Devices and time to fall asleep: a small self-experiment

I did a small self-experiment on the question "Does the use of devices (phone, laptop) in the evening affect the time taken to fall asleep?".

Setup

On each day during the experiment I went to sleep at 23:00. 

At 21:30 I randomized what I'll do at 21:30-22:45. Each of the following three options was equally likely:

• Read a physical book
• Read a book on my phone
• Read a book on my laptop

At 22:45-23:00 I brushed my teeth etc. and did not use devices at this time.

Time taken to fall asleep was measured by a smart watch. (I have not selected it for being good to measure sleep, though.) I had blue light filters on my phone and laptop.

Results

I ran the experiment for n = 17 days (the days were not consecutive, but all took place in a consecutive ~month).

I ended up having 6 days for "phys. book", 6 days for "book on phone" and 5 days for "book on laptop".

On one experiment day (when I read a physical book), my watch reported me as falling asleep at 21:31. I discarded this as a measuring error.

For the resulting 16 days, average times to fall asleep were 5.4 minutes, 21 minutes and 22 minutes, for phys. book, phone and laptop, respectively.

[Raw data:

Phys. book: 0, 0, 2, 5, 22

Phone: 2, 14, 21, 24, 32, 33

Laptop: 0, 6, 10, 27, 66.]

Conclusion

The sample size was small (I unfortunately lost the motivation to continue). Nevertheless it gave me quite strong evidence that being on devices indeed does affect sleep.

Part 3/4 - General uptakes

In my previous two shortform posts I've talked about some object-level belief changes about technical alignment and some meta-level thoughts about how to do research, both which were prompted by starting in an alignment program.

Let me here talk about some uptakes from all this.

(Note: As with previous posts, this is "me writing about my thoughts and experiences in case they are useful to someone", putting in relatively low effort. It's a conscious decision to put these in shortform posts, where they are not shoved to everyone's faces.)

The main point is that I now think it's much more feasible to do useful technical AI safety work than I previously thought. This update is a result of realizing both that the action space is larger than I thought (this is a theme in the object-level post) and that I have been intimidated by the culture in LW (see meta-post).

One day I heard someone saying "I thought AI alignment was about coming up with some smart shit, but it's more like doing a bunch of kinda annoying things". This comment stuck with me.

Let's take a concrete example. Very recently the "Sleeper Agents" paper came out. And I think both of the following are true:

1: This work is really good.

For reasons such as: it provides actual non-zero information about safety techniques and deceptive alignment; it's a clear demonstration of failures of safety techniques; it provides a test case for testing new alignment techniques and lays out the idea "we could come up with more test cases".

2: The work doesn't contain a 200 IQ godly breakthrough idea.

(Before you ask: I'm not belittling the work. See point 1 above.)

Like: There are a lot of motivations for the work. Many of them are intuitive. Many build on previous work. The setup is natural. The used techniques are standard.

The value is in stuff like combining a dozen "obvious" ideas in a suitable way, carefully designing the experiment, properly implementing the experiment, writing it down clearly and, you know, actually showing up and doing the thing.

And yep, one shouldn't hindsight-bias oneself to think all of this is obvious. Clearly I myself didn't come up with the idea starting from the null string. I still think that I could contribute, to have the field produce more things like that. None of the individual steps is that hard - or, there exist steps that are not that hard. Many of them are "people who have the competence to do the standard things do the standard things" (or, as someone would say, "do a bunch of kinda annoying things").

I don't think the bottleneck is "coming up with good project ideas". I've heard a lot of project ideas lately. While all of them aren't good, in absolute terms many of them are. Turns out that coming up with an idea takes 10 seconds or 1 hour, and then properly executing that requires 10 hours or 1 full-time-equivalent-years.

So I actually think that the bottleneck is more about "we have people executing the tons of projects the field comes up with", at least much more than I previously thought.

And sure, for individual newcomers it's not trivial to come up with good projects. Realistically one needs (or at least I needed) more than the null string. I'll talk about this more in my final post.

