Mark Xu's Shortform

Better control solutions make AI more economically useful, which speeds up the AI race and makes it even harder to do an AI pause.

When we have controlled unaligned AIs doing economically useful work, they probably won't be very useful for solving alignment. Alignment will still be philosophically confusing, and it will be hard to trust the alignment work done by such AIs. Such AIs can help solve some parts of alignment problems, parts that are easy to verify, but alignment as a whole will still be bottle-necked on philosophically confusing, hard to verify parts.

Such AIs will probably be used to solve control problems for more powerful AIs, so the basic situation will continue and just become more fragile, with humans trying to control increasingly intelligent unaligned AIs. This seems unlikely to turn out well. They may also persuade some of us to trust their alignment work, even though we really shouldn't.

So to go down this road is to bet that alignment has no philosophically confusing or hard to verify parts. I see some people saying this explicitly in the comments here, but why do they think that? How do they know? (I'm afraid that some people just don't feel philosophically confused about much of anything, and will push forward on that basis.) But you do seem to worry about philosophical problems, which makes me confused about the position you take here.

BTW I have similar objections [LW(p) · GW(p)] to working on relatively easy forms of (i.e., unscalable) alignment solutions, and using the resulting aligned AIs to solve alignment for more powerful AIs. But at least there, one might gain some insights into the harder alignment problems from working on the easy problems, potentially producing some useful strategic information or making it easier to verify future proposed alignment solutions. So while I don't think that's a good plan, this plan seems even worse.

I really think if you want to tell a story of AI Control work being good (especially compared to working on preventing AGI from being built in the first place), the important and difficult part is figuring out how to actually use these more powerful AI systems to either achieve some kind of global moratorium, or make unprecedented progress on the hard parts of the AI Alignment problem. 

When I see most people start thinking about control, I rarely see them interface with either of these two things, and honestly, I mostly see them come up with cool additional commercial applications of AI, which I think will mostly make the present situation worse. To be clear, I think thinking about how to use AI systems for global coordination or making progress on the hard parts of the AI Alignment problem is tractable, but I haven't seen much work on it, and I am worried it is not the default thing that people will work on when they start thinking about control.

I think it is unlikely for control work to buy humanity much time until someone builds a very powerful unaligned AI system, at least at our present levels of coordination tools. I think controlled AI systems might help with coordination here, but I do think someone needs to do the actual hard work of thinking about how it might.

(I go into this and various related things in my dialogue with Ryan on control [LW · GW])

This topic is important enough that you could consider making a full post.

My belief is that this would improve reach, and also make it easier for people to reference your arguments. 

Consider, you believe there is a 45% chance that alignment researchers would be better suited pivoting to control research. I do not suspect a quick take will reach anywhere close to that number, and has a low chance of catalysing dramatic, institutional level change. 

quick analogy: if the sum of a bunch of numbers is large, there doesn’t need to be any individual number that is large; similarly, if the consequences of a sequence of actions results in a large change, no individual action needs to be “pivotal”

This feels like a pretty central cruxy point - and not just for the relevance of the pivotal act framing specifically. I think it's underlying a whole difference of worldview or problem-solving approach.

A couple other points in a similar direction:

• A thing I noticed in our discussion on the model delta with Christiano post [LW(p) · GW(p)]: your criterion for useful AI safety work seems to be roughly "this will contribute some marginal value" as opposed to "this will address a bottleneck".
• Right at the top of this thread, you say: "I think Redwood Research’s recent work on AI control really 'hits it out of the park', and they have identified a tractable and neglected intervention that can make AI go a lot better". Note what's conspicuously missing there: tractable and neglected, but you don't claim importance.

I would say that your mindset, when approaching AI safety, seems to be an epsilon fallacy.

Sure, in principle a sum of numbers can be large without any individual number being large. In practice, the 80/20 rule is a thing, and everything has bottlenecks all the time. If work is not addressing a bottleneck, then it's approximately useless.

(Somewhat more precisely: if marginal work is not addressing something which is a bottleneck on current margins, then it's approximately useless.)

Of importance, tractability and neglectedness, importance is the most important. In practice, it is usually better to have a thousand people trying to solve a major bottleneck each with low chance of success, than a thousand people making tractable progress on some neglected issue which is not a bottleneck.

