 There is one aspect of validation that can sometimes be gauged at the level of modeling itself and with modeling tools alone. Where the target is some general phenomenon—opinion polarization or the emergence of communication, for example—a model which produces that phenomenon within only a tiny range of parameters should be suspicious. Our estimate of the parameters actually in play in the actual phenomenon may be merely intuitive or extremely rough, and the real phenomenon may be ubiquitous in a wide range of settings. In such a case, it would seem prima facie unlikely that a model which produced a parallel effect within only a tiny window of parameters could be capturing the general mechanism of a general phenomenon. In such cases robustness testing is called for, a test for one aspect of validation that can still be performed on the computer. To what extent do conclusions drawn from the modeling effect hold up under a range of parameter variations?