Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Slowing down AI progress is an underexplored alignment strategy, published by Norman Borlaug on July 12, 2022 on LessWrong.

The emotional burden of watching the world end

My current beliefs about AI timelines have made my life significantly worse. I find thoughts about the ever-shrinking timeline to AGI invading nearly every aspect of my life. Every choice now seems to be affected by the trajectory of this horrible technology.

Based on the responses in this post and others, many if not most people on this forum have been affected in a similar way. AI is the black hole swallowing all other considerations about the future.

Frankly, I feel a good deal of anger about the current state of affairs. All these otherwise thoughtful and nice people are working to build potentially world-ending technology as fast as possible. I'm angry so few people are paying any attention to the danger. I'm angry that invasive thoughts about AGI destroying the world are making it harder for me to focus on work that might have a non-zero chance of saving it. And I'm frustrated we've been so closed-minded in our approach to solving this problem.

The current situation is almost exactly analogous to the creation of the atomic bomb during World War 2. There's a bunch of very smart people gathered together, supplied with a fuckton of money by powerful groups, working towards the same destructive goal. There is social proof all around them that they are doing the right thing. There are superficially plausible reasons to think that everything will turn out fine. There is token engagement with the concerns raised by people concerned about the implications of the technology. But at the end of the day, the combination of personal incentives, social proof and outside impetus make everyone turn their head and ignore the danger. On the rare occasion that people are convinced to leave, it's almost always the most conscientious, most cautious people, ensuring the remaining team is even less careful.

There is so much magical thinking going on among otherwise intelligent people. Everyone seems to be operating on the assumption that no technology can destroy us, that everything will magically turn out fine, despite the numerous historical examples of new inventions destroying or nearly destroying the world (see the Cuban Missile Crisis or the great oxidation event when oxygen-producing bacteria extincted themselves and most other life on earth and caused a 300 million year ice age).

Frankly I've found the response from the EA/rationalist community has been pretty lackluster so far. Every serious solution that has been proposed revolves around solving alignment before we make AGI, yet I know ZERO people who are working on slowing down capabilities progress. Hell, until just a month ago, EA orgs like 80,000 hours were RECOMMENDING people join AI research labs and work on CREATING superintelligence.

The justifications I've read for this behavior always seem to be along the lines of "we don't want to alienate the people working at top AI orgs because we feel that will be counter-productive to our goals of convincing them that AI alignment is important." Where has this strategy gotten us? Does the current strategy of getting a couple of members of the EA/Rationalist community onto the safety teams at major AI orgs actually have a chance at working? And is it worth foregoing all efforts to slow down progress towards AGI?

The goals of DeepMind, OpenAI, and all the other top research labs are fundamentally opposed to the goal of alignment. The founding goal of Deepmind is to "solve intelligence, then use that to solve everything else." That mission statement has been operationalized as paying a bunch of extremely smart people ridiculous salaries to create and distribute the blueprints for (potentially) world-ending AGI...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: AI ethics: the case for including animals (my first published paper, Peter Singer's first on AI), published by Fai on July 12, 2022 on The Effective Altruism Forum.

Hi! I am Tse Yip Fai. This is a linkpost to my first published paper (second author). Since the paper is open access, I won't be doing any copy-and-paste here.

This is a seminal paper resulting from an one year research period. We hope that this paper can break the ground and raise awareness in the field of AI ethics so that they start caring about impact on animals. This, I believe, is not just relevant to the AI ethics field, but also to the AI alignment field.

If you prefer videos, here's a talk by Professory Singer and myself on the same topic.

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Response to Blake Richards: AGI, generality, alignment, & loss functions, published by Steve Byrnes on July 12, 2022 on The AI Alignment Forum.

Blake Richards is a neuroscientist / AI researcher with appointments at McGill & MiLA. Much of his recent work has involved making connections between machine learning algorithms and the operating principles of the cortex and hippocampus, including theorizing about how the neocortex might accomplish something functionally similar to backprop. (Backprop itself is not biologically plausible.) I have read lots of his papers; they're always very interesting!

Anyway, Blake recently gave a nice interview on Michaël Trazzi’s podcast “The Inside View”. Episode and transcript at:.

