In other words, what apparent imperfections in human reasoning

A) Remain apparent after the Replication Crisis,

B) Aren't secretly adaptive/reasonable in some counterintuitive way, and

C) Deal most damage to the people they inhabit (and/or those close to them, and/or wider society)?

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Seth Herd

7835

Motivated reasoning/confirmation bias.

As Scott Alexander said in his review of Julia Galif's The Scout Mindset:

Of the fifty-odd biases discovered by Kahneman, Tversky, and their successors, forty-nine are cute quirks, and one is destroying civilization. This last one is confirmation bias.

He goes on to argue that this bias is the source of polarization in society, which is distorting our beliefs and setting us at each other's throats. How could someone believe such different things unless they're either really stupid or lying to conceal their selfishness? I think this is right, and I think it's at play even in the best rationalist communities like LessWrong. I think it's particularly powerful in difficult domains, like AGI prediction and alignment theory. When there's less real evidence, biases play a larger role.

I reached this conclusion independently while studying those and the remaining ~149 biases listed on Wikipedia at that point. You can get a little more rational by making your estimates carefully. That covers most of the biases. But the world is being destroyed by people believing what is comfortable to believe instead of what the evidence suggests. This is usually also what they already believe, so the definition of confirmation bias is highly overlapping with motivated reasoning. 

I studied the brain basis of cognitive biases for four years while funded by an IARPA program; I thought it was more worthwhile than the rest of what we were doing in cognitive neuroscience, so I kept up with it as part of my research for the remaining four years I was in the field.

I think motivated reasoning is a better conceptual term for understanding what's going on, but let's not quibble about terminology. I'm going to mostly call it motivated reasoning, MR, but you can take almost everything I'm going to say and apply it to confirmation bias- because mostly it's comfortable to keep believing what we already do. We chose to believe it partly because it was comfortable, and now it fits with all of our other beliefs, so changing it and re-evaluating the rest of our connected beliefs is uncomfortable.

Wait, you're saying! I'm a rationalist! I don't just believe what's comfortable!

Yes, that's partly true. Believing in seeking truth when it's hard does provide some resistance to motivated reasoning. A hardcore rationalist actually enjoys changing their mind sometimes. But it doesn't confer immunity. We still have emotions, and it's still more comfortable to think that we're already right because we're good rationalists who've already discerned the truth.

There are two ways confirmation bias works. One is that it's easier to think of confirming evidence than disconfirming evidence. The associative links tend to be stronger. When you're thinking of a hypothesis you tend to believe, it's easy to think of evidence that supports it. 

The stronger one is that there's a miniature Ugh field[1] surrounding thinking about evidence and arguments that would disprove a belief you care about. It only takes a flicker of a thought to make the accurate prediction about where considering that evidence could lead: admitting you were wrong, and doing a bunch of work re-evaluating all of your related beliefs. Then there's a little unconscious yuck feeling when you try to pay attention to that evidence.

This is just a consequence of how the brain estimates the value of predicted outcomes and uses that to guide its decision-making, including its micro-decisions about what to attend to. I wrote a paper reviewing all of the neuroscience behind this, Neural mechanisms of human decision-making, but it's honestly kind of crappy based on the pressure to write for a super-specialized audience, and my reluctance at the time to speed up progress on brain-like AGI. So I recommend Steve Byrnes' valence sequence over that complex mess; it perfectly describes the psychological level, and he's basing it on those brain mechanisms even though he's not directly talking about them. And he's a better writer than I am. 

Trapped priors is at least partly overlapping with confirmation bias. Or it could even just be strong priors. The issue is that everyone has seen different evidence and arguments - and we've very likely spent more time attending to evidence that supports our original hypothesis, because of the subtle push of motivated reasoning.

Motivated reasoning isn't even strictly speaking irrational. Suppose there's some belief that really doesn't make a difference in your daily life, like that there's a sky guy with a cozy afterlife, or which of two similar parties should receive your vote (which will almost never actually change any outcomes). Here the two definitions of rationality diverge: believing the truth is now at odds with doing what works. It will obviously work better to believe what your friends and neighbors believe, so you won't be in arguments with them and they'll support you more when you need it.

If we had infinite cognitive capacity, we could just believe the truth while claiming to believe whatever works. And we could keep track of all of the evidence instead of picking and choosing which to attend to.

