Model averaging is often presented as a more careful and uncertainty-aware alternative to choosing one model specification. It is supposed to reduce overconfidence and make analysis more robust. But what if all the models being averaged share the same blind spots from the start? 

In this episode, we break down why model averaging often overpromises, how shared structural weaknesses survive the averaging process, and why uncertainty cannot be handled simply by blending similar models. 

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