I'm not sure how to respond to your point about Bem and Baumeister's work since those cases are the most obvious culprits for being vulnerable to scientific weakness/malpractice (in particular, because they came before the time of open access science, pre-registration, and sample sizes calculated from power analyses).

I also don't get your point about TESS. It seems obvious that there are many benefits for choosing the repository of TESS studies from the authors' perspective. Namely, it conveniently allows for a consistent analytic approach since many important things are held constant between studies such as 1) the studies have the exact same sample demographics (which prevents accidental heterogeneity in results due to differences in participant demographics) and 2) the way in which demographic variables are measured is standardized so that the only difference between survey datasets is the specific experiment at hand (this is crucial because the way in which demographic variables are measured varies can affect the interpretation of results). This is apart from the more obvious benefits that the TESS studies cover a wide range of social science fields (like political science, sociology, psychology, communication, etc., allowing for the testing of robustness in GPT predictions across multiple fields) and all of the studies are well-powered nationally representative probability samples.

Re: your point about experts being equal to random people in predicting results of studies, that's simply not true. The current evidence on this shows that, most of the time, experts are better than laypeople when it comes to predicting the results of experiments. For example, this thorough study (https://www.nber.org/system/files/working_papers/w22566/w225...) finds that the average of expert predictions outperforms the average of laypeople predictions. One thing I will concede here though is that, despite social scientists being superior at predicting the results of lab-based experiments, there seems to be growing evidence that social scientists are not particularly better than laypeople at predicting domain-relevant societal change in the real world (e.g., clinical psychologists predicting trends in loneliness) [https://www.cell.com/trends/cognitive-sciences/abstract/S136... ; full-text pdf here: https://www.researchgate.net/publication/374753713_When_expe...]. Nonetheless, your point about there being no difference in the predictive capabilities of experts vs. laypeople (which you raise multiple times) is just not supported by any evidence since, especially in the case of the GPT study we're discussing, most of the analyses focus on predicting survey experiments that are run by social science labs.

Also, based on what the paper is suggesting, the authors don't seem to be suggesting that these are "replications" of the original work. Rather, GPT4 is able to simulate the results of these experiments like true participants. To fully replicate the work, you'd need to do a lot more (in particular, you'd want to do 'conceptual replications' wherein you the underlying causal model is validated but now with different stimuli/questions).

Finally, to address the previous discussion about the authors finding that GPT4 seems to be comparable to human forecasters in predicting the results of social science experiments, let's dig deeper into this. In the paper, but specifically in the supplemental material, the authors note that they "designed the forecasting study with the goal of giving forecasters the best possible chance to make accurate predictions." The way they do this is by showing laypeople the various conditions of the experiment and have the participants predict where the average response for a given dependent variable would be within each of those conditions. This is very different from how GPT4 predicts the results of experiments in the study. Specifically, they prompt GPT to be a respondent and do this iteratively (feeding it different demographic info each time). The result of this is essentially the same raw data that you would get from actually running the experiment. In light of this, it's clear that this is a very conservative way of testing how much better GPT is than humans at predicting results and they still find comparable performance. All that said, what's so nice about GPT being able to predict social science results just as well as (or perhaps better than) humans? Well, it's much cheaper (and efficient) to run thousands of GPT queries than is to recruit thousands of human participants!