Blake said a lot of things in the interview that I wholeheartedly agree with; for example, I would echo pretty much everything Blake said about scaling. But Blake & I strongly part ways on the subject of (what I call) artificial general intelligence (AGI). (Blake doesn’t like the term “AGI”, more on which below.) So that’s what I’ll talk about in this post.

1. Two mental images for thinking about an AI. Or: My lips say “human-level”, but my heart says “a fancier version of GPT or AlphaFold”

I’ll jump right into a key issue that I think is lurking below the surface. Here are two mental images for thinking about an AI system (I’ll be returning to this table throughout the post)

Throughout the interview, even while Blake was saying things like “as general as a human”, I got a strong feeling that what he was imagining was the thing in the left column. But if it was actually as general as a human, it would look like the thing in the right column. For example, if an AI is actually as general as a (very competent) human, and you ask it to fold proteins, and it doesn’t know how, then it downloads AlphaFold. Or if AlphaFold doesn’t exist, it starts reading papers and textbooks, and goes about inventing AlphaFold!

(In Section 3 below, I’ll go over an example of one of the many things Blake said, in the interview, that gave me the strong impression that he was engaging in left-column thinking. If I’m wrong, then so much the better—we can just skip to the next section!)

Anyway, left-column thinking is extremely natural, because that’s what ML looks like today. But we know that it’s technically possible to make AIs that require right-column thinking—Blake and I are in full agreement that anything the human brain can do, an AI can do too.

A further question is, even if the right-column AIs are technically possible, will humans actually create them? It seems to me that we should have quite high credence on “Yes”. After all, how do you prevent anyone, anywhere on earth, from solving a scientific problem and implementing the corresponding algorithm, forever and ever?? Remember when the USA tried to ban people from talking about a certain algorithm? It was not a permanent solution, to say the least! And of course, Blake himself is one of the many people devoting their professional lives to knocking down the technical barriers that prevent us from building right-column AIs.

Thus, it seems to me that we will, sooner or later, have right-column AIs. And those future AIs are the thing that I want to talk about in this post.

2. We need a term for the right-column thing, and “Artificial General Intelligence” (AGI) seems about as good as any.

That brings us to the term "AGI" for talking about the thing in the right column of the table above. Blake (like Yann LeCun) does not like the term “AGI”, but I’m going to use it anyway in this post, and here I’ll explain why.

The thing I feel strongest about is that we need some term—any term—for the thing in the right column. Because it’s a thing we need to talk about. I don’t feel very strongly that this term has to be “AGI” in particular. B...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Mosaic and Palimpsests: Two Shapes of Research, published by Adam Shimi on July 12, 2022 on The AI Alignment Forum.

This work has been done while working at Conjecture.

Introduction

In Old Masters and Young Geniuses, economist-turned-art-data-analyst David Galenson investigates a striking regularity in the careers of painters: art history and markets favors either their early pieces or the complete opposite — their last ones. From this pattern and additional data, Galenson extracts and defends a separation of creatives into two categories, two extremes of a spectrum: conceptual innovators (remembered as young geniuses) and experimental innovators (remembered as old masters).

Conceptual innovators, like painter Pablo Picasso, start with a crystal clear idea of their goal, and spend tremendous amounts of time in preliminary research, preparatory drawing and all-around planning. They then mostly stick to these extensive plans (or have them executed by others) when concretely creating the output. As such, their most impressive innovations generally come from their ground breaking ideas, when they know and have done so little that they can simplify and break all the rules they haven't yet learned — they're young geniuses. Galenson provides additional examples: artist Andy Warhol, novelist Herman Melville, movie director Orson Welles, scientist Albert Einstein...

On the other hand, experimental innovators, like painter Paul Cézanne, only figure out their aim by relentless trial-and-error, making something up and then iterating on it. Their intuitions start vague and their goals cloudy, leading to their perpetual uncertainty and doubt about having accomplished what they wanted. Yet because experimental innovators keep on refining their attempts, and because they build on all that happened before, their best output (measured by metrics like auction prices, bestselling list, mentions in textbooks) emerges towards the end of their lives — they're old masters. Some additional examples given by Galensen are: abstract painter Jackson Pollock, novelist Virginia Woolf, movie director Alfred Hitchcock, and scientist Charles Darwin.