The intelligence explosion might happen with less-fully-AGI AIs, who will also be doing some alignment work on the side. It’s important for them to not escape and do other bad stuff until they’ve solve alignment. We can give ourselves more time to use smart AIs to help with alignment if we have better AI control.

Well, this would be the lone crux. The rest of the stuff you wrote is about non-exploding AI, right? And is therefore irrelevant to the thing about everyone dying, except insofar as controlled non-exploding AI can help prevent uncontrolled exploding AI from killing everyone?

I directionally agree with this (and think it's good to write about this more, strongly upvoted!)

For clarity, I would distinguish between two control-related ideas more explicitly when talking about how much work should go into what area:

1. "ensuring that if the AIs are not aligned [...], then you are still OK" (which I think is the main meaning of "AI control")
2. Making ~worst-case assumptions about things like neural representations or inductive biases (which in practice means you likely rely on black-box methods, as in Redwood's existing work on control).

I think 2. is arguably the most promising strategy for 1., but I've occasionally noticed myself conflating them more than I should.

1. gives you the naive 50/50 equilibrium, i.e. 50% of people should naively work on this broad notion of control. But I think other reasons in favor apply more strongly to 2. (e.g. the tractability arguments are significantly weaker for model internals-based approaches to 1.)

I also think (non-confidently) that 2. is what's really very different from most existing research. For control in the first, broad sense, some research seems less clearly on either the control or alignment side.

But I do agree that safety-motivated researchers should evaluate approaches from a control perspective (in the broad sense) more on the margin. And I also really like the narrower black-box approach to control!

This seems to presume that you can divide up research topics into "alignment" vs "control" but this seems wrong to me. E.g. my categorization would be something like:

• Clearly alignment: debate theory, certain flavors of process supervision
• Clearly control: removing affordances (e.g. "don't connect the model to the Internet")
• Could be either one: interpretability, critique models (in control this is called "untrusted monitoring"), most conceptions of ELK, generating inputs on which models behave badly, anomaly detection, capability evaluations, faithful chain of thought, ...

Redwood (I think Buck?) sometimes talks about how labs should have the A-team on control and the B-team on alignment, and I have the same complaint about that claim. It doesn't make much sense for research, most of which helps with both. It does make sense as a distinction for "what plan will you implement in practice" -- but labs have said very little publicly about that.

Other things that characterize work done under the name of "control" so far are (1) it tries to be very concrete about its threat models, to a greater degree than most other work in AI safety, and (2) it tries to do assurance, taking a very worst case approach. Maybe you're saying that people should do those things more, but this seems way more contentious and I'd probably just straightforwardly disagree with the strength of your recommendation (though probably not its direction).

Nitpick: I would also quibble with your definitions; under your definitions, control seems like a subset of alignment (the one exception if you notice the model is scheming and then simply stop using AI). I think you really have to define alignment as models reliably doing what you want independent of the surrounding context, or talk about "trying to do what you want" (which only makes sense when applied to models, so has similar upshots).

Tbc I like control and think more effort should be put into it; I just disagree with the strength of the recommendation here.

I agree with bullet points 1, 2, 3, 6 and 7, partially agree with bullet point 5, and disagree with bullet point 4.

Thus, I agree with the central claim here:

I think Redwood Research’s recent work on AI control really “hits it out of the park”, and they have identified a tractable and neglected intervention that can make AI go a lot better. Obviously we should shift labor until the marginal unit of research in either area decreases P(doom) by the same amount. I think that implies lots of alignment researchers should shift to AI control type work, and would naively guess that the equilibrium is close to 50/50 across people who are reading this post. That means if you’re working on alignment and reading this, I think there’s probably a ~45% chance it would be better for your values if you instead were working on AI control!

For more of my analysis on the bullet points, read the rest of the comment.

For bullet point 1, I basically agree with this, mostly due to not favoring binary assumptions and instead prefer continuous quantity reasoning, which tends to both be a better match for the IRL world, and also continuous quantity reasoning gives you more information than binary outcomes.

I really like bullet point 2, and also think that even in a scenario where it's easy to prevent defection, you should still have controls that make defecting employees have much less reward and much more punishment for subversive actions.

I deeply agree with point 3, and I'd frame AI control in one of 2 ways:

1. As a replacement for the pivotal act concept.
2. As a pivotal act that doesn't require destruction or death, and doesn't require you to overthrow nations in your quest.

