Parametric Prospect Theory : Experimental Design, Certainty E⁄Ects and Complexity Bias

We introduce an alternative approach to experimental design aimed at collecting data for parametric estimation of the cumulative form of Prospect Theory (PT). Our approach incorporates two fundamental principles: ensuring that all tasks provide valuable information and avoiding redundancy among tasks. Using this methodology, we collect data and estimate model parameters using a Hierarchical Bayesian approach that accounts for complexity biases and sure-thing bias. The results indicate that respondents exhibit a bias towards negative sure-things, although to a lesser extent for positive sure-things. We also nd weak evidence supporting a bias towards three-payo⁄ prospects compared to two-payo⁄ prospects. Thus, while biases are present, there is no clear uniform bias for or against complexity. Furthermore, we observe that choices in the gain domain are more predictable, suggesting that respondents nd gain domain tasks cognitively easier compared to making choices in the loss and mixed domains