This article seems really dumb (no Dunning-Kruger joke intended).

The two lines on the graph are basically linear (for the "actual performance" the quasi-linearity is obvious by the design, for the "estimated performance" it still means that even though dumber people over-estimate their performance, all group still think they do best, when they actually do best, in a relative linear way)

And when they do their simple model (we assume they just generated "real performance" from a gaussian, then added some gaussian noise for the "performance" and another gaussian noise for the "self-assessment") they still (obviously) got two linear graphs that crossed each other.

And then they conclude that this means there is no effect, because "the graphs are eerily similar" (whatever that means)

But obviously the simple model is going to make two lines cross (in particular if you use a min(100, max(0, actual_performance + noise)) since at each extreme, then min and max will tend to skew the line). To put it simply: someone really stupid will STILL not pretend that he's "negatively stupid".

The argument "I can make a simple model without using actual humans which shows some kind of bias that vaguely ressembles the result of a paper" doesn't mean that the actual paper is wrong...