A nitpick: AI labor will be the huge majority of alignment progress in every stage, not just the early stage.

I think one big reason the pivotal act frame dominated a lot of discussions is the assumption that we would get a pure software singularity which would FOOM in several weeks, but reality is shaping up to not be a pure software-singularity, since physical stuff like robotics and data centers still matters.

There's a reason why every hyperscaler is trying to get large amounts of power and datacenter compute contracts, because they realize that the singularity is bottlenecked currently on power and to a lesser extent compute.

I disagree with 4, but that's due to my views on alignment, which tend to view it as a significantly easier problem than the median LWer does, and in particular I view essentially 0 need for philosophical deconfusion to make the future go well.

I agree that AI control enhances alignment arguments universally, and provides more compelling stories. I disagree with the assumption that all alignment plans depend on dubious philosophical assumptions about the nature of cognition/SGD.

I definitely agree with bullet point 6 that superhuman savant AI could well play a big part in the intelligence explosion, and I believe this most for formal math theorem provers/AI coders.

Agree with bullet point 7, and think it would definitely be helpful if people focused more on "how can this be helpful even if it fails to produce an aligned AI?"

I would suggest 50% of researchers working on a broader definition of control: including "control", technical governance work and technical outreach (scary demos, model organisms of misalignment). 

I'm in the process of trying to build an org [LW(p) · GW(p)] focused on "automated/augmented alignment research." As part of that, I've been thinking about which alignment research agendas could be investigated in order to make automated alignment safer and trustworthy. And so, I've been thinking of doing internal research on AI control/security and using that research internally to build parts of the system I intend to build. I figured this would be a useful test case for applying the AI control agenda and iterating on issues we face in implementation, and then sharing those insights with the wider community.

Would love to talk to anyone who has thoughts on this or who would introduce me to someone who would fund this kind of work.

I don't see a significant difference in your distinction between alignment and control. If you say alignment is about doing what you want (which I strongly disagree with in its generality, e.g. when someone might want to murder or torture people or otherwise act unethically), that obviously includes your wanting to "be OK" when the AI didn't do exactly what you want. Alignment comes in degrees, and you merely seem to equate control with non-perfect alignment and alignment with perfect alignment. Or I might be misunderstanding what you have in mind.

I disagree with this take. A.I. control will only be important in a multipolar situation in which no single A.I. system can create a gray goo catastrophe etc. But if such pivotal acts are impossible and no singular A.I. takes control, but instead many A.I.’s are competing, than some groups will develop better or worse control for economic reasons and it won’t affect existential risk much to work on it now. I don’t think I can see a situation where control matters - only a few players have A.G.I. for a very long time and none escape or are open sourced but also none gain a decisive advantage?

This is a good and important point. I don't have a strong opinion on whether you're right, but one counterpoint: AI companies are already well-incentivized to figure out how to control AI, because (as Wei Dai said) controllable AI is more economically useful. It makes more sense for nonprofits / independent researchers to do work that AI companies wouldn't do otherwise.

This post raises an important perspective on the practicalities of AI Control versus Alignment. Given the potential for AI to function productively even when not fully aligned, do you believe that current AI control methods are scalable enough to handle future AGI systems? Additionally, what would be the main challenges in ensuring that AI control strategies are robust in highly dynamic or emergency scenarios?

ANT has a stronger safety culture, and so it is a more pleasant experience to work at ANT for the average safety researcher. This suggests that there might be a systematic bias towards ANT that pulls away from the "optimal allocation".

I think this depends on whether you think AI safety at a lab is more of an O-ring process or a swiss-cheese process. Also, if you think it's more of an O-ring process, you might be generally less excited about working at a scaling lab.

Some possible counterpoints:

• Centralization might actually be good if you believe there are compounding returns to having lots of really strong safety researchers in one spot working together, e.g. in terms of having other really good people to work with, learn from, and give you feedback.
• My guess would be that Anthropic resources its safety teams substantially more than GDM in terms of e.g. compute per researcher (though I'm not positive of this).
• I think the object-level research productivity concerns probably dominate, but if you're thinking about influence instead, it's still not clear to me that GDM is better. GDM is a much larger, more bureaucratic organization, which makes it a lot harder to influence. So influencing Anthropic might just be much more tractable.

I think two major cruxes for me here are:

• is it actually tractable to affect Deepmind's culture and organizational decisionmaking
• how close to the threshold is Anthropic for having a good enough safety culture?

