Recent theoretical work helped explain certain optimization-related pathologies in cooperative coevolutionary algorithms (CCEAs). Such explanations have led to adopting specific and constructive strategies for improving CCEA optimization performance by biasing the algorithm toward ideal collaboration. This paper investigates how sensitivity to the degree of bias (set in advance) is affected by certain algorithmic and problem properties. We discover that the previous static biasing approach is quite sensitive to a number of problem properties, and we propose a stochastic alternative which alleviates this problem. We believe that finding appropriate biasing rates is more feasible with this new biasing technique.