Ambiguity effect: decision-making influenced by lack of information

Quite often, the best human decision-makers outperform computer-aided decision systems. It is not only because humans can take into account faint pieces of information that cannot be formalized but also that they occasionally behave intuitively, which can hardly be incorporated into a formal optimization criterion. Therefore, mathematicians enhance their decision models to make their behavior similar to that of human decision-makers. They fit decision models up with different parameters controlling the optimality of the considered decision. From this point of view, the simplest and perhaps the most popular is the Hurwitz coefficient of pessimism controlling whether the decision process tends to expect more the best or the worst outcome. In this paper, we design a model with a parameter controlling the strength of ambiguity aversion of the resulting decision process.