My current best guess is that Anthropic is still under the threshold for good enough safety culture (despite seeming better than I expected in a number of ways), and meanwhile that Deepmind is just too intractably far gone. 

I think people should be hesitant to work at any scaling lab, but, I think Anthropic might be possible to make "the one actually good scaling lab", and I don't currently expect that to be tractable at Deepmind and I think "having at least one" seems good for the world (although it's a bit hard for me to articulate why at the moment)

I am interested in hearing details about Deepmind that anyone thinks should change my mind about this.

This viewpoint is based on having spent at least 10s of hours trying to learn and about influence both org's culture, at various times.

In both cases, I don't get the sense that people at the orgs really have a visceral sense that "decisionmaking processes can be fake", I think they will be fake by default and the org is better modeled as following general incentives, and DeepMind has too many moving people and moving parts at a low enough density that it doesn't seem possible to fix. For me to change my mind about this, I would need to someone there to look me in the eye and explain that they do have a visceral sense of how organizational decisionmaking processes can be fake, and why they nonetheless think DeepMind is tractable to fix. I assume it's hard for @Rohin Shah [LW · GW] and @Neel Nanda [LW · GW] can't really say anything publicly that's capable of changing my mind for various confidentiality and political reasons, but, like, that's my crux.

(conving me in more general terms "Ray, you're too pessimistic about org culture" would hypothetically somehow work, but, you have a lot of work to do given how thoroughly those pessimistic predictions came true about OpenAi)

I think Anthropic also has this problem, but the threshold of almost-aligned-leadership and actually-pretty-aligned people that it feels at least possible to me for the to fix it. The main things that would persuade me that they are over the critical threshold is if they publicly spent social capital on clearly spelling out why the x-risk problem is hard, and made explicit plans to not merely pause for a bit when they hit an RSP threshold, but (at least in some circumstances) advocate strongly for global government shutdown for like 20+ years.

I largely agree with this take & also think that people often aren't aware of some of GDM's bright spots from a safety perspective. My guess is that most people overestimate the degree to which ANT>GDM from a safety perspective.

For example, I think GDM has been thinking more about international coordination than ANT. Demis has said [LW · GW]that he supports a "CERN for AI" model, and GDM's governance team (led by Allan Dafoe) has written a few pieces about international coordination proposals.

ANT has said very little about international coordination. It's much harder to get a sense of where ANT's policy team is at. My guess is that they are less enthusiastic about international coordination relative to GDM and more enthusiastic about things like RSPs, safety cases, and letting scaling labs continue unless/until there is clearer empirical evidence of loss of control risks. 

I also think GDM deserves some praise for engaging publicly with arguments about AGI ruin [LW · GW] and threat models [? · GW].

(On the other hand, GDM is ultimately controlled by Google, which makes it unclear how important Demis's opinions or Allan's work will be. Also, my impression is that Google was neutral or against SB1047, whereas ANT eventually said that the benefits outweighed the costs.)

Great post. I'm on GDM's new AI safety and alignment team in the Bay Area and hope readers will consider joining us!

I would advise people to think hard about how joining a scaling lab might inhibit their future careers by e.g. creating a perception they are “corrupted”

What evidence is there that working at a scaling lab risks creating a "corrupted" perception? When I try thinking of examples, the people that come to my mind seem to have quite successfully transitioned from working at a scaling lab to doing nonprofit / government work. For example:

• Paul Christiano went from OpenAI to the nonprofit Alignment Research Center (ARC) to head of AI safety at the US AI Safety Institute.
• Geoffrey Irving worked at Google Brain, OpenAI, and Google DeepMind. Geoffrey is now Chief Scientist at the UK AI Safety Institute.
• Beth Barnes worked at DeepMind and OpenAI and is now founder and head of research at Model Evaluation and Threat Research (METR).

> think hard about how joining a scaling lab might inhibit their future careers by e.g. creating a perception they are “corrupted”

Does this mean something like:

1. People who join scaling labs can have their values drift, and future safety employers will suspect by-default that ex-scaling lab staff have had their values drift, or
 

2. If there is a non-existential AGI disaster, scaling lab staff will be looked down upon

or something else entirely?

The high level claim seems pretty true to me. Come to the GDM alignment team, it's great over here! It seems quite important to me that all AGI labs have good safety teams

Thanks for writing the post!

