Published on October 6, 2025 1:01 PM GMT

Written in June 2023. I’ve only made some light style edits before posting here. Hence, this piece does not necessarily reflect my current views. I originally did not want to post it because I disagreed with some of the stuff I wrote (or at least thought it needed more justification). I also considered this post superseded by DiGiovanni ([2023] 2024 ), which makes the same case, better. But well, maybe someone will find the present piece interesting, anyway, so here it is. (I also find it amusing to see the seeds of cluelessness concerns in the writings of 2023-Jim, who was still keen on the speculative EA interventions other than ECL backed by nothing more than seemingly arbitrary “best-guesses”. My thinking has evolved since then.)

Target audience: People who have engaged with ECL (Evidential Cooperation in Large Worlds).

Disclaimer from 2023-Jim: I think I assert my views more strongly than I hold them in this doc.

Summary

Some s-risk researchers have made a case for updating our cause prioritization based on Evidential Cooperation in Large Worlds (ECL) and for promoting it as something agents should pursue. My goal, here, is to red-team their take, by arguing that the necessary conditions for ECL to make a human non-trivially update their cause prioritization don’t seem met at all, such that – although it is likely the rational thing to do in theory – ECL for humans is not a big deal and not worth thinking about in practice.

I 100% buy the argument that any human should – in theory – engage in Evidential Cooperation in Large Worlds (ECL) to better achieve their goals. However, this does not automatically imply that they should – in practice – behave differently than they would have behaved otherwise. 

For that to be the case, they’d need to have evidential analogs that have values different from theirs, be able to find out what those values are, in what proportion they are represented among the analogs, and what concrete actions they should take based on that. And the arguments that have been made so far to support the claim that all these conditions are met are – I argue – based on very questionable premises.

My crux is that decision-entanglement seems much more complex and fragile than what people who have given thought to this seem to believe (i.e., meeting the necessary conditions for two agents to be decision-entangled is much harder than they imagine), in a way that should lead us to assume a given human’s evidential analogs either roughly share their values or have hardly predictable values (making trade impossible or very costly).

I also argue that this position should be everyone’s prior and that we should stick to it in the absence of solid evidence pointing somewhere else. And I doubt that we will ever find such evidence. Therefore, given how clueless one should be regarding what the values of their analogs are, they don’t seem to have any good reason to update their cause prioritization based on ECL considerations. Doing so would be far less likely to lead to gains from trade than it would be to lead to them shooting themselves in the foot and being exploited in this one-shot collective action problem (See Appendix A on why we should model ECL as a one-shot collective action problem with the whole multiverse).

For what it’s worth, I also suspect that people considering working on ECL might better understand its foundations after reading this.

Terminology

• Two agents are decision-entangled^[1] iff, in a particular situation, the fact that Player 1 makes some decision A is evidence that Player 2 makes some decision A’.^[2] For instance, in a chicken game between two perfect copies, Copy-1’s decision to swerve is evidence that Copy 2 swerves as well. Unless specified otherwise, I assume decision-entanglement with regard to one decision the players have to make to be something binary (i.e., on a given problem, the decisions of two agents are entangled or they aren’t; no in between), at least for the sake of simplicity.

• An agent who is decision-entangled with me is an evidential analog, but I’m almost always going to refer to such an agent as an analog.^[3] Note that an analog is not necessarily an exact copy (with the same values, same memory, same favorite Taylor Swift song, etc.). Two agents may meet the conditions for decision-entanglement (see Buhler 2022) without being perfect copies.

• The term MCUF (Multiverse-wide Compromise Utility Function), formerly introduced as “superrational compromise utility function” by Oesterheld (2017), refers to the function you should maximize to generate gains from evidential trade. The function is ideally some sort of fair compromise between your values and those of your analogs.

1. Conditions for ECL to be a big deal

Consider Alice, a young human adult who wants to dedicate her career to reducing s-risks. One day, she stumbles upon Caspar Oesterheld’s (2017) talk on ECL and gets convinced that instead of (A) focusing on reducing s-risks, she should be (B) focusing on maximizing a MCUF compromising between her values and those of the agents decision-entangled with her (her analogs). However, she’s unsure about whether (B) would/should non-trivially differ from (A) in practice. I.e., she’s uncertain whether ECL is a “big deal”.

I think ECL should be a big deal for Alice if and only if

• (i) she has (a non-trivial number of) evidential analogs that have values different from hers; and

• (ii) she’s able to find out what those values are and in what proportion they are represented among her analogs, and derive a MCUF – or at least some beneficial cooperation heuristics (see Gloor 2018) – from that knowledge;^[4] and

• (iii) assuming she derived a MCUF, she knows what actions she can take to maximize it better than she would have by focusing on her original utility function.

