1.  Thanks for creating this post! 
2. I think it could be worth clarifying how you operationalize EA epistemics. In this comment, I mostly focus on epistemics at EA-related organizations and focus on "improving decision-making at organizations" as a concrete outcome of good epistemics. 
3. I think I can potentially provide value by adding anecdotal data points from my work on improving epistemics of EA related organizations. For context, I work at cFactual, supporting high-impact organizations and individuals during pivotal times. So far we have done 20+ projects partnering with 10+ EA adjacent organizations. 
   1. Note that there might be several sampling biases and selection effects, e.g., organizations who work with us are likely not representing all high-impact organizations.
   2. So please read it what it is: Mixed confidence thoughts based on weak anecdotal data which are based on doing projects on important decisions for almost 2 years. 
4. Overall, I agree with you that epistemics at EA orgs tend to be better than what I have seen while doing for-profit-driven consulting in the private, public and social sectors. 
   1. For example, following a simple decision document structure including: Epistemic status, RAPID, current best guess, main alternatives considered, best arguments for the best guess, best arguments against the best guess, key uncertainties and cruxes, most likely failure mode and things we would do if we had more time, is something I have never seen in the non-EA world. 
5. The services we list under "Regular management and leadership support for boards and executives" are gaps we see that often ultimately improve organizational decision-making.
   1. Note that clients pay us, hence, we are not listing things, which could be useful but don't have a business model (like writing a report on improving risk management by considering base rates and how risks link and compound). 
   2. I think many of the gaps, we are seeing, are more about getting the basics right in the first place and don't require sophisticated decision-making methods, e.g.,
      1. spending more time developing goals incl. OKRs, plans, theories of change, impact measurement and risk management 
         1. Quite often it is hard for leaders to spend time on the important things instead of the urgent and important things, e.g., more sophisticated risk management still seems neglected at some organizations even after the FTX fall-out
      2. improving executive- and organization-wide reflection, prioritization and planning rhythms 
      3. asking the right questions and doing the right, time-effective analysis at a level which is decision-relevant
      4. getting an outside view on important decisions and CEO performance from well-run boards, advisors and coaches
      5. improving the executive team structure and hiring the right people to spend more time on the topics above
   3. Overall, I think the highest variance on whether an organization has good epistemics can be explained by hiring the right people and the right people simply spending more time on the prioritized, important topics. I think there are various tweaks on culture (e.g., rewarding if someone changes someone's mind, introducing an obligation to dissent and Watch team backup), processes (e.g., having a structured and regular retro and prioritization session, making forecasts when launching a new project) as well as targeted upskilling (e.g., there are great existing calibration tools which could be included in the onboarding process) but the main thing seems to be something simple like having the right people, in the right roles spending their time on the things that matter most. 
      1. I think simply creating a structured menu of things organizations currently do to improve epistemics (aka a google doc) could be a cost-effective MVP for improving epistemics at organizations
6. To provide more concrete, anecdotal data on improving epistemics of key organizational decisions, the comments I leave most often when redteaming google docs of high impact orgs are roughly the following:
   1. What are the goals?
   2. Did you consider all alternatives? Are there some shades of grey between Option A and B? Did you also consider postponing the decisions?
   3. What is the most likely failure mode? 
   4. What are the main cruxes and uncertainties which would influence the outcome of the decision and how can we get data on this quickly? What would you do if you had more time?
   5. Part X doesn't seem consistent with part Y
7. To be very clear, 
   1. I also think that I am making many of these prioritization and reasoning mistakes myself! Once a month, I imagine providing advice to cFactual as an outsider and every time I shake my head due to obvious mistakes I am making. 
   2. I also think there is room to use more sophisticated methods like forecasting for strategy, impact measurement and risk management or other tools mentioned here and here

Three Potential Project Ideas:

Alternate Perspectives Fellowship:

A bunch of EA's explore different non-EA perspectives for X number of weeks then each participant writes up a blog post based on something that they learned or one place where they updated. There could also be a cohort post that contains a paragraph or two from each participant about their biggest updates.

