Short vocabulary points :
Epistemic ... :
General approaches don't work.
Model-checking, validity and proof-search can be hard. Like, NP, PSPACE, non-elementary hard or even undecidable. Particularly, validity of propositions in first-order logic is undecidable.
Our propositions about the world are more complex than what's described by first order logic. Making it impossible to prove validity of propositions in the general case. As such, trying to find a general logic to deal with the world (ie, critical thinking) is energy badly spent.
Specific approaches work.
This problem has been answered in fields relying on logic. For instance : model checking, type theory or not-statistical computational linguistics. The standard answer is finding specialized and efficiently computable logics.
However, not every field can afford a full formalization. At least, as humans studying the world, we can't. Epistemies can be seen as detailed informal efficient logics. They give us a particular way to study some particular thing, just like logics. They don't provide mathematical guarantees, but they still can offer guarantees.
Science faced that problem, as the study of world by humans. However, critical thinking wasn't enough. That's partly why we moved from philosophy to sciences. The Science solution was to subdivide the world into several overlapping-but-independently-experimentable parts.
Thus, rather than by its object of study, a science is defined by a combination of its object of study and its epistemy. This explains why 3 different articles studying logic can be discriminated by their science : to Philosophy, Math and Theoretical Computer Science.
Stopping redundancy.
Valuing critical thought led to a high amount of redundancy. Anyone can dump their ideas and have it judged by the community, provided a bit of critical thinking has been done. The core insight being that critical thinking should filter most of the bad ideas.
However, if the subject of the idea relies a bit on a non-epistemied technical field, obfuscating lack of consistency/thorough-thinking becomes very easy. As such, community time is spent finding obvious flaws in a reasoning when the author could do it alone provided there was an appropriate epistemy.
Epistemic effort.
As such, before suggesting new models, one should confront it to the standard epistemy of the fields the model belong to. That epistemy can be as simple as some sanity checks, eg : "Does this model lead to X, Y or Z contradiction ? If it does, it's a bad one. Else, it is a good one.". If there is no standard epistemy in the given field, working on one is critical.
I agree Raemon's post about using "epistemic effort" instead of "epistemic status". Following the previous line of thought, I think "epistemic status" should refer to an epistemic status (and the field relative to which it is defined) instead of the epistemic effort. I see 3 kinds of epistemic status, that could be refined :
1. Pre-epistemy : Thoughts meant to gather comments. Models trying to see if modelling a particular subject is worth it or works well.
2. Epistemy building : Defining the epistemy meta-assumptions. Defining the epistemy logic. Defining the epistemy facts (eg, Which sources are relevant in that field ? Which meta-facts are relevant in that field ?).
3. Post-epistemy : Once the epistemy is defined, anything benefiting the science's episteme. Facts, models, questioning the epistemy (might lead to forks, eg, math and computer science).
"Bayesian probabilities"
Initially, I thought that someone putting a probability in front of a belief had an objective meaning. I asked around for an epistemy, and I have been told that it was only a way to express more precisely a subjective feeling.
However, it looks like there might be a confusion between the map and the territory when I see things like bet-to-update. Because when I see "Bayesian rational agent", it feels like we should be supposed to be bayesian rational agents in the general case. (Which I think is an AGI-complete problem.)
Bayesian framework
Bayesian rules and its derivatives define the "proof rules" part of an agent's epistemy. But axioms are still required, a world, a way to gather facts and such. It also relies on meta-assumption for efficiency and relevancy. Bayesian rules are not enough to define an epistemy.
Therefore, not only I am strongly prejudiced against someone self-describing as a bayesianist because of the "I apply the same epistemy everywhere"-approach, but also because it isn't a proper epistemy.
There are better ways to say "I know the Bayes' rule, and how it might apply to real-life situation." than "I'm bayesianist".
Maybe "bayesianist" solely means "I update my beliefs based on evidence", but I think "open-minded" is the right word for that.
Not even wrong
Showing not-even-wrong-ness is possible in sciences with an epistemy. (Well, it's possible to show it to people who know that epistemy. Showing someone who don't know maths that his "informal" maths aren't even maths is hard.)
In other fields, we are subject to too much not-even-wrong-ness. I'd like to link some LW posts to exemplify my point, but I think it might violate the local BNBR policy.