Part 4/4 - Concluding comments on how to contribute to alignment

In part 1 I talked about object-level belief changes, in part 2 about how to do research and in part 3 about what alignment research looks like.

Let me conclude by saying things that would have been useful for past-me about "how to contribute to alignment". As in past posts, my mode here is "personal musings I felt like writing that might accidentally be useful to others".

So, for me-1-month-ago, the bottleneck was "uh, I don't really know what to work on". Let's talk about that.

First of all, experienced alignment researchers tend to have plenty of ideas. (Come on, me-1-month-ago, don't be surprised.) Did you know that there's this forum where alignment people write out their thoughts?

"But there's so much material there", me-1-month-ago responds.

what kind of excuse is that Okay so how research programs work is that you have some mentor and you try to learn stuff from them. You can do a version of this alone as well: just take some researcher you think has good takes and go read their texts.

No, I mean actually read them. I don't mean "skim through the posts", I mean going above and beyond here: printing the text on paper, going through it line by line, flagging down new considerations you haven't thought before. Try to actually understand what the author thinks, to understand the worldview that has generated those posts, not just going "that claim is true, that one is false, that's true, OK done".

And I don't mean reading just two or three posts by the author. I mean like a dozen or more. Spending hours on reading posts, really taking the time there. This is what turns "characters on a screen" to "actually learning something".

A major part of my first week in my program involved reading posts by Evan Hubinger. I learned a lot. Which is silly: I didn't need to fly to the Bay to access https://www.alignmentforum.org/users/evhub. But, well, I have a printer and some "let's actually do something ok?" attitude here.

Okay, so I still haven't a list of Concrete Projects To Work On. The main reason is that going through the process above kind of results in that. You will likely see something promising, something fruitful, something worthwhile. Posts often have "future work" sections. If you really want explicit lists of projects, then you can unsurprisingly find those as well (example). (And while I can't speak for others, my guess is that if you really have understood someone's worldview and you go ask them "is there some project you want me to do?", they just might answer you.)

Me-from-1-ago would have had some flinch reaction of "but are these projects Real? do they actually address the core problems?", which is why I wrote my previous three posts. Not that they provide a magic wand which waves away this question, rather they point out that past-me's standard for what counts as Real Work was unreasonably high.

And yeah, you very well might have thoughts like "why is this post focusing on this instead of..." or "meh, that idea has the issue where...". You know what to do with those.

Good luck!

Clarifying a confusion around deceptive alignment / scheming

There's a common blurrying-the-lines motion related to deceptive alignment that especially non-experts easily fall into.^[1]

There is a whole spectrum of "how deceptive/schemy is the model", that includes at least

deception - instrumental deception - alignment-faking - instrumental alignment-faking - scheming.^[2]

Especially in casual conversations people tend to conflate between things like "someone builds a scaffolded LLM agent that starts to acquire power and resources, deceive humans (including about the agent's aims) etc." and "scheming". This is incorrect. While this can count for instrumental alignment-faking, scheming (as a technical term defined by Carlsmith) demands training gaming, and hence scaffolded LLM agents are out of the scope of the definition.^[3] 

The main point: when people debate likelihood scheming/deceptive alignment, they are NOT talking about whether LLM agents will exhibit instrumental deception or such. They are debating whether the training process creates models that training-game (for instrumental reasons).

I think the right mental picture is to think of dynamics of SGD and the training process rather than dynamics of LLM scaffolding and prompting.^[4]

Corollaries:

• This confusion allows for accidental motte-and-bailey dynamics^[5]
  • Motte: "scaffolded LLM agents will exhibit power-seeking behavior, including deception about their alignment" (which is what some might call "the AI scheming")
  • Bailey: "power-motivated instrumental training gaming is likely to arise from such-and-such training processes" (which is what the actual technical term of scheming refers to)
• People disagreeing with the bailey are not necessarily disagreeing about the motte.^[6]
• You can still be worried about the motte (indeed, that is bad as well!) without having to agree with the bailey.