While I haven’t thought about (i) and (iii) carefully enough to claim that we should assume condition these two conditions are fulfilled,^[5] I think my strongest card against the claim that ECL is a big deal is – by far – my argument against assuming (ii) is met, so the next section focuses on that.

2. One should be so uncertain about the value systems distribution among their analogs and how much they should help them that their best shot to maximize their MCUF is probably to focus on their original utility function

I think Alice, from her current clueless position, should obviously start with assuming that (ii) is not met and that the best way to maximize her MCUF is to do what she would have done without considering ECL.^[6] Call this the no-big-deal prior. The question is: do we have arguments that – if sounds – should make Alice update away from this prior? Yes, we do! Here are those that have been made and/or that I can think of (I gave them names and considered whether they pan out):

• The strong representativity argument
• The weak representativity argument
• The predictability-despite-non-representativity argument

However, I argue that all are inconclusive and that we have no good reason (none that I’m aware of, at least) not to stick to the no-big-deal prior.

You might want to open another window with my conditions for decision-entanglement to follow this section without needing to regularly go back to the page before to refresh your mind about key things.

  2.1 The strong representativity argument

The argument can be summarized/formalized as follows:

• P1: We should assume the values of one’s analogs are (at least roughly) representative of the values of all superrationalists (i.e., agents willing to acausally cooperate with agents that have decisions entangled with theirs) in the multiverse.^[7]

• P2: We have some (minor) clues about what the values of superrationalists in the multiverse are, as well as about their distribution and how to help them.^[8]

• Conclusion: One should update away from the no-big-deal prior and update their cause prio.

While I think the conclusion logically follows from the conjunction of P1 and P2 and that Oesterheld (2017) makes a good case for assuming P2, I am highly skeptical of P1.

For P1 to be true, one needs to assume that the factors making two agents decision-entangled are not (or very weakly) correlated with the factors making them have the values they have.

Here’s my argument against making such an assumption:^[9]

• PA: If two agents end up with entangled decisions, this entanglement is most likely homologous, i.e., due to shared “origin” (e.g., shared experiences, genes, and/or architectures) as opposed to due to convergent evolution.
• PB: If they share a common origin influencing what makes them entangled, those same experiences/genes probably also have a large influence over what they value. Origin and values are not orthogonal.
• Conclusion: Therefore, if we know Alice is decision-entangled with Alix, this gives us strong evidence that Alix’s values are not representative of those of a randomly picked superrationalist in the multiverse (i.e., the first premise of the strong representativity argument is false).

I think it is obvious and uncontroversial that the conclusion logically follows from the conjunction of PA and PB, and that PB is true. However, I expect some people not to immediately agree with PA, so let’s expand on that.

Here are what the brilliant Jim Buhler (2022) identifies as the necessary conditions for decision-entanglement: (DT = decision theory)

We know Alice buys non-causal DT and we know that one certainly doesn’t need to share Alice’s values in order to converge to the same. However – contrariwise to what Oesterheld (2017) seems to implicitly assume many times in his paper – (1) compatible DTs, alone, don’t imply decision-entanglement. You also need the agents to (2) be epistemically entangled, and to (3) have been exposed to compatible pieces of evidence. This means that you need them to (2) update and process the information in ways that are identical/similar enough for them to output the same beliefs (regarding whether there are entangled) given the same inputs, and (3) get inputs that are compatible enough for them to output the same beliefs (regarding entanglement) given the same epistemic algorithms. I think we tend not to intuitively grasp how much of an insanely contrived situation that is.

Alice is a human and therefore has an extremely messy and unstable epistemic algorithm. If you bring to her attention something that should make her update on some belief she has, she might update very differently depending on things like her mood, even if she’s the most rational human there is. And what if it’s not you but someone else who brings the thing to her attention? Someone with a different status, different appearance, different voice, different relationship with her, etc. It feels like this slight difference could have a huge butterfly effect on Alice’s beliefs and choices. Therefore, if you take two versions of Alice on different days and bring to their attention the same fact but in different ways, their resulting beliefs may very well not be entangled, even though the two versions of Alice share a strong homologous similarity. Entanglement is fragile. (See Appendix B for more thoughts on this fragility, which is I think a crucial point in my case against updating away from the no-big-deal prior.)

However, let’s consider two different agents:

• Alice before she eats lunch.
• Some randomly picked superrationalist (from somewhere in the multiverse) that doesn’t share any common origin with Alice.

Let’s say Alice after lunch plays a one-shot PD against these two. Which one is most likely to be decision-entangled with her? I hope you would very confidently bet on the former. If so, I think it means you’re endorsing PA.