But we don't. So motivated reasoning, confirmation bias, and the resulting tribalism (which happens when other emotions like irritation and outrage get involved in our selection of evidence and arguments) are powerful factors, even for a devoted rationalist.

The only remedy I know of is to cultivate enjoying being wrong. This involves giving up a good bit of one's self-concept as a highly intelligent individual. This gets easier if you remember that everyone else is also doing their thinking with a monkey brain that can barely chin itself on rationality.

Thanks for asking this question; it's a very smart question to ask. And I've been meaning to write about this on LW and haven't prioritized doing a proper job, so it's nice to have an excuse to do a brief writeup.

 

  1. ^

    See also Defeating Ugh Fields In Practice for some interesting and useful review.

    Edit: Staring into the abyss as a core life skill seems to very much be about why and how to overcome motivated reasoning. The author learned to value the idea of being wrong about important beliefs, by seeing a few people accomplish extraordinary things as a result of questioning their central beliefs and changing their minds.

This is a great comment, IMO you should expand it, refine it, and turn it into a top-level post.

Also, question: How would you design a LLM-based AI agent (think: like the recent computer-using Claude but much better, able to operate autonomously for months) so as to be immune from this bias? Can it be done?

6Seth Herd
Thank you. Edit: I dashed off a response on short time and missed the important bit of the question. See my response below for the real answer. What an oddly off-topic but perfect question. As it happens, that's something I've thought about a lot. Here's the old version: Capabilities and alignment of LLM cognitive architectures And how to align it: Internal independent review for language model agent alignment These are both older versions. I was worried about pushing capabilities at the time, but progress has been going that direction anyway, so I'm working on updated versions that are clearer. I've been catching up on your recent work in the past couple of weeks; it seems on-target for my projected path to AGI.
4Daniel Kokotajlo
Unimportant: I don't think it's off-topic, because it's secretly a way of asking you to explain your model of why confirmation bias happens more and prove that your brain-inspired model is meaningful by describing a cognitive architecture that doesn't have that bias (or explaining why such an architecture is not possible). ;) Thanks for the links! On brief skim they don't seem to be talking much about cognitive biases. Can you spell it out here how the bureaucracy/LMP of LMA's you describe could be set up to avoid motivated reasoning?
2Seth Herd
I apologize. Those links don't answer the question at all. I dashed off an answer in the middle of a social occasion and completely missed the most relevant piece of your question - quite possibly motivated reasoning led me to assume it was on my favorite topic, whether language model agents will be our first AGIs and how to align them. Anyway, here's my answer to your actual question. This isn't something I've thought about before, because it's not clear it will play a central role in alignment. (I'm curious if you see a more direct link to alignment than I'm seeing) In sum, they'll probably have some MR and CB; they can use the same strategies as humans can to correct them, but more reliably if it can be included in scripted prompts as part of the scaffolding that makes the LLMs into cognitive architectures (or real AGI). That's basically to notice when you're in a situation that would cause MR or CB, and do some extra cognitive work to counteract it. Language model AGI will probably have MR, but less than humans: Language model agents won't have as much motivated reasoning as humans do, because they're not probably going to use the same very rough estimated-value-maximization decision-making algorithm. (this is probably good for alignment; they're not maximizing anything, at least directly. They are almost oracle-based agents). But they may have a substantial amount of MR, to the extent that language encodes linked beliefs that were created by humans with lots of MR. I wonder if anyone has run tests that could indicate how much MR or CB they have, if that can somehow be disentangled from sycophancy. And they will probably have some amount of confirmation bias, because language probably encodes the same sort of associative links that make it easier to think of confirming evidence than disconfirming. How LMAs could correct for MR and CB (imperfectly but better-than-human - at a cost): Like  humans, they could correct for MR and CB by doing some compensatory
6Daniel Kokotajlo
no need to apologize, thanks for this answer! Question: Wouldn't these imperfect bias-corrections for LMA's also work similarly well for humans? E.g. humans could have a 'prompt' written on their desk that says "Now, make sure you spend 10min thinking about evidence against as well..." There are reasons why this doesn't work so well in practice for humans (though it does help); might similar reasons apply to LMAs? What's your argument that the situation will be substantially better for LMAs? I'm particularly interested in elaboration on this bit:
4Seth Herd