See also: Deceptive AI =≠= Deceptively-aligned AI, which makes very closely related points, and my comment on that post listing a bunch of anti-examples of deceptive alignment.

1. ^{^}

   (Source: I've seen this blurrying pop up in a couple of conversations, and have earlier fallen into the mistake myself.)

2. ^{^}

   Alignment-faking is basically just "deceiving humans about the AI's alignment specifically". Scheming demands the model is training-gaming(!) for instrumental reasons. See the very beginning of Carlsmith's report.

3. ^{^}

   Scheming as an English word is descriptive of the situation, though, and this duplicate meaning of the word probably explains much of the confusion. "Deceptive alignment" suffers from the same issue (and can also be confused for mere alignment-faking, i.e. deception about alignment).

4. ^{^}

   Note also that "there is a hyperspecific prompt you can use to make the model simulate Clippy" is basically separate from scheming: if Clippy-mode doesn't active during training, the Clippy can't training-game, and thus this isn't scheming-as-defined-by-Carlsmith.

   There's more to say about context-dependent vs. context-independent power-seeking malicious behavior, but I won't discuss that here.

5. ^{^}

   I've found such dynamics in my own thoughts at least.

6. ^{^}

   The motte and bailey just are very different. And example: Reading Alex Turner's Many Arguments for AI x-risk are wrong, he seems to think deceptive alignment is unlikely while writing "I’m worried about people turning AIs into agentic systems using scaffolding and other tricks, and then instructing the systems to complete large-scale projects."

I've recently started in a research program for alignment. One outcome among many is that my beliefs and models have changed. Here I outline some ideas I've thought about.

The tone of this post is more like

"Here are possibilities and ideas I haven't really realized that exist.  I'm yet uncertain about how important they are, but seems worth thinking about them. (Maybe these points are useful to others as well?)"

than

"These new hypotheses I encountered are definitely right."

This ended up rather long for a shortform post, but still posting it here as it's quite low-effort and probably not of that wide of an interest.

Insight 1: You can have a an aligned model that is neither inner nor outer aligned.

Opposing frame: To solve the alignment problem, we need to solve both the outer and inner alignment: to choose a loss function whose global optimum is safe and then have the model actually aim to optimize it.

Current thoughts: 

A major point of the inner alignment and related texts is that the outer optimization processes are cognition-modifiers, not terminal-values. Why on earth should we limit our cognition-modifiers to things that are kinda like humans' terminal values?

That is: we can choose our loss functions and whatever so that they build good cognition inside the model, even if those loss functions' global optimums were nothing like Human Values Are Satisfied. In fact, this seems like a more promising alignment approach than what the opposing frame suggests!

(What made this click for me was Evan Hubinger's training stories post, in particular the excerpt

It’s worth pointing out how phrasing inner and outer alignment in terms of training stories makes clear what I think was our biggest mistake in formulating that terminology, which is that inner/outer alignment presumes that the right way to build an aligned model is to find an aligned loss function and then have a training goal of finding a model that optimizes for that loss function.

I'd guess that this post makes a similar point somewhere, though I haven't fully read it.)

Insight 2: You probably want to prevent deceptively aligned models from arising in the first place, rather than be able to answer the question "is this model deceptive or not?" in the general case,

Elaboration: I've been thinking that "oh man, deceptive alignment and the adversarial set-up makes things really hard". I hadn't made the next thought of "okay, could we prevent deception from arising in the first place?".

("How could you possibly do that without being able to tell whether models are deceptive or not?", one asks. See here for an answer. My summary: Decompose the model-space to three regions,

A) Verifiably Good models

B) models where the verification throws "false"

C) deceptively aligned models which trick us to thinking they are Verifiably Good.

Aim to find a notion of Verifiably Good such that one gradient-step never makes you go from A to C. Then you just start training from A, and if you end up at B, just undo your updates.)

Insight 3: There are more approaches to understanding our models / having transparent models than mechanistic interpretability.

Previous thought: We have to do mechanistic interpretability to understand our models!

