Based on Cumulative Prospect Theory, three essays are presented in this thesis. All three works are linked by a deeper understanding of Probability Weighting Functions and its connection with decisions in a risk scenario. The first essay is an empirical work using prospect theory to analyze the narrow framing bias in investment decisions in certain emerging countries: Brazil, China, Russia, Mexico and South Africa. In all cases, we empirically identified the predictive power of prospect theory for stock returns. We also found that the probability weighting function is the most important factor in this predictive power. The second essay is a theoretical work proposing an axiomatization for the Goldstein-Einhorn weighting function. Since 1987, the well known Goldstein- Einhorn Weighting Function is widely used in many empirical and theoretical papers. Richard Gonzalez and George Wu proposed an axiomatization for it in 1999. The present work analyses their preference condition and finds a bigger family of weighting functions. We provide useful examples and suggest a new preference condition which is necessary and sufficient for Goldstein-Einhorn function. This new preference condition simulates the behavior of people in risky attitudes. The third essay propose a measure to evaluate the psychological features of attractiveness and discriminability in the context of probability weighting functions. These concepts are important to help us understand how some emotions drive our behavior. We propose measures in absolute and in the relative sense and compare with some particular cases found in the literature. Our findings are consistent with the qualitative understanding widespread in the literature and provide a quantitative analysis for it.