I think there is an important reason things are different for LMAs than humans: you can program in a check for whether it's worth correcting for motivated reasoning. Humans have to care enough to develop a habit (including creating reminders they'll actually mind). Whether a real AGI LMA would want to remove that scripted anti-bias part of their "artificial conscience" is a fascinating question; I think it could go either way, with them identifying it as a valued part of themselves, or an external check limiting their freedom of thought (same logic applies to internal alignment checks). This also would substitute for a motivation that humans mostly don't have. People, particularly non-rationalists, just aren't trying very hard to arrive at the truth - because taking the effort to do that doesn't serve their perceived interests.  Most often, humans don't even want to correct for motivated reasoning. Firmly believing the same things as their friends and family serves them. In important life decisions, they can benefit by countering MR and CB. I just added Staring into the abyss as a core life skill to the footnote, since it seems to be about exactly that. Spending an extra ten minutes thinking about the counterevidence is usually a huge waste of time - unless you hugely value reaching correct conclusions on abstract matters that are likely irrelevant to your life success (I expect you do, and I do too - but it's not hard to see why that's a minority position). Finally, there is no common knowledge of how big a problem MR/CB is, or how one might correct them.  I couldn't find any study where they told people "try to compensate for this bias", at least as of ~8 years ago when I was actively researching this.    Oracle-based agent is a term I'm toying with to intuitively capture how a language model agent still isn't directly motivated by RL based on a goal. They are trained to have an accurate world model, and largely to answer questions as they were intended (
4Daniel Kokotajlo
This is helping, thanks. I do buy that something like this would help reduce the biases to some significant extent probably. Will the overall system be trained? Presumably it will be. So, won't that create a tension/pressure, whereby the explicit structure prompting it to avoid cognitive biases will be hurting performance according to the training signal? (If instead it helped performance, then shouldn't a version of it evolve naturally in the weights?)
2Seth Herd
I'm not at all sure the overall system will be trained. Interesting that you seem to expect that with some confidence. I'd expect the checks for cognitive biases to only call for extra cognition when a correct answer is particularly important to completing the task at hand. As such, it shouldn't decrease performance much. But I'm really not sure that training the overall system end-to-end is going to play a role. The success and relatively faithful CoT from r1 and QwQ give me hope that end-to-end training won't be very useful. Certainly people will try end-to-end training, but given the high compute cost for long-horizon tasks, I don't think that's going to play as large a role as piecewise and therefore fairly goal-agnostic training. I think humans' long-horizon performance isn't mostly based on RL training, but our ability to reason and to learn important principles (some from direct success/failure at LTH tasks, some from vicarious experience or advice). So I expect the type of CoT RL training used in o1 to be used, as well as extensions to general reasoning where there's not a perfectly check-able correct answer. That allows good System 2 reasoning performance, which I think is the biggerst basis of humans' ability to perform useful LTH tasks. Combining that with some form of continuous learning (either better episodic memory than vector databases and/or fine-tuning for facts/skills judged as useful) seems like all we need to get to human level. Probably there will be some end-to-end performance RL, but that will still be mixed with strong contributions from reasoning about how to achieve a user-defined goal. Gauging how much goal-directed RL is too much isn't an ideal situation to be in, but it seems like if there's not too much, instruction-following alignment will work. WRT to cognitive biases, end-to-end training would increase some desired biases while decreasing some that are hurting performance (sometimes correct answers are very useful). MR as h
5Daniel Kokotajlo
Huh, isn't this exactly backwards? Presumably r1 and QwQ got that way due to lots of end-to-end training. They aren't LMPs/bureaucracies. ...reading onward I don't think we disagree much about what the architecture will look like though. It sounds like you agree that probably there'll be some amount of end-to-end training and the question is how much? My curiosity stems from: 1. Generic curiosity about how minds work. It's an important and interesting topic and MR is a bias that we've observed empirically but don't have a mechanistic story for why the structure of the mind causes that bias -- at least, I don't have such a story but it seems like you do! 2. Hope that we could build significantly more rational AI agents in the near future, prior to the singularity, which could then e.g. participate in massive liquid virtual prediction markets and improve human collective epistemics greatly.
2Seth Herd
Ah- now now I see how we're using end-to-end training a little differently. I'm thinking of end-to-end training as training a CoT, as in o1 etc, AND training an outer-loop control scheme for organizing complex plans and tasks. That outer-loop training is what I'm not sure will be helpful. You can script roughly how humans solve complex tasks, I think in a way that will help and not get in the way of the considerable power of the trained CoT. So I still expect this to play a role; I just don't know how big a role it will play. I agree that training CoT will be done, and has important implications for alignment, particularly in making CoTs opaque or unfaithful. Reason 1: makes sense. One reason I never got around to publishing my work in comprehensible form is that it's just adding mechanistic detail to an earlier essentially-correct theory that it's just the result of applying self-interest (or equivalently, RL) to decisions about beliefs, in the context of our cognitive limitations. I'll try to dig up the reference when I get a little time. Reason 2: Interesting. I do hope we can align early agents well enough to get nontrivial help from them in solving the remaining alignment problems, both for AGI agents and for the power structures that are deploying and controlling them. Biases in their thinking have been less of a concern for me than their capabilities, but they will play a role. Sycophancy in particular seems like the language model equivalent of human motivated reasoning, and it could really wreck agents' usefulness in making their user's/controllers actions more sane.