Current thoughts: Sure, solving mech interp would be great. Still, there are other approaches:

• Train models to be transparent. (Think: have a term in the loss function for transparency.)
• Better understand training processes and inductive biases. (See e.g. work on deep double descent, grokking, phase changes, ...)
• Creating architectures that are more transparent by design
• (Chain-of-thought faithfulness is about making LLMs thought processes interpretable in natural language)

Past-me would have objected "those don't give you actual detailed understanding of what's going on". To which I respond: 

"Yeah sure, again, I'm with you on solving mech interp being the holy grail. Still, it seems like using mech interp to conclusively answer 'how likely is deceptive alignment' is actually quite hard, whereas we can get some info about that by understanding e.g. inductive biases."

(I don't intend here to make claims about the feasibility of various approaches.)

Insight 4: It's easier for gradient descent to do small updates throughout the net than a large update in one part. 

(Comment by Paul Christiano as understood by me.) At least in the context where this was said, it was a good point for expecting that neural nets have distributed representations for things (instead of local "this neuron does...").

Insight 5: You can focus on safe transformative AI vs. safe superintelligence.

Previous thought: "Oh man, lots of alignment ideas I see obviously fail at a sufficiently high capability level"

Current thought: You can reasonably focus on kinda-roughly-human-level AI instead of full superintelligence. Yep, you do want to explicitly think "this won't work for superintelligences for reasons XYZ" and note which assumptions about the capability of the AI your idea relies on. Having done that, you can have plans aim to use AIs for stuff and you can have beliefs like

"It seems plausible that in the future we have AIs that can do [cognitive task], while not being capable enough to break [security assumption], and it is important to seize such opportunities if they appear."

Past-me had flinch negative reactions about plans that fail at high capability levels, largely due to finding Yudkowsky's model of alignment compelling. While I think it's useful to instinctively think "okay, this plan will obviously fail once we get models that are capable of X because of Y" to notice the security assumptions, I think I went too far by essentially thinking "anything that fails for a superintelligence is useless". 

Insight 6: The reversal curse bounds the level of reasoning/inference current LLMs do.

(Owain Evans said something that made this click for me.) I think the reversal curse is good news in the sense of "current models probably don't do anything too advanced under-the-hood". I could imagine worlds where LLMs do a lot of advanced, dangerous inference during training - but the reversal curse being a thing is evidence against those worlds (in contrast to more mundane worlds).

"Trends are meaningful, individual data points are not"^[1]

Claims like "This model gets x% on this benchmark" or "With this prompt this model does X with p probability" are often individually quite meaningless. Is 60% on a benchmark a lot or not? Hard to tell. Is doing X 20% of the time a lot or not? Go figure.

On the other hand, if you have results like "previous models got 10% and 20% on this benchmark, but our model gets 60%", then that sure sounds like something. "With this prompt the model does X with 20% probability, but with this modification the probability drops to <1%" also sounds like something, as does "models will do more/less of Y with model size".

There are some exceptions: maybe your results really are good enough to stand on their own (xkcd), maybe it's interesting that something happens even some of the time (see also this). It's still a good rule of thumb.

1. ^{^}

   Shoutout to Evan Hubinger for stressing this point to me

A frame for thinking about capability evaluations: outer vs. inner evaluations

When people hear the phrase "capability evaluations", I think they are often picturing something very roughly like METR's evaluations, where we test stuff like:

• Can the AI buy a dress from Amazon?
• Can the AI solve a sudoku?
• Can the AI reverse engineer this binary file?
• Can the AI replicate this ML paper?
• Can the AI replicate autonomously?

(See more examples at METRs repo of public tasks.)

In contrast, consider the following capabilities:

• Is the AI situationally aware?
• Can the AI do out-of-context reasoning?
• Can the AI do introspection?
• Can the AI do steganography?
• Can the AI utilize filler tokens?
• Can the AI obfuscate its internals?
• Can the AI gradient hack?

