Reddit reply in r/AcademicQuran: some presumptions of historicalcritical — February 17, 2025 · mdaubzx | Ali Shukri Amin

>The paper is commendable for initiating the conversation on the problem of reproducibility, but it is quite outdated and does not itself provide any evidence itself for the existence of a reproducibility problem. Of course, decades of social sciences work has suffered from reproducibility problems and there are now widespread and concerted efforts in the academy to determine which findings are reproducible and which are not.

>That being said, I see no connection between the social sciences reproducibility problem and the principle of uniformitarianism in the sciences (or its parallel principle from analogy in historiography).

The issue with reproducibility is a structural issue that hasn't been addressed although that paper is admittedly ancient. The problem stems from reliance on models thar assume stability in systems that are too complex to be accounted for in such a fashion. Your top level comment assumes there is a typical or an average that we can then analogize from. This is assuming that these matters follow a Gaussian distribution which is misleading and inaccurate. Social sciences and even history deal with non-ergodic systems. They don't convene to predictable averages overtime in any demonstrable fashion. They are described more accurately by models that take into account wild swings or extreme singular events, which makes uniformitarianism problematic. It isn't just that studies have a problem of reproducibility, these methods are too fragile to draw inferences from.

>A general and uniform prior experience around a phenomena provides gives us a strong prior for what we should expect to see the next time we interact with an instance of that phenomena. This is clearly a general principle of Bayesian statistics (whereas you're thinking in terms of frequentist statistics) that does not depend on any specific probability distribution like a Gaussian/normal distribution.

>By the way: randomness can be modelled with any distribution. You could use a normal distribution or you can use a gamma distribution etc. It depends on the phenomena you're studying. But nothing I said depends on "modelling randomness with a normal distribution" and I can't really tell how you got that out of my comment.

The problem with normal distributions is that you can't model some kind of randomness with it nor is that what I intended to claim. It's that real world phenomenon - especially pertaining to historical events - actually follow power-law distributions where there are extreme exceptions that are sparsely distributed. These are called fat tails and they dominate the course of history. These rare events have an outsize impact and a normal Gaussian distribution would fail to account for that even though they ARE the story. The kind of assumption of normality you're suggesting here is problematic because it underestimates extreme deviations.

We aren't just choosing a random model. We are talking about probability in history. I have noticed that you tend to appeal to what is "probable" or "statistical" when talking about likelihoods in the past. The problem is you're assuming Gaussian randomness when history is defined moreso by power-law randomness.

>It does **indicate** truth (esp. look up [consilience](https://en.wikipedia.org/wiki/Consilience)) and your stock trading analogy doesn't make sense to me. If there was an underlying theory of economics that uniformly predicted all movements in the stock market over the course of many decades and in different nations, we would all take that as extremely powerful evidence that this theory of economics is either true or captures most of the relevant truth content of whatever the true and complete theory of economics is.

The argument that empirical success "indicates" truth is flawed. A model's success can easily be survivorship bias. The fact that a theory has SO FAR consistently predicted outcomes does not necessarily mean it will continue to do so in the future, and in fact models of reality we KNOW are inaccurate still lead to consistently correct results within a certain framework (Newtonian physics for example). This doesn't mean they capture some deep structure of reality. You are conflating useful heuristics for truth. This is overconfidence and sidesteps the entire problem of induction.

In non-linear, complex systems (such as markets, economies, societies, human history as a whole), local observations and empirical consistency do not necessarily generalize on a grand scale or project backwards. That's why in science theories are tested - and even that leads us to efficacy not truth. We can't run backwards experiments on history so our conclusions are much more fraught.

I don't think any serious philosopher of science would argue that efficacy equals truth. There's just no evidence of that. Wrong theories can definitely lead to good results for a potentially infinite period of time but that doesn't mean their description of the world is true.

I don't see what consillience has to do with the point you're making.