Why is it that you present EEA as an either/or, valid/invalid issue that makes twin studies valid or invalid? I’ve read the EEA criticisms of twin studies, including Burt and Simons that only seems to reference you, but I’ve seen nothing in them that actually invalidates twin studies. What these criticism do is put error bars on the estimates of genetic, shared environment, and unshared environment effects. That is, the degree to which the numerical estimates output from the model change due to the degree of inaccuracy of any of the inputs, including EEA, is a matter for error propagation studies. These studies clearly show the output estimates are relatively insensitive to deviations from the EEA. The error propagation of EEA has also been studied for decades and the minimal effects on the outcomes has been known, e.g., as outline by Barnes et al. Furthermore, these criticisms via EEA only affect the model that provides the numerical estimates of percentages of genetic and environmental (shared and unshared) components; it does not at all affect the science of behaviours as resulting from genes, nor replaces that with any sort of environmental explanation for the data. For example, the EEA does not affect the high correlation of behaviours with gene-relatedness nor low correlation with shared environment. With or without the numerical model you still have the problem of explaining such data. That is, given the results of behavioural and personality tests, you would generally not be able to tell much better than guessing which MZ twins were raised in the same household and which were not, which DZ twins were raised in the same household and which were not, and which unrelated peers were raised in the same household (adoptees) and which were not, but you would be able to tell which sets of results came from MZ twins (highly correlated), DZ twins (~half as correlates), and unrelated (not correlated). The explanation would also have cover why these correlations between pairs of people also correlate with their genetic relatedness. To reiterate, none of that data relies on the EEA. The EEA only shows up in the model for estimating percentage influences, which again is quite robust to EEA deviation. So if you have some references that justify both your rejection of the model (despite error propagation studies showing robustness) and your rejection of behavioural genetic data in general, please provide them. That would truly be scientifically important if such science exists to reject these things, yet all you refer to is the EEA assumption which doesn’t reject either the model or the underlying data. It appears to me, reviewing all of your work, you are beating a dead horse. The science is clear, but you seem stuck on an either/or mentality of either a perfect model or else it is zero value, which of course is not valid reasoning.