There's a real difference^[1] between the first and second categories. Rough attempt at putting it in words: the first one is treating the AI as an indivisible atom and seeing how it can affect the world, whereas the second one treats the AI as having "an inner life" and seeing how it can affect itself.

Hence the name "outer vs. inner evaluations". (I think the alternative name "dualistic vs. embedded evaluations", following dualistic and embedded notions of agency, gets closer at the distinction while being less snappy. Compare also to behavioral vs. cognitive psychology.)

It seems to me that the outer evaluations are better established: we have METR and the labs itself doing such capability evaluations. There's plenty of work on inner evaluations as well, the difference being that it's more diffused. (Maybe for good reason: it is tricky to do proper inner evals.)

I've gotten value out of this frame; it helps me not forget inner evals in the context of evaluating model capabilities.

1. ^{^}

   Another difference is that in outer evals we often are interested in getting the most out of the model by ~any means, whereas with inner evals we might deliberately restrict the model's action space. This difference might be best thought of as a separate axis, though.

Epistemic responsibility

"You are responsible for you having accurate beliefs."

Epistemic responsibility refers to the idea that it is on you to have true beliefs. The concept is motivated by the following two applications.

In discussions

Sometimes in discussions people are in a combative "1v1 mode", where they try to convince the other person of their position and defend their own position, in contrast to a cooperative "2v0 mode" where they share their beliefs and try to figure out what's true. See the soldier mindset vs. the scout mindset.

This may be framed in terms of epistemic responsibility: If you accept that "It is (solely) my responsibility that I have accurate beliefs", the conversation naturally becomes less about winning and more about having better beliefs afterwards. That is, a shift from "darn, my conversation partner is so wrong, how do I explain it to them" to "let me see if the other person has valuable points, or if they can explain how I could be wrong about this".

In particular, from this viewpoint it sounds a bit odd if one says the phrase "that doesn't convince me" when presented with an argument, as it's not on the other person to convince you of something. 

Note: This doesn't mean that you have to be especially cooperative in the conversation. It is your responsibility that you have true beliefs, not that you both have. If you end up being less wrong, success. If the other person doesn't, that's on them :-)

Trusting experts

There's a question Alice wants to know the answer to. Unfortunately, the question is too difficult for Alice to find out the answer herself. Hence she defers to experts, and ultimately believes what Bob-the-expert says.

Later, it turns out that Bob was wrong. How does Alice react?

A bad reaction is to be angry at Bob and throw rotten tomatoes at him.

Under the epistemic responsibility frame, the proper reaction is "Huh, I trusted the wrong expert. Oops. What went wrong, and how do I better defer to experts next time?"

When (not) to use the frame

I find the concept to be useful when revising your own beliefs, as in the above examples of discussions and expert-deferring.

One limitation is that belief-revising often happens via interpersonal communication, whereas epistemic responsibility is individualistic. So while "my aim is to improve my beliefs" is a better starting point for conversations than "my aim is to win", this is still not ideal, and epistemic responsibility is to be used with a sense of cooperativeness or other virtues.

Another limitation is that "everyone is responsible for themselves" is a bad norm for a community/society, and this is true of epistemic responsibility as well.

I'd say that the concept of epistemic responsibility is mostly for personal use. I think that especially the strongest versions of epistemic responsibility (heroic epistemic responsibility?), where you are the sole person responsible for you having true beliefs and where any mistakes are your fault, are something you shouldn't demand of others. For example, I feel like a teacher has a lot of epistemic responsibility on the behalf of their students (and there are other types of responsibilities going on here).

Or whatever, use it how you want - it's on you to use it properly.

In praise of prompting

(Content: I say obvious beginner things about how prompting LLMs is really flexible, correcting my previous preconceptions.)

I've been doing some of my first ML experiments in the last couple of months. Here I outline the thing that surprised me the most:

Prompting is both lightweight and really powerful.

As context, my preconception of ML research was something like "to do an ML experiment you need to train a large neural network from scratch, doing hyperparameter optimization, maybe doing some RL and getting frustrated when things just break for no reason".^[1]

And, uh, no.^[2] I now think my preconception was really wrong. Let me say some things that me-three-months-ago would have benefited from hearing.

