Cooperative coevolutionary algorithms (CCEAs) have been applied to many optimization problems with varied success. Recent empirical studies have shown that choices surrounding methods of collaboration may have a strong impact on the success of the algorithm. Moreover, certain properties of the problem landscape, such as variable interaction, greatly influence how these choices should be made. A more general view of variable interaction is one that considers epistatic linkages which span population boundaries. Such linkages can be caused by the decomposition of the actual problem, as well as by CCEA representation decisions regarding population structure. We posit that it is the way in which represented problem components interact, and not necessarily the existence of cross-population epistatic linkages that impacts these decisions. In order to explore this issue, we identify two different kinds of representational bias with respect to the population structure of the algorithm, decompositional bias and linkage bias. We provide analysis and constructive examples which help illustrate that even when the algorithm’s representation is poorly suited for the problem, the choice of how best to select collaborators can be unaffected.