When formalized, causal relationships are usually formalized as a directed acyclic graph from parent events to child events saying how to compute the probable child given the state of its parents.

Local introductions

• Causal Diagrams and Causal Models
• Causality: A Brief Introduction

External introductions

• The Book of Why [Amazon]
• An Introduction to Causal Inference

You may want to look at the Founder Toolkit which will probably be more up-to-date.

Inkhaven is a 30-day residency where one has to publish posts every day.day, as part of an effort to grow stronger as a writer. While this likely helps one in the longer term, the shorter-term effect ishas produced some excellent posts it also produces a fair bit of noise too, and also many more likely creation ofhastily-written or experimental posts with less effort to doublecheck the arguments and, as a result, with epistemic problems. than usual.

Inkhaven-like posts emerge when other people try to imitate this manner on a smaller scale (e.g. Lightcone team members doing their own 1-week writing stints)stints, or 'HalfHaven' where remote LessWrongers aim to post 30 posts over the course of two months).

In doxastic modal logic, the statement "P is a hyperstition" is written as □P→□P→P. Modal reasoners that satisfy Löb's Theorem believe all personal hyperstitions. This can cause some problems for modal embedded agents. Löbian cooperation works by making mutual cooperation a collective hyperstition.

A reasoning step is "logically valid" when that kind of step never produces a false conclusion from true premises. For example, in algebra, "Add 2 to both sides of the equation" is valid because it only produces true equations from true equations, while "Divide both sides by x" is invalid because x might be 0. So even if "2x = (y+1)x", letting x = 0 and y = 2, the original equation can be true while "2 = y + 1" is false. But "2x + 2 = (y+1)x + 2" will be true in every semantic model where the original equation is true.

More generally in life, there's a question of "did you execute each local step of reasoning correctly", which can be considered apart from "did you arrive at the correct conclusion". Validity is a local property of a reasoning step or sequence; we can (and should) evaluate each step's validity separately from whether we agree with the premises or end up agreeing with the conclusion. For near-logical domains, this asks "Does the next proposition follow (with very high probability, given other things usually believed about the world or explicitly introduced as premises) from the previous proposition?" For probabilistic reasoning, informal validity asks, "Given everything else believed or introduced as a premise, is this next step adjusting probabilities by the right amount?" or "Does this kind of reasoning step in general produce well-calibrated conclusions from well-calibrated premises?"

Eg, consider why the ad hominem fallacy should be seen as "invalid" or a "locally invalid reasoning step" from this viewpoint. Suppose you start out with well-calibrated probabilities (things you say "60%" for, happen around 60% of the time). You assign 60% probability that the sky is blue. Then somebody says, "Yeah, well, people who believe in blueskyism are ugly" and you nod and adjust your credence in blueskyism down to 40%. Your odds just went from 3:2 to 2:3, so by Bayes's Rule you should've heard evidence with a likelihood ratio of 4:9 to produce that probability shift. Unless you already believe that false propositions are 225% as likely as true propositions to be believed by ugly people, you should already expect that believing an ad hominem argument is something that can produce ill-calibrated conclusions in expectation from well-calibrated premises.

Main articles:

• Local Validity as a Key to Sanity and Civilization.
• Proofs, Implications, and Models.

Scalable oversight is an approach to AI control ^[1]in which AIs supervise each other.the problem of providing reliable supervision of outputs from AIs, even as they become smarter than humans. Often groups of weaker AIs supervise a stronger AI, or AIs are set in a zero-sum interactiondebate with each other.

1. ^{^}

   People used to refer to scalable oversight as a set of AI alignment techniques, but they usually work on the level of incentives to the AIs, and have less to do with architecture.

Focuses on the intersection of frontier AI agents and traditional infrastructure security, including exploit detection, system persistence, and hardware-level attributability

• Simulacrum Level 1: Attempt to describe the world accurately.
• Simulacrum Level 2: Choose what to say based on what your statement will cause other people to do or believe.
• Simulacrum Level 3: Say things that signal true things, such as membership to your ingroup.
• Simulacrum Level 4: Choose which groupthings to signal membership to based on what the benefit would be for you.

By Ruthenis (summarized; includes level 0):

• Level 0: Action in base reality, purely for its immediate effects. No communication at all.
• Level 1: Literal statements about reality, to communicate models of ground truth. Straightforward communication.
• Level 2: Bullshit statements about reality, to manipulate others' models of ground truth. Straightforward deception. In late stage level 2, statements are weapons and beliefs are the contested territory. Literal truth becomes irrelevant.
• Level 3: Statements made to signal that one is the kind of person that makes such a statement. No longer attempts to modify others' models of base reality. Statements are clothing and applause lights. No one cares about literal truth, or even what people believe about literal truth. In late stage level 3, people start to realize this, and then...
• Level 4: The recursion limit has been reached, and everything collapses. Statements are only made for their social effects, which have become completely uncorrelated with any notion that even pretends to pretend to be ground truth. Symbols have lost all meaning except the immediate effects of using them. In many ways, level 4 is similar to level 0.

Inkhaven is a 30-day residency where one has to publish posts every day. While this likely helps one in the longer term, the shorter-term effect is a more likely creation of posts with less effort to doublecheck the arguments and, as a result, with epistemic problems.

Inkhaven-like posts emerge when other people try to imitate this manner on a smaller scale (e.g. Lightcone team members doing their own 1-week writing stints).

The main problems with CEV include, firstly, the great difficulty of implementing such a program - “If one attempted to write an ordinary computer program using ordinary computer programming skills, the task would be a thousand lightyears beyond hopeless.” Secondly, the possibility that human values may not converge. Yudkowsky considered CEV obsolete almost immediately after its publication in 2004. He states that there's a "principled distinction between discussing CEV as an initial dynamic of Friendliness, and discussing CEV as a Nice Place to Live" and his essay was essentially conflating the two definitions.

ATOW (2025-09-09)(2026-04-03), nothing has been published that claimMoore et al. (2026) is probably the best academic account of LLM-Induced Psychosis (LIP) is a definite, real, phenomena. Though, many anecdotal accounts exist. It is not yet clear, if LIP is caused by AIs, if pre-existing disillusion are 'sped up' or reinforced by interactinginduced psychosis. They "analyze logs of conversations with an AI, or, if LIP exists at all.LLM chatbots from 19 users who report having experienced psychological harms from chatbot use" where the users mostly came from " support group for such chatbot users."

ML4Good is a France-based field-building organisation that runs AI Safety bootcamps.

Summaries of discussions, takeaways, etc. from LessWrong meetups that have already taken place.

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