When I say that prompting is "lightweight", I mean that both in absolute terms (you can just type text in a text field) and relative terms (compare to e.g. RL).^[3] And sure, I have done plenty of coding recently, but the coding has been basically just to streamline prompting (automatically sampling through API, moving data from one place to another, handling large prompts etc.) rather than ML-specific programming. This isn't hard, just basic Python.

When I say that prompting is "really powerful", I mean a couple of things.

First, "prompting" basically just means "we don't modify the model's weights", which, stated like that, actually covers quite a bit of surface area. Concrete things one can do: few-shot examples, best-of-N, look at trajectories as you have multiple turns in the prompt, construct simulation settings and seeing how the model behaves, etc.

Second, suitable prompting actually lets you get effects quite close to supervised fine-tuning or reinforcement learning(!) Let me explain:

Imagine that I want to train my LLM to be very good at, say, collecting gold coins in mazes. So I create some data. And then what?

My cached thought was "do reinforcement learning". But actually, you can just do "poor man's RL": you sample the model a few times, take the action that led to the most gold coins, supervised fine-tune the model on that and repeat.

So, just do poor man's RL? Actually, you can just do "very poor man's RL", i.e. prompting: instead of doing supervised fine-tuning on the data, you simply use the data as few-shot examples in your prompt.

My understanding is that many forms of RL are quite close to poor man's RL (the resulting weight updates are kind of similar), and poor man's RL is quite similar to prompting (intuition being that both condition the model on the provided examples).^[4]

As a result, prompting suffices way more often than I've previously thought.

1. ^{^}

   This negative preconception probably biased me towards inaction.

2. ^{^}

   Obviously some people do the things I described, I just strongly object to the "need" part.

3. ^{^}

   Let me also flag that supervised fine-tuning is much easier than I initially thought: you literally just upload the training data file at https://platform.openai.com/finetune

4. ^{^}

   I admit that I'm not very confident on the claims of this paragraph. This is what I've gotten from Evan Hubinger, who seems more confident on these, and I'm partly just deferring here.

Inspired by the "reward chisels cognition into the agent's network" framing from Reward is not the optimization target, I thought: is reward necessarily a fine enough tool? More elaborately: if you want the model to behave in a specific way or to have certain internal properties, can you achieve this simply by a suitable choosing of the reward function?

I looked at two toy cases, namely Q-learning and training a neural network (the latter which is not actually reinforcement learning but supervised learning). The answers were "yep, suitable reward/loss (and datapoints in the case of supervised learning) are enough". 

I was hoping for this not to be the case, as that would have been more interesting (imagine if there were fundamental limitations to the reward/loss paradigm!), but anyways. I now expect that also in more complicated situations reward/loss are, in principle, enough.

Example 1: Q-learning. You have a set $S$ of states and a set $A$ of actions. Given a target policy $\pi :S\to A$, can you necessarily choose a reward function $R:S\times A\to R$ such that, training for long enough* with Q-learning (with positive learning rate and discount factor), the action that maximizes reward is the one given by the target policy: $\forall s\in S:\arg {max}_{a\in A}Q(s,a)=\pi \left(s\right)$?

*and assuming we visit all of the states in $S$ many times

The answer is yes. Simply reward the behavior you want to see: let $R(s,a)=1$ if $a=\pi \left(s\right)$ and $R(s,a)=0$ otherwise.

(In fact, one can more strongly choose, for any target value function  $Q^{\prime }:S\times A\to R$, a reward function $R$ such that the values $Q(s,a)$ in Q-learning converge in the limit to $Q^{\prime }(s,a)$. So not only can you force certain behavior out of the model, you can also choose the internals.)

Example 2: Neural network.

Say you have a neural network $R^n\to R$ with $m$ tunable weights $w=(w_1,\dots ,w_m)$. Can you, by suitable input-output pairs and choices of the learning rate, modify the weights of the net so that they are (approximately) equal to $w^{\prime }=(w_1^{\prime },\dots ,w_m^{\prime })$?

