Computer models are used by ecologists for studying a broad range of research questions, from long-term forest dynamics to the functional traits that theoretically give one species an advantage over others. Despite their increasing popularity, these models have been criticized for simulating complex biological phenomena, involving numerous biotic and abiotic variables, using seemingly overly simplistic computational approaches. In this article, we review the usefulness and limitations of spatially explicit individual-based models for forested ecosystems by focusing on the attributes of a recent model, called SERA (for spatially explicit reiterative algorithm), that employs seven allometric formulas and a few physical principles. Despite its simplicity, SERA successfully predicts forest self-assembly and dynamics. It also predicts phenomena that are not part of its mathematical structure. Because of this, SERA simulations can be used to explore the consequences of experimentally manipulating plant communities in ways that cannot be achieved using real communities.