The analogy with the evolution of animal species seems pretty good actually. If you see two animals that look and behave exactly the same, that is probably because of some common origin. There are cases of high similarities due to convergent evolution, obviously, but these are much rarer than homologous similarities.

And convergent evolution leading agents with totally different origins to end up meeting all the necessary conditions for having entangled decisions (see the above complex and quite demanding conditions) actually seems to me far more specific, unlikely, and rare than convergent evolution making two animal species look or behave the same.

I think I’ve demonstrated that the strong representativity argument doesn’t pan out and therefore shouldn’t make us update away from our no-big-deal prior. However, we could try to patch this argument in light of what we’ve just seen.

  2.2 The weak representativity argument

Here’s the argument (in italic and bold what differs from the previous argument):

• P1: We should assume the values of one’s analogs that have different values from theirs are (at least roughly) representative of the values of all superrationalists (i.e., people willing to acausally cooperate with agents that have decisions entangled with theirs) in the multiverse.

• P2: We have some (minor) clues about what the values of superrationalists in the multiverse are, as well as about their distribution and how to help them.^[10]

• P3: One can somewhat reliably estimate their analogs-with-different-values/analogs-with-same-values ratio in a way that suggests it’s non-trivially >0.
• Conclusion: One should derive from the no-big-deal prior and update their cause prio.

I’m still skeptical of this patched version of P1 and highly doubt P3.

First, let’s focus on P1. If the values of Alice’s analogs are not representative of the values of all superrationalists, why would the values of Alice’s analogs that have different values be representative of the values of all superrationalists? If Alix (an analog of Alice) doesn’t share Alice’s values, she still must have values that are consistent with one of the (very narrow) paths that can lead her to end up decision-entangled with Alice (see previous sub-section). It would be incredibly surprising if all superrationalists with values different from Alice’s were (even roughly) equally likely to take the paths leading to being decision-entangled with her, wouldn’t it? Therefore, someone going out of their way in an attempt to help the values of their analogs seems much less likely to gain from trade than they are to just be a “sucker” and shoot themself in the foot.^[11] I’m aware that the argument I’m making may seem weak in the sense that it relies on intuitions, but

• my intuitions are informed by the conditions for decision-entanglement I identified and that no one carefully thought about so far, as far as I know.
  • when people expressed divergent intuitions in private conversations and Google Doc comments, I think these were always based on the implicit assumption that common superrationality was pretty much sufficient for decision-entanglement (an assumption which I argue – in the previous section and in Buhler 2022 – is very unwarranted).
  • see also Appendix A for more thoughts on what leads me to have these intuitions.
• I still think we should stick to the conservative no-big-deal prior and not naively assume P1 if we have no strong evidence either way. I believe that the bold claim that must be rigorously and carefully justified is P1, not the claim that we’re – by default – clueless regarding the values of one’s analogs.

What about P3? Should we assume we can somewhat reliably estimate Alice’s analogs-with-different-values/analogs-with-same-values ratio in a way that suggests it’s non-trivially? I think we should clearly say “no, unless someone comes up with such a sensible and tractable method that overcomes the unpredictability of butterfly effects and everything”. Did someone come up with such a method that seems sensible and overcomes the unpredictability of butterfly effects and everything? If yes, please let me know, and happy to discuss how promising it seems. 

In sum, I think we are not remotely close to having evidence suggesting we should assume P1 and P3, such that I think the weak representativity argument fails as much as the strong one. However, I can think of another way one might attempt to reach the same conclusion while not buying all the premises of the strong and weak representativity argument.

  2.3 The predictability-despite-non-representativity argument

• P0: We can think about all the plausible paths leading a given agent to end up decision-entangled to a given other (e.g., Alice), and somewhat reliably predict the values they may have in these different paths.
• Conclusion: One should derive from the no-big-deal prior and update their cause prio based on their findings.

If P0 is true, the conclusion is true, but P0 is a wild assumption (even wilder than P1 and P3 in the previous argument, I think). Such predictions seem deeply intractable. Alice would need incredibly accurate/expensive simulations – that track butterfly effects – only to find out the values of her analogs (this wouldn’t tell me much about the values of my analogs or yours). I doubt that Alice will be able to both run such simulations and discover potential gains from trade that are large enough to justify the cost of these simulations. The opportunity cost of spending resources on trying to run these simulations (or thinking about ECL more broadly), instead of focusing on maximizing her original goal, is huge!

“But what if she doesn’t try to run simulations that are not very accurate and focuses on obvious low-hanging fruits?” you ask, nicely.