(I'm assuming here that we simply update the weights after each data point, instead of doing SGD or something. The choice of loss function is not very relevant, take e.g. square-error.)

The following sketch convinces me that the answer is positive:

Choose $m$ random input-output pairs $(x_i,y_i)$. The gradients $g_i$ of the weight vectors are almost certainly linearly independent. Hence, some linear combination $c_1{g}_1+\dots +c_m{g}_m$ of them equals $w^{\prime }-w$. Now, for small $ϵ>0$, running back-propagation on the pair $(x_i,y_i)$ with learning rate $ϵc_i$ for all $i=1,\dots ,m$ gives you an update approximately in the direction of $w^{\prime }-w$. Rinse and repeat.

Iteration as an intuition pump

I feel like many game/decision theoretic claims are most easily grasped when looking at the iterated setup:

Example 1. When one first sees the prisoner's dilemma, the argument that "you should defect because of whatever the other person does, you are better off by defecting" feels compelling. The counterargument goes "the other person can predict what you'll do, and this can affect what they'll play".

This has some force, but I have had a hard time really feeling the leap from "you are a person who does X in the dilemma" to "the other person models you as doing X in the dilemma". (One thing that makes this difficult that usually in PD it is not specified whether the players can communicate beforehand or what information they have of each other.) And indeed, humans models' of other humans are limited - this is not something you should just dismiss.

However, the point "the Nash equilibrium is not necessarily what you should play" does hold, as is illustrated by the iterated Prisoner's dilemma. It feels intuitively obvious that in a 100-round dilemma there ought to be something better than always defecting.

This is among the strongest intuitions I have for "Nash equilibria do not generally describe optimal solutions".

Example 2. When presented with lotteries, i.e. opportunities such as "X% chance you win A dollars, (100-X)% chance of winning B dollars", it's not immediately obvious that one should maximize expected value (or, at least, humans generally exhibit loss aversion, bias towards certain outcomes, sensitivity to framing etc.).

This feels much clearer when given the option to choose between lotteries repeatedly. For example, if you are presented with the two buttons, one giving you a sure 100% chance of winning 1 dollar and the other one giving you a 40% chance of winning 3 dollars, and you are allowed to press the buttons a total of 100 times, it feels much clearer that you should always pick the one with the highest expected value. Indeed, as you are given more button presses, the probability of you getting (a lot) more money that way tends to 1 (by the law of large numbers).

This gives me a strong intuition that expected values are the way to go.

Example 3. I find Newcomb's problem a bit confusing to think about (and I don't seem to be alone in this). This is, however, more or less the same problem as prisoner's dilemma, so I'll be brief here.

The basic argument "the contents of the boxes have already been decided, so you should two-box" feel compelling, but then you realize that in an iterated Newcomb's problem you will, by backward induction, always two-box.

This, in turn, sounds intuitively wrong, in which case the original argument proves too much. 

One thing I like about iteration is that it makes the concept of ""it really is possible to make predictions about your actions" feel more plausible: there's clear-cut information about what kind of plays you'll make, namely the previous rounds. I feel like in my thoughts I sometimes feel like rejecting the premise, or thinking that "sure, if the premise holds, I should one-box, but it doesn't really work that way in real life, this feels like one of those absurd thought experiments that don't actually teach you anything". Iteration solves this issue.

Another pump I like is "how many iterations do there need to be before you Cooperate/maximize-expected-value/one-box?". There (I think) is some number of iterations for this to happen, and, given that, it feels like "1" is often the best answer.

All that said, I don't think iterations provide the Real Argument for/against the position presented. There's always some wiggle room for "but what if you are not in an iterated scenario, what if this truly is a Unique Once-In-A-Lifetime Opportunity?". I think the Real Arguments are something else - e.g. in example 2 I think coherence theorems give a stronger case (even if I still don't feel them as strongly on an intuitive level). I don't think I know the Real Argument for example 1/3.