Galenson's distinction jolted my mind and turned it racing with questions: in which box did I fall? And others in alignment? What were the consequences for accelerating science in general and alignment in particular? What are the limits of the distinction?

Unfortunately, the painter-based distinction proved unwieldy to analyze conceptual research, my main application. Yet there was definitely something there...

This post thus follows my process in carving a frame out of the old master/young genius template which can be applied to heady topics like alignment and epistemology, and illuminates both my own mistakes with regard to my work, and the different styles of alignment researchers. My frame applies to research outputs rather than to researchers, and separate them into mosaics (works which fit within a clear, simple, trimmed down structure, like colored tiles in a mosaic), and palimpsests (works which iterate on previous idea by shifting and altering them, rewriting on top like a palimpsest)

Epistemic status: Like most of my work, this post fits into a palimpsest rather than a mosaic (see definitions below). As such, it is but one step of an iterative process. I currently agree with it; I will find it limited or inadequate soon, and will probably build something new on top (or with its ashes).

Issues with Conceptual and Experimental Innovators

Going back to Galenson's conceptual and experimental innovators for a moment — what was the problem? For my purposes, I had to confront three issues.

(Too anchored in art) Because Galenson discovered his pattern in painting, it embeds part of the art analysis ontology. The main problematic example is the differenc...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: On how various plans miss the hard bits of the alignment challenge, published by So8res on July 12, 2022 on The Effective Altruism Forum.

(As usual, this post was written by Nate Soares with some help and editing from Rob Bensinger.)

In my last post, I described a “hard bit” of the challenge of aligning AGI—the sharp left turn that comes when your system slides into the “AGI” capabilities well, the fact that alignment doesn’t generalize similarly well at this turn, and the fact that this turn seems likely to break a bunch of your existing alignment properties.

Here, I want to briefly discuss a variety of current research proposals in the field, to explain why I think this problem is currently neglected.

I also want to mention research proposals that do strike me as having some promise, or that strike me as adjacent to promising approaches.

Before getting into that, let me be very explicit about three points:

On my model, solutions to how capabilities generalize further than alignment are necessary but not sufficient. There is dignity in attacking a variety of other real problems, and I endorse that practice.

The imaginary versions of people in the dialogs below are not the same as the people themselves. I'm probably misunderstanding the various proposals in important ways, and/or rounding them to stupider versions of themselves along some important dimensions. If I've misrepresented your view, I apologize.

I do not subscribe to the Copenhagen interpretation of ethics wherein someone who takes a bad swing at the problem (or takes a swing at a different problem) is more culpable for civilization's failure than someone who never takes a swing at all. Everyone whose plans I discuss below is highly commendable, laudable, and virtuous by my accounting.

Also, many of the plans I touch upon below are not being given the depth of response that I'd ideally be able to give them, and I apologize for not engaging with their authors in significantly more depth first. I’ll be especially cursory in my discussion of some MIRI researchers and research associates like Vanessa Kosoy and Scott Garrabrant.

In this document I'm attempting to summarize my high-level view of the approaches I know about; I'm not attempting to provide full arguments for why I think particular approaches are more or less promising.

Think of the below as a window into my thought process, rather than an attempt to state or justify my entire background view. And obviously, if you disagree with my thoughts, I welcome objections.

So, without further ado, I’ll explain why I think that the larger field is basically not working on this particular hard problem:

Reactions to specific plans

Owen Cotton-Barratt & Truthful AI

Imaginary, possibly-mischaracterized-by-Nate version of Owen: What if we train our AGIs to be truthful? If our AGIs were generally truthful, we could just ask them if they're plotting to be deceptive, and if so how to fix it, and we could do these things early in ways that help us nip the problems in the bud before they fester, and so on and so forth.

Even if that particular idea doesn't work, it seems like our lives are a lot easier insofar as the AGI is truthful.

Nate: "Truthfulness" sure does sound like a nice property for our AGIs to have. But how do you get it in there? And how do you keep it in there, after that sharp left turn? If this idea is to make any progress on the hard problem we're discussing, it would have to come from some property of "truthfulness" that makes it more likely than other desirable properties to survive the great generalization of capabilities.