Here the two definitions of rationality diverge: believing the truth is now at odds with doing what works. It will obviously work better to believe what your friends and neighbors believe, so you won't be in arguments with them and they'll support you more when you need it.

 

This is only true if you can't figure out how to handle disagreements.

It will often be better to have wrong beliefs if it keeps you from acting on the even wronger belief that you must argue with everyone who disagrees. It's better yet to believe the truth on both fronts, and simpl... (read more)

The only remedy I know of is to cultivate enjoying being wrong. This involves giving up a good bit of one's self-concept as a highly intelligent individual. This gets easier if you remember that everyone else is also doing their thinking with a monkey brain that can barely chin itself on rationality.

 

Some thoughts:

I have less trouble with this than most, and the areas where I do notice it arising lead me toward an interesting speculation.

I'm status blind: I very rarely, and mostly only when I was much younger, worry about looking like an idiot/failing... (read more)

To add to that, Oeberst (2023) argues that all cognitive biases at heart are just confirmation bias based around a few "fundamental prior" beliefs. (A "belief" would be a hypothesis about the world bundled with an accuracy.) The fundamental beliefs are:

  • My experience is a reasonable reference
  • I make correct assessments of the world
  • I am good
  • My group is a reasonable reference
  • My group (members) is (are) good
  • People's attributes (not context) shape outcomes

That is obviously rather speculative, but I think it's some further weak reason to think motivated reasoning... (read more)

One problematic aspect is that it's often easier to avoid motivated reasoning when the stakes are low. Even if you manage to avoid it in 95% of the cases, if th remaining 5% are there what really matters you are still overall screwed.

1Seth Herd
Good point. Alignment theory and AGI prediction spring to mind again; there it's not just our self-concepts at stake, but the literal fate of the world.
[-]HNX30

A bit of a pushback, if I may: confirmation bias/motivated reasoning themselves only arise because of an inherent, deep-seated, [fairly likely] genetically conditioned, if not unconscious sense that:

A. there is, in fact, a single source of ground truth even, if not especially, outside of regular, axiomatic, bottom-up, abstract, formalized representation: be it math [+] or politics [-]

B. it is, in fact, both viable and desirable, to affiliate yourself with any one/number of groups, whose culture/perspective/approach/outlook must fully represent the A: inste... (read more)

David Gross

220

The fundamental attribution error is another important one. I sometimes find I slip into it myself when I get tired or inattentive, but for most people I observe it seems fully baked into their characters.

Gordon Seidoh Worley

112

The Typical Mind Fallacy is the most important bias in human reasoning.

How do I know? Because it's the one I struggle with the most!

robo

72

Conjunction Fallacy.  Adding detail make ideas feel more realistic, and strictly less likely to be true.

Virtues for communication and thought can be diametrically opposed.

romeostevensit

30

Is-ought confabulation

Means-ends confabulation

Scope sensitivity

Fundamental attribution error

Attribute substitution

Ambiguity aversion

Reasoning from consequences

3 comments, sorted by Click to highlight new comments since:

I think most of these are "secretly adaptive/reasonable" in certain contexts.

  • Fundamental Attribution Error: Reduces computational load when predicting the behavior of strangers in short interactions.

  • Conjunction Fallacy: It's harder to tell a complex lie without getting caught, so complexity is evidence for honesty.

[-]kave10

What do you mean by A?

Edited it to be less pointlessly poetic; hopefully the new version is less ambiguous. Ty!