It probably wouldn't make sense for this to scale massively. Instead, you'd want to try to recruit skilled communicators or decision-makers such that this course could be impactful even with only a small number of participants.

Rethinking EA Fellowship

Get a bunch of smart young EA's from different backgrounds into the same room for two weeks. In the first week, ask them to rethink EA/the EA community from the ground up taking into account how the situation has shifted since EA was founded.

During the second week, bring in a bunch of experienced EA's who can share their thoughts on why things are the way that they are. The young EA's can then revise their suggestions taking their feedback into account.

Alternate Positive Vision Competition

We had a criticism competition before. Unfortunately, only a small proportion of the criticism seemed valuable (no offense to anyone!) and too much criticism creates a negative social environment. So what I'd love to propose as an alternative would be a competition to craft an alternate positive vision.

I think this is a really important area, and it is great someone is thinking whether EAIF could expand more into this sort of area. 

To provide some thoughts of my own having explored working with a few EA entities through our consultancy (https://www.daymark-di.com/) that works to help organisations improve their decision making processes, along with discussions I've had with others on similar endeavours:

1. Funding is a key bottleneck, which isn't surpising. I think there is naturally an aversion to consultancy-type support in EA organisations, mostly driven by lack of funds to pay for it and partly because I think they are concerned how it'll look in progress reports if they spend money on consultants. 
   1. EAIF funding could make this easier, as it'll remove the entire (or a large part) of the cost above.

2. There does appear a fairly frequent assumption that EA organisations suffer less from poor epistemics and decision making practices, which my experience suggests is somewhat true but unfortunately not entirely. I want to repeat what Jona from cFactual commented below that there are lots of actions EA organisations take that are very positive, such as BOTECs, likely failure models, and using decision matrices. This should be praised as many organisations don't even do these. However, the simple existence of these is too often assumed to mean good analysis and judgment will naturally occur and the systems/processes that are needed to make them useful are often lacking. To be more concrete, as an example few BOTECs incorporate second order probability/confidence correctly (or it is conflated with first order probability) and they subsequently fail to properly account for the uncertainty of the calculation and the accurate comparisons that can be made between options. 

   It has been surprising to observe the difference between some EA organisations and non-EA institutions when it comes to interest in improving their epistemics/decision making. With large institutions (including governmental) being more receptive and proactive in trying to improve - often those institutions are mostly being constrained by their slow procurement processes as opposed appetite.

When it comes to future projects, my recommendations of those with highest value add would be:

1. Projects that help organisations improve their accountability, incentive, and prioritisation mechanisms. In particular helping to identify and implement internal processes that properly link workstreams/actions/decisions/judgments to the goals of the organisation and that role's objectives. This is something which is most useful to larger organisations (and especially those that make funding/granting decisions or recommendations), but can also be something which smaller organisations would benefit from.
2. Projects that help organisations improve their processes to more accurately and efficiently reason under uncertainty, namely assisting with defining the prediction/diagnostic they are trying to make a judgment on, identify the causal chain and predictors (and their relative importance) that underpins their judgment, and provide them with a framework to communicate their uncertainty and reasoning in a transparent and consistent way.
3. Projects that provide external scrutiny and advanced decision modelling capabilities. I think at a low level there are some relatively easy wins that can be gained from having an entity/service which red teams and provides external assessment of big decisions that organisations are looking at making (e.g. new requests for new funding). At a greater level there should be more advanced modelling (including using tools such as Bayesian models) which can provide transparent and updatable analysis which can expose conditional relationships that we can't properly account for in our minds.
   1. I think funding entities like EA Funds could utilise such an entity/entities to inform their grant making decisions (e.g. if such analysis was instrumental in the decision EA Funds made on a grant request, they'd pay half of the cost of the analysis).