I don’t think there is or will be any obvious low-hanging fruit for her. If Alice takes action based on what her mediocre simulations tell her the values of her analogs are, she still seems much less likely to gain from trade than to just be a “sucker” and shoot herself in the foot. She’s probably better off sticking to the no-big-deal prior and not wasting her time/resources.

3. Conclusion

I’ve identified the conditions under which Alice (or you, or me) should update away from the no-big-deal prior and revise their cause prioritization based on ECL considerations. I argued that at least one of them – the ability to predict the values of someone’s analogs and how much weight they should have in their MCUF – seems far from being fulfilled and that the arguments that exist for supporting the claim that it is are based on the extremely wobbly assumption that one can (in one way or another) predict the values of their analogs at relatively low cost. I suggested that the reason why this assumption might naively not seem that strong is that we have so far tended to severely underestimate how complex and fragile decision-entanglement is.

Given this huge uncertainty regarding the values of one’s analogs, one’s MCUF should probably not differ from their original utility function (in order to avoid being far more likely to be a “sucker” and shoot themselves in the foot than to actually reap any gains from trade).

This is one of the main reasons why I think researching and promoting ECL is not a priority. If someone reading this still wishes to research ECL, however, they may want to consider focusing on finding new arguments (in favor of updating away from the no-big-deal prior) that actually work, and/or debunk the assumptions I make in this post (e.g., the assumption that we should start with the no-big-deal prior, or my conditions for decision-entanglement).

Acknowledgment

This doc is partly based on some old notes I wrote in 2022 during my summer research fellowship at Existential Risk Alliance, which benefited a lot from comments from Caspar Oesterheld, Johannes Treutlein, and Lukas Gloor who were mentoring me at the time. Some discussions I had with Sylvester Kollin were also very helpful. Thanks to Caspar, Sylvester, Anthony DiGiovanni, Antonin Broi, and James Faville for valuable comments on a 2023-draft. All assumptions/claims/omissions remain my own.

Appendix A: ECL as a one-shot collective action problem with the whole multiverse

Recall Alice, who wants to do ECL, and say N is the total number of agents other than Alice in the multiverse. Here, I’d claim that Alice is basically playing N different one-shot prisoner’s dilemmas (PDs) against all of these N agents. (Nothing truly new or controversial I think. This clearly seems implied by Oesterheld 2017.)

What her MCUF looks like depends on whom she thinks she should cooperate with (among these N agents), their values, and their relative bargaining power (which influences the payoffs of the PDs).

While I don’t expect Alice to actually compute her dominant strategy for the PDs one by one before deciding how to act, I think this is ideally what she should do if she wasn’t constrained by time or resources, such that – in practice –  her method should be something that’d best approximate the result she would get if she was doing this expensive game-theoretical computation.

Therefore, I think pinning down the exact conditions under which she should cooperate with someone in a one-shot PD (see Buhler 2022), will help her figure out what kind of agents (e.g., with what sort of values) she should help via ECL, such that she could better approximate what her MCUF should be (or find out whether it’s worth trying to build an MCUF different from her original utility function in the first place).

Appendix B: Entanglement is fragile

Here is how I would proceed if I think about whether I should cooperate in a one-shot prisoner’s dilemma with some other agent, on a day where I feel ambitious (see Appendix A for explanation for why I think this is a good way to think about ECL):

I have a very high credence in non-causal decision theory – I might have an “acausal influence” over the other agent’s decision – I start with the prior that our decisions are most certainly not entangled – I try to identify the necessary conditions for decision-entanglement (see, e.g., Buhler 2022) and look for reasons to update away from this prior – If I find any, I somehow update my credence in our decisions being entangled – I try to find a way to compute my dominant strategy based on this credence and on the exact payoffs of the game (see, e.g., the Appendix in Buhler 2022) – The maths tell me whether I should cooperate or defect.

I think this process is very fragile, for the following reasons:

1. I might very well have ended up identifying different conditions for decision-entanglement that make me think about the problem in a different way. For example, some discussions with Sylvester Kollin highly influenced the conditions I formalized (sidenote: this doesn’t mean that he endorses them), and therefore also my a priori credence in entanglement between my decision and that of my counterpart.
2. The way I “update my credence in our decisions being entangled” is underdefined and probably highly dependent on random small factors, such that I might update pretty differently depending on, e.g., my mood.
3. I expect the vast majority of superrationalists not to converge on this process I have. Even I, on a day where I don’t feel ambitious, would proceed differently: I have a very high credence in non-causal decision theory – I might have an “acausal influence” over the other agent’s decision – I start with the prior that our decisions are most certainly not entangled – This prior is so strong that I think it’s not worth my time looking for unlikely reasons to update away from it – I defect.

