One of the main problems to solve in multi-robot systems is to select the best robot to execute each task (task allocation). Several ways to address this problem have been proposed in the literature. This paper focuses on one of them, the so-called response threshold methods. In a recent previous work, it was proved that the possibilistic Markov chains outperform the classical probabilistic using a celebrated possibility transition function. In this paper we use a new possibility transition function and we make several experiments in order to compare both, the new one and the tested before. The experiments show that the number of steps that a possibilistic Markov chain needs to converge does not depend on the response function used. This paper also emphasizes that these possibility transition functions are indistinguishably operators.