Like, even simpler than the problem of an AGI that puts two identical strawberries on a plate and does nothing else, is the problem of an AGI that turns as much of the universe as possible into diamonds. This is easier because, whi...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: On how various plans miss the hard bits of the alignment challenge, published by So8res on July 12, 2022 on LessWrong.

(As usual, this post was written by Nate Soares with some help and editing from Rob Bensinger.)

In my last post, I described a “hard bit” of the challenge of aligning AGI—the sharp left turn that comes when your system slides into the “AGI” capabilities well, the fact that alignment doesn’t generalize similarly well at this turn, and the fact that this turn seems likely to break a bunch of your existing alignment properties.

Here, I want to briefly discuss a variety of current research proposals in the field, to explain why I think this problem is currently neglected.

I also want to mention research proposals that do strike me as having some promise, or that strike me as adjacent to promising approaches.

Before getting into that, let me be very explicit about three points:

On my model, solutions to how capabilities generalize further than alignment are necessary but not sufficient. There is dignity in attacking a variety of other real problems, and I endorse that practice.

The imaginary versions of people in the dialogs below are not the same as the people themselves. I'm probably misunderstanding the various proposals in important ways, and/or rounding them to stupider versions of themselves along some important dimensions. If I've misrepresented your view, I apologize.

I do not subscribe to the Copenhagen interpretation of ethics wherein someone who takes a bad swing at the problem (or takes a swing at a different problem) is more culpable for civilization's failure than someone who never takes a swing at all. Everyone whose plans I discuss below is highly commendable, laudable, and virtuous by my accounting.

Also, many of the plans I touch upon below are not being given the depth of response that I'd ideally be able to give them, and I apologize for not engaging with their authors in significantly more depth first. I’ll be especially cursory in my discussion of some MIRI researchers and research associates like Vanessa Kosoy and Scott Garrabrant.

In this document I'm attempting to summarize my high-level view of the approaches I know about; I'm not attempting to provide full arguments for why I think particular approaches are more or less promising.

Think of the below as a window into my thought process, rather than an attempt to state or justify my entire background view. And obviously, if you disagree with my thoughts, I welcome objections.

So, without further ado, I’ll explain why I think that the larger field is basically not working on this particular hard problem:

Reactions to specific plans

Owen Cotton-Barratt & Truthful AI

Imaginary, possibly-mischaracterized-by-Nate version of Owen: What if we train our AGIs to be truthful? If our AGIs were generally truthful, we could just ask them if they're plotting to be deceptive, and if so how to fix it, and we could do these things early in ways that help us nip the problems in the bud before they fester, and so on and so forth.

Even if that particular idea doesn't work, it seems like our lives are a lot easier insofar as the AGI is truthful.

Nate: "Truthfulness" sure does sound like a nice property for our AGIs to have. But how do you get it in there? And how do you keep it in there, after that sharp left turn? If this idea is to make any progress on the hard problem we're discussing, it would have to come from some property of "truthfulness" that makes it more likely than other desirable properties to survive the great generalization of capabilities.

Like, even simpler than the problem of an AGI that puts two identical strawberries on a plate and does nothing else, is the problem of an AGI that turns as much of the universe as possible into diamonds. This is easier because, while it still require...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Explore the new UN demographic projections to 2100, published by EdMathieu on July 11, 2022 on The Effective Altruism Forum.

The UN releases an update of its World Population Prospects every two years. Its latest release was due in 2021 but was delayed as a result of the pandemic. But today, the long-awaited dataset has been released.

With early access to this new UN data, we have published a new Population and Demography Data Explorer on Our World in Data, where you can explore this full dataset in detail.

To complement this data explorer, we've also published a post on the Five key findings from the 2022 UN Population Prospects.

All metrics are available:

For every country in the world, continent, and World Bank income group

With projection scenarios up to the year 2100

Broken down by sex and age group

Here's the full list of metrics:

Population

Age structure

Population density

Births (absolute & rate)

Deaths (absolute & rate / all ages, child, infant)

Life expectancy

Migration

Sex ratio

Dependency ratio

Median age

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: New ideas for mitigating biotechnology misuse, published by jbs on July 11, 2022 on The Effective Altruism Forum.

