The selection of a model should be based on the nature of the studies and our goals. The fixed effect model makes sense if (a) there is reason to believe that all the studies are functionally identical, and (b) our goal is to compute the common effect size, which would then be generalized to other examples of this same population.
By contrast, the random effects model assumes that the studies were drawn from populations that differ from each other in ways that could impact on the treatment effect. For example, the intensity of the intervention or the age of the subjects may have varied from one study to the next. It follows that the effect size will vary from one study to the next for two reasons. The first is random error within studies, as in the fixed effect model. The second is true variation in effect size from one study to the next. Therefore, the random effects model is more easily justified than the fixed effect model.
Further reading: An Introduction to Meta-analysis: Fixed versus Random effects models.