Alternative taxonomy:

• rationalists belonging to Eliezer
• cryopreserved rationalists
• rationalists trained by CFAR
• aspiring rationalists
• rationalists working for MIRI
• legendary rationalists
• metarationalists
• those commenting on this taxonomy
• those that tweet as if they were mad
• Bayesians
• et cetera
• Zvi
• those that from afar look like paperclips

:)

I think there is a community of discord rationalists and tumblr rationalists.

I am a google doc rationalist! (Or I would like to be. Google docs are great.)

What are you using the word "rationalist" to mean? If you just mean "members of any subculture bearing some line of memetic descent from early Less Wrong" (which I don't think deserves the pretentious term rationalist, but putting that aside), why is "communication platform" a useful way to chop up that social grouping? A lot of the same people use Less Wrong and Facebook and Twitter and Discord and Google Docs, and a lot of the people who use the same platform wouldn't be in the same cluster if you were to analyze the graph of what actual conversations people are using these platforms to have.

I'm not sure what to recommend, but a few words that come to mind that might be relevant and help you spark an idea:

• resilient
• robust
• reliable
• antifragile
• high availability
• self healing
• overdetermined

Is the victory bit important in the quotation?

If it's not about the victory/winning, and rather about the path/journey ....

A first draft that springs to mind as I type:

The key to rationality.... is not to chose the label, but to choose to take every opportunity to improve/update your thinking.

(Can't ... stop ... myself ... from commenting: From what I've observed too much ego gets in the way of rational thinking sometimes.)

Why not something along the lines of "your rationality is measured as much by your worst case performance as your average case"?

It seems to me that during the quarantine I eat less than usual; either I am deluding myself, or it is a combination of having less physical activity (such as walking to/from job/lunch/shops/playground), being able to eat whenever I want (so there is no pressure to "eat a lot now, because the next opportunity will be 7 hours later"), making less superstimulating food (using less sugar and salt), and having other ways to get some hedons (e.g. taking a nap). Sometimes I cook a soup, and that's most of my daily food.

And soups are really cheap. You take $1-2 worth of ingredients, cook them in water, add little salt and spices; optionally eat with bread. Bread is cheap, salt is cheap, most spices are cheap (per portion), potatoes, broccoli, carrot, onion, and beans are cheap. Most of these things are like $1 per 1kg.

Okay, soups are not super healthy; cooking destroys vitamins. You should also get some fresh fruits and vegetables. Apples, tomatoes, cucumbers are $1-2 per 1 kg. You should definitely be able to eat healthy food for less than $5 a day.

What is expensive? Chocolate and other sweets, cheese, berries, nuts; I probably forgot something. You shouldn't eat sweets; and you can afford the other things now and then even under $5 a day on average. (It is not an optimal diet; some people recommend eating berries and nuts every day. But still healthier than many people eat, including those who spend more money on food.)

.

On the other hand, we usually spend more than $5 per person per day, even during the quarantine. We spend a lot on sweets and cheese. The tastier ones are even more expensive than the basic ones, which are already more expensive than the actually useful food. Instant gratification -- it's addictive! The more stress I have during the day, the more I need something that will improve my mood instantly, even if it's only for a moment.

Poor people probably have more stress, and thus less willpower to resists things full of sugar and salt. (Also alcohol and tobacco. Okay, the last one is technically not food, but still comes from the same budget.)

Very poor people, e.g. the homeless, don't have the place to cook. So many cheapest things are ironically out of their reach. Not having a fridge is also a problem.

Then, I assume many poor people don't have the good skills and habits. Some of them don't have the necessary IQ, some have mental problems, some had shitty upbringing.

There are a few things to keep in mind:

1) The claim that 40 million Americans "deal with hunger" is, um, questionable. Their citation leads to feedingamerica.org, which cites USDA's Household Food Security in the United States report (https://www.ers.usda.gov/webdocs/publications/94849/err-270.pdf?v=963.1). The methodology used is an 11-question survey (18 for households with children), where answering 3 questions in the affirmative marks you as low food security. The questions asked are (naturally) subjective. Even better, the first question is this: “We worried whether our food would run out before we got money to buy more.” Was that often, sometimes, or never true for you in the last 12 months? That's an a real concern to have, but it is not what people are talking about when they say "dealing with hunger". You can be running on a shoestring budget and often worry about whether you'll have enough money for food without ever actually not having enough money for food.