Jonas Sandbrink

Mitigating the misuse and proliferation of dangerous biotechnology capabilities is a crucial part of our biosecurity strategy, especially until systems like robust pathogen detection and super PPE are in place. However, preventing the misuse of biotechnology can be tricky to work on due to the risk of drawing attention to the very things we fear. Andrew Snyder-Beattie and Ethan Alley mention strengthening the biological weapons convention as one such project in their Concrete Biosecurity Projects post.

Here, I present some new (more or less) concrete project ideas in this space that seem good and which have not been explored substantially. These should be imagined as standing next to core risk mitigation efforts that are already receiving attention, including strengthening the biological weapons convention, DNA synthesis screening, and Genetic Engineering Attribution.

If you are keen to work on any of the projects below, please register your interest through this Google Form.

1. Record-keeping for strong attribution

In most places, if you buy a gun, its serial number is registered. This enables law enforcement to link guns used in crimes to their owners and thus deter their misuse. We should create a similar system for attributing biological agents - agents that also have the potential to kill people - to their creators. This may be achieved through keeping records of the genetic sequences of organisms that scientists work on.

Record-keeping could take place at the DNA synthesis or sequencing stage. For instance, a record-keeping module could be introduced together with DNA synthesis screening into all DNA synthesis machines. Recorded DNA sequences would be encrypted (potentially hashed) to protect intellectual property (IP) and stored in a way accessible to later investigation. Once an unusual outbreak occurs, the sequence for the causative agent could be similarly encrypted and screened against the encrypted DNA records from facilities. Matches would result in a flag of facilities that have worked on the agent in question, which could automatically trigger an inspection. I call this system Retrospective Encrypted Corroboration Of Recorded DNA Sequences (RECORDS), but you might come up with something better.

A record-based system for strong attribution of biological agents may be a powerful mechanism to deter biological weapons development and use. Thus, such a system could feature as part of a future BWC compliance regime. Routine visits as part of such a compliance regime could check for active record-keeping. For instance, microbial samples collected in the laboratory could be sequenced, encrypted, and checked against facility records for gaps. Lastly, functional strong attribution could be used to identify accidental laboratory releases - it could have provided important positive or negative evidence in the COVID-19 origins debate.

A more simple initial alternative to a RECORDS system might be to create a commitment of DNA synthesis companies to cross-check existing records in the case of an unusual outbreak. For instance, members of the International Gene Synthesis Consortium keep order and customer records for 8 years which could be tapped into.

There are many technical, economic, and political challenges to making record-keeping workable. Individuals familiar with DNA synthesis and sequencing, cryptography, blockchain developers, and social studies might be able to contribute here by doing initial scoping and expanding on this idea and how to put it into practice.

2. Responsible access to genetic sequences

In most places, to buy a gun, you have to have a license. Such a licence certifies your need to have a gun and a clean criminal...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Marriage, the Giving What We Can Pledge, and the damage caused by vague public commitments, published by Jeffrey Ladish on July 11, 2022 on The Effective Altruism Forum.

I believe honesty is very important. I think most people agree that honesty is like, pretty important, but I think it’s a lot more important than that. I basically think that people will be dishonest in ways that hurt them and others by default, even when they’re trying to be pretty honest, because I think it’s just that hard. I think it’s hard because there are a lot of incentives that push away from honesty. E.g. You want the job so you’re tempted to overstate your experience or past performance. You said you wouldn’t tell anyone about your friend’s secret, but this seems like a situation where they wouldn’t mind, and it would be pretty awkward to say nothing.etc. There’s a huge variety of situations that incentivize small acts of dishonesty. And it’s not always clear whether something is a little dishonest or not - dishonesty can be quite a spectrum.

If I’m correct, and honesty is pretty hard by default, I think this is quite bad. Honesty is important because it greatly improves the ability of people to coordinate with each other. And it’s important because it allows people to reason better about themselves and about the world. Good coordination and good reasoning are things we badly need. Fortunately, I think many people could level up their honesty by putting in a reasonable amount of thinking and effort, and that a lot of the failure modes are caused by not paying attention to the incentives around them, or not thinking about how to structure their own lives and commitments to be more honest.If you want to be honest, it’s important to think about how to structure your life so being honest isn’t extremely difficult. This is especially true when it comes to promises and commitments. It’s often easier to be honest about your current beliefs than it is to be honest about what you’re going to do in the future.

