Social collaboration has been shown to facilitate problemsolving activity in diverse sets of environments. Nevertheless, if not well designed, social and human computation systems may achieve results only similar to those of a single human subject performing a task. This scenario reflects a need for better understanding of the performance issues of human problem-solving social networks. Firstly, we propose a model for simulating social problem-solving. We then carry out several simulations with artificial agents supported by results of experiments carried out with human subjects, in order to analyse which parameters influence the performance of collaborative problem-solving social networks. We analyse the strategies humans follow when solving a problem, comparing them with alternative ones, and identify the consequences of the employed strategies in the collective performance of the social network. Our results also indicate that copying and guessing are beneficial to the performance of the social networks. We then propose mechanisms that can improve collaborative problem-solving. Finally, we show that our results lead to a methodology for the design of efficient problem-solving systems that can be applied to several kinds of collaborative social systems.