Information integration in risky decision making was studied by using a functional measurement approach. Participants (N=84) played a computerized risky card game incorporating a within-subject design with the 3 factors (a) probability of negative or positive outcome, (b) amount of gain, and (c) amount of loss. Results on the group level showed mainly additive patterns of integration. Observed deviations from the general pattern could be explained more detailed by the results of the individual analyses: There was a wide range of different strategies from centration to additive and mixed additive-multiplicative strategies. The most frequent rules of integration were additive; pure multiplicative rules were rarely found. These findings give support to additive models in risky decision making. However, individual differences in risky decision making strategies appear to be a topic that deserves further study.
The presented study had two main aims. The first was to compare loss aversion in risky choices and anticipated loss aversion in predicting one's emotions in response to future loss. The second aim was to compare both kinds of loss aversion in two culturally different samples of university students (175 students in Slovakia, mean age 21.4, and 124 students in Ecuador, mean age 22.9). The research hypotheses were based on the assumption of loss aversion universality. The authoresses summarized research results as follows: 1. Ecuadorean students showed a significantly lower loss aversion than Slovaks in risky choice. 2. They found unexpectedly that results of 'affective forecasting' indicated a near absence of loss aversion in anticipating loss/gain in the Ecuadorean sample. 3. Correlation between loss aversion in risky choice and anticipated loss aversion was to some extent present only in the Slovak sample and was not present in the Ecuadorean sample. The results of their research join those findings that indicate the need to correct the universalistic point of view in basic decision making models and the need to learn and include cultural variations in how different cultures perceive the world in decision models.
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