Because of this, even a barely different past or future version of me – or a perfect-ish copy of me in a barely different environment – may very well not be decision-entangled with me.

Therefore, I a priori seem fairly unlikely to be entangled with agents that have values different from mine, and if I happen to be, butterfly effects make predicting the values of such agents and their distribution – or the values I should help and how much – pretty intractable. And while I talk only about me and my analogs (as opposed to, e.g., you and yours), here, I don’t think we have any reason to assume I am a weird unrepresentative edge case.

1.  For those familiar with Oesterheld’s (2017) terminology, this is meant to be analogous to “Two agents have correlated decisions”. I think Daniel Kokotajlo is the one who informally started to use the term “entanglement” which I prefer, mainly because I think it addresses the problem Richard Ngo points out in the fourth point of this comment.

2.  This is assuming that the presence of decision-entanglement is an objective fact about the world, i.e., that there is something that does (or doesn’t) make the decisions of two agents entangled, and that it is not up to our interpretation (this doesn’t mean that decision-entanglement doesn’t heavily rely on the subjectivity of the two agents). This assumption is sort of controversial. However, I am using this “entanglement realist” framework all along the doc, and think the takeaways would be the same if I was adopting an “anti-realist” view. This is the reason why I don’t wanna bother thinking too much about this “entanglement (anti-)realism” thing. It doesn’t seem useful. Nonetheless, please let me know if you think my framework leads me to conclusions that are peculiar to it, such that they would be more questionable.

3.  Thanks to Rory Švarc for coming up with this term.

4.  This is acknowledged by Caspar Oesterheld (2017, section 3.2.3, 4.1 and 6.11).

5.  I haven’t thought about (iii) at all. There may be a wager in favor of assuming (i) (see, e.g., Treutlein 2018). Also, even if the ratio analogs-with-different-values/analogs-with-same-values is ridiculously small, the potential gains from trade might still be fairly large if the number of analogs is enormous or infinite. However, there are probably counter-considerations I haven’t thought about. Lukas Gloor raised some seemingly compelling arguments against Treutlein’s wager in comments on some old doc of mine, for example, but I haven’t taken the time to dive into this.

6.  Oesterheld (2017, section 4.1 and 6.11) writes, “We begin with a general challenge: given that we currently know so little about the values of other agents in the multiverse [/ our analogs], how can we cooperate with them? With our current state of knowledge, it appears impossible to conclude what position [ECL] recommends on particular issues. For example, it seems impractical to decide whether we should vote and lobby in favor or against mass surveillance, abortion, marijuana legalization or the death penalty. Perhaps [ECL], while interesting in theory, is practically impossible to apply because of our ignorance of the values in the multiverse? […] Indeed, one possible criticism of [ECL] is that we will never be sufficiently certain of just how common some other value system is. Thus, the argument goes, we should in practice never take any specific value systems other than our own into consideration.” Note that – as we will see – he argues that condition (ii) is (at least somewhat) met, however.

7.  This premise is implicitly assumed in Osterheld’s (2017) paper.

8.  Most of Osterheld’s (2017) section 4.1 and 6.11 focus on justifying this second premise.

9.  Tobias Baumann gives a similar-ish argument in this EA Forum comment.

10.  Most of Osterheld’s (2017) section 4.1 and 6.11 focuses on justifying this second premise.

11. ^{^}

     This is partly due to the fact that there are many more ways in which someone can maximize something irrelevant than ways in which they can do things right. Here are some related quotes I find interesting: “It is apparent that things would start to look confusing pretty quickly, and it seems legitimate to question whether humans can get the details of this picture right enough to reap any gains from trade at all (as opposed to shooting oneself in the foot).” (Lukas Gloor 2018). “Once you’ve started trying to acausally influence the behavior of aliens throughout the multiverse, though, one starts to wonder even more about the whole lost-your-marbles thing. And even if you’re OK with this sort of thing in principle, it’s a much further question whether you should expect any efforts in this broad funky-decision-theoretic vein to go well in practice. Indeed, my strong suspicion is that with respect to multiverse-wide whatever whatevers, for example, any such efforts, undertaken with our current level of understanding, will end up looking very misguided in hindsight, even if the decision theory that motivated them ends up vindicated. Here I think of Bostrom’s “ladder of deliberation,” in which one notices that whether an intervention seems like a good or bad idea switches back and forth as one reasons about it more, with no end in sight, thus inducing corresponding pessimism about the reliability of one’s current conclusions. Even if the weird-decision-theory ladder is sound, we are, I think, on a pretty early rung.” (Joe Carlsmith 2021)