiveness, using average voices and speaker-dependent voices without model transformation, and show that using several speakers to create a voice improves smoothness (measured by Harmonics-to-Noise Ratio), reduces distance from the the average voice in the log F0-F1 space of the final voice and hence makes it more attractive at the segmental level. However, this is weakened or overridden at supra-segmental or sentence levels.