After all, you don’t know what’s going to happen in the future. You can definitely influence it, and you can choose now to take particular actions in the future. But if you’re not careful, you might promise to do a thing in a week you think will be easy, and then find later the thing is extremely difficult or costly.At present there is an understanding that some kinds of commitments are much stronger and more serious than other kinds. One particularly strong example of a commitment or promise is a commitment you make in publicly, with people witnessing, e.g. marriage vows.The point of a public pledge is help structure our own incentives to fulfill our commitment. If you pledge to get married to someone privately, but then a couple years later someone really attractive comes along, it might be tempting to leave your partner for that other person. But if you have publicly married someone, you’re going to pay a lot of social costs for leaving your partner for someone else.

That’s a feature, not a bug; most people who get married want that incentive to stay together. They say aloud their vows in front of their friends for this reason.Unfortunately, I think the wider subculture I’m in has a pretty weak ability to hold people accountable to their commitments. I think people often are vague about the kinds of commitments they’re making publicly, and this is very bad for honesty. When people make public commitments but aren’t clear about how serious the commitments are, this weakens the ability of everyone to make public commitments.

Marriage as a public commitment

In part, the problem is that people have pretty different understandings of what public commitments mean. For example, Marriage is usually a somewhat-costly commitment witnessed by friends & family, in part to help hold the parties accounta...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Marriage, the Giving What We Can Pledge, and the damage caused by vague public commitments, published by Jeffrey Ladish on July 11, 2022 on LessWrong.

I believe honesty is very important. I think most people agree that honesty is like, pretty important, but I think it’s a lot more important than that. I basically think that people will be dishonest in ways that hurt them and others by default, even when they’re trying to be pretty honest, because I think it’s just that hard. I think it’s hard because there are a lot of incentives that push away from honesty. E.g. You want the job so you’re tempted to overstate your experience or past performance. You said you wouldn’t tell anyone about your friend’s secret, but this seems like a situation where they wouldn’t mind, and it would be pretty awkward to say nothing.etc. There’s a huge variety of situations that incentivize small acts of dishonesty. And it’s not always clear whether something is a little dishonest or not - dishonesty can be quite a spectrum.

If I’m correct, and honesty is pretty hard by default, I think this is quite bad. Honesty is important because it greatly improves the ability of people to coordinate with each other. And it’s important because it allows people to reason better about themselves and about the world. Good coordination and good reasoning are things we badly need. Fortunately, I think many people could level up their honesty by putting in a reasonable amount of thinking and effort, and that a lot of the failure modes are caused by not paying attention to the incentives around them, or not thinking about how to structure their own lives and commitments to be more honest.

If you want to be honest, it’s important to think about how to structure your life so being honest isn’t extremely difficult. This is especially true when it comes to promises and commitments. It’s often easier to be honest about your current beliefs than it is to be honest about what you’re going to do in the future. After all, you don’t know what’s going to happen in the future. You can definitely influence it, and you can choose now to take particular actions in the future. But if you’re not careful, you might promise to do a thing in a week you think will be easy, and then find later the thing is extremely difficult or costly.

At present there is an understanding that some kinds of commitments are much stronger and more serious than other kinds. One particularly strong example of a commitment or promise is a commitment you make in publicly, with people witnessing, e.g. marriage vows.

The point of a public pledge is help structure our own incentives to fulfill our commitment. If you pledge to get married to someone privately, but then a couple years later someone really attractive comes along, it might be tempting to leave your partner for that other person. But if you have publicly married someone, you’re going to pay a lot of social costs for leaving your partner for someone else. That’s a feature, not a bug; most people who get married want that incentive to stay together. They say aloud their vows in front of their friends for this reason.

Unfortunately, I think the wider subculture I’m in has a pretty weak ability to hold people accountable to their commitments. I think people often are vague about the kinds of commitments they’re making publicly, and this is very bad for honesty. When people make public commitments but aren’t clear about how serious the commitments are, this weakens the ability of everyone to make public commitments.

Marriage as a public commitment

In part, the problem is that people have pretty different understandings of what public commitments mean. For example, Marriage is usually a somewhat-costly commitment witnessed by friends & family, in part to help hold the parties accountable - to make i...