2) A significant percentage of the population has non-trivial issues with executive function. Also, most of the population isn't familiar with "best practices" (in terms of effective life strategies, basic finances, etc). Most people simply don't think about things like this systematically, which is how you get the phenomenon of ~50% of the population not being able to cover a $400 emergency (or whatever those numbers are, they're pretty close). This would be less of an issue if those cultural norms were inherited, but you can't teach something you don't know, and apparently we don't teach Home Economics anymore (not that it'd be sufficient, but it would be better than nothing). This is a subject that deserves a much more in-depth treatment, but I think as a high-level claim this is both close enough to true and sufficient as a cause for what we might observe here. Making an infographic with a rotating course of 10 cheap, easy-to-prepare, relatively healthy, and relatively tasty meals is a great idea, but it'll only be useful to the sorts of people who already know what "meal prep" means. You might catch some stragglers on the margin, but not a lot.

3) The upfront costs are less trivial than they appear if you don't inherit any of the larger items, and remember, 50% of the population can't cover a mid-3-figure emergency. "Basic kitchen equipment" can be had for under $100, but "basic kitchen equipment" doesn't necessarily set you up to prepare food in a "meal prep" kind of way.

It seems like the bottom 20% of the US spends $2216 per year per income earner or ~$6 per day on food. Given that children themselves don't have an income they might spend less then $5 per person for food per day.

People can drown in a river that's on average 1m deep.

I found a reference to "value handshakes" here [LW(p) · GW(p)]:

Your AI doesn't figure out how to do a reasonable "values handshake" with a competitor (where two agents agree to both pursue some appropriate compromise values in order to be Pareto efficient)...

I think it refers to something like this: Imagine that a superintelligent human-friendly AI meets a superintelligent paperclip maximizer, and they both realize their powers are approximately balanced. What should they do?

For humans, "let's fight, and to the victor go the spoils" is the intuitive answer, but the superintelligences can possibly do better. If they fight, they have a 50% chance of achieving nothing, and a 50% chance of winning the universe... minus whatever was sacrificed to Moloch, which could possibly be a lot. If they split the universe to halves, and find out a way how to trust each other, that is better that war. But there is a possibility of even better solution, when both of them would agree on acting as if they were a single superintelligence that values both humans and paperclips equally.

The cooperative solution can be better than a 50% split of the universe, because you could build paperclip factories on places humans care less about, such as uninhabitable planets; or perhaps you could find a way how to introduce paperclips to human environment without reducing the human quality of life. For example, would you mind using paperclips to reinforce the walls of your house? Would you mind if almost all materials used to build stuff for humans contained little paperclips inside? Would you mind living in a simulation implemented on paperclip-shaped circuits? So maybe at the end, humans could get like 70% of the potential utility of the universe, while 70% of potential material would be converted to paperclips.

There are a bunch of explanations of logarithm as length on Arbital.

Example: "when did <person> die?" If I was alive when <person> died, there's a higher chance of me hearing about their death. Thus not having heard of <person> is evidence that they died some time ago.

Technically, it could also be evidence that you are dead, but your ghost cannot move to afterlife, probably because it is too attached to scoring internet points (a fate that awaits many of us, I am afraid).

(epistemic status: just kidding)

Sure. Also, current employees are dis-incented from being truthful about the bad parts. But you're not applying statistics to the results, so that's not terribly important. Such interviews provide limited evidence about goodness of the company. They provide decent evidence about the potential coworkers you're interviewing.

Generally, when you're interviewing employees about a company about whether the company is any good, you're trying to decide whether to work there yourself. And you're evaluating whether any of them seem competent and interesting enough that you can tolerate being near them for any length of time.

update the CDC advises testing for the flu and there's a lot of medical things that cause "flu-like" symptoms. Turns out that "flu-like" symptoms is basically "immune system doing things", which is going to happen with most things your body doesn't like.

Not everybody who has a cold has influenza (the flu) or COVID-19. There are many different viruses that cause influenza-like-illnesses.

Low is relativ. 2% of coindience is still a bunch.

Decision theoretic uncertainty seems easier to deal with, because you’re trying to maximise expected value in each case, just changing what you condition on in calculating the expectation. So you can just take the overall expectation given your different credences in the decision theories