In optimization the constructed mathematical models are very often idealized mappings of the actual problem. Considering human decision-making processes there is always a chance that cognitive biases occur when constructing the objective function and the constrains. Misrepresented human desires in the objective function or in the constrains result non-acceptable outcome for the decision-maker. To solve the problem of uncertainty concerning the search space we propose the use of fuzzy search space. Bacterial evolutionary algorithm is applied to demonstrate the difference between solutions with altering degree of satisfaction of the original constrains. By presenting the whole set of solutions to the human decision-maker the cognitive biases encoded into the mathematical model can be corrected.