Here are four incredibly bad ideas (that are still commonly believed):

1) That people should be judged for, blamed for, or feel shame about the behavior of their ancestors.

Of course, if your ancestors did bad things, you should condemn those acts. And if you directly benefit from something bad your family member did, you should consider if you can make amends.

But, logically, people cannot be morally responsible for events that they were unable to affect.

This bad idea (that we are responsible for the behavior of our ancestors) is partly responsible for numerous cycles of violent conflict, as well as for unjust punishments and unhelpful guilt.

2) That small differences in group means are a good justification for jumping to conclusions about members.

This idea is partly responsible for a variety of forms of prejudice and harmful stereotyping.

Epistemically, a small mean difference provides only a sliver of evidence about individuals. Morally, it’s unjust to treat individuals as though only their group matters.

3) That there’s a simple explanation for all or most human behavior.

Variations on this idea have led to a great deal of confusion about human nature.

Human behavior is not all about sex, or self-interest, or happiness, or status, or individual survival, or even love.

Humans have a very diverse set of forces that influence behavior: emotions, culture, values, habits, instincts, heuristics, goals, etc.

4) That you need to follow a prescribed template in order to have a fulfilling life.

This idea has led to countless people being pressured into or trapped in lives that are bad for them.

Some people will be happiest as homemakers, others as professionals, and still others as adventurers, etc. Sometimes, the best life for a person will match the stereotype for their gender or group; other times, it will be the opposite, and at other times, it will seem completely out of left field.

Our boxes are much too small to force people’s lives into.

This piece was first written on July 5, 2024, and first appeared on my website on November 10, 2024.

The only approach I know of that I think has a meaningful shot to help solve such huge problems, which you might call “Scientific Entrepreneurship,” combines two methods into one:

(1) Rigorous science to deeply understand the causal structures of the problem and how strong an effect each cause has (which often will begin with a qualitative approach to understand the outlines of the problem and then move to analysis of carefully conducted measurements).   

(2) A “lean startup” approach, where you try things (guided by your current understanding of the causal relationships), see what happens, and then rapidly course-correct based on the results (and sometimes take large pivots) to iterate towards better and better approaches.

When dealing with these highly complex problems, I think that a “lean startup” approach without rigorous science ends up leading to lots of random attempts that have almost no chance of ultimate success (and sometimes even converge to self-propagating but useless approaches, such as charities that perpetually absorb money without genuinely helping the cause).  

On the other hand, a purely scientific approach without the lean startup iterative mindset often ends up missing critical contextual details that are actually essential for the project to work. You’ll inevitably encounter many specific barriers that the scientific theory doesn’t address. In summary, I think our best bet for solving highly complex world problems is Scientific Entrepreneurship: developing a deep understanding of the high-level causal structures through scientific rigor and then combining that with an entrepreneurial form of rapid iteration.

This piece was first written on May 5, 2024, and first appeared on my website on June 5, 2024.

Incompleteness Theorem 1 is about whether all mathematical truths can be proven.

The first incompleteness theorem says there will always be unprovable truths within every sufficiently powerful and non-contradictory mathematical system.

In order to prove these “unprovable” truths, you’d have to go outside that system into a bigger system, but that bigger system would then have its own unprovable truths as well, and to prove those, you’d need an even bigger system, and so on.

The exceptions are mathematical systems that you can’t use for arithmetic and mathematical systems that are self-contradictory (and therefore useless).

Why is this theorem true?

Step 1: Number all mathematical statements so that they can talk about each other

Gödel figured out how to assign a unique number to each statement in arithmetic. So, for instance, the statement “1+1=2” might be (just to make up a simple example) statement #1, and “4 < 5” might be statement #2 (realistically, these would be assigned huge numbers, but to keep things simple I’ll use small numbers).

Numbering all of the statements allows statements to make claims about each other in the language of arithmetic. For instance, maybe statement #3 says, “Statement #2 cannot be proven” (but it says it in the completely precise language of arithmetic, not in words – these statements are completely normal math, much like a typical mathematical statement you’d find in a math textbook, and so you’d expect each of these statements to either be true or false just like 1+1=2 is true and 1+1=3 is false).

Step 2: Create a self-referential statement

Gödel then considers a special statement, which he proved always will exist (say, for simplicity, that it’s statement #4). Statement #4 says, “Statement #4 *cannot* be proven in this system.” So, it refers to itself. In essence, in completely precise mathematics (rather than words), the statement says, referring to itself, “I cannot be proven.” That’s not the same as a statement that says “I am false” – being unprovable and being false are different, as we’ll see.

Step 3: Produce a contradiction

Now, we can show that statement #4 cannot be proven.

That’s because if the mathematical system *could* be used to prove that statement #4 is true, that would produce a contradiction. Statement #4 claims that statement #4 *cannot* be proved – so if you prove it’s true, then that implies you can’t prove it’s true!

Since we’ve assumed our mathematical system contains no contradictions, that means statement #4 can’t be proved in that system – which is precisely what statement #4 claims! So, statement #4 is true. Hence, it is a true statement that can’t be proved in that system.

Since Gödel showed that a statement like statement #4 can always be created in any mathematical system powerful enough to allow for arithmetic, any such system that has no contradictions has true statements that the system can’t be used to prove!

Incompleteness Theorem 2 is about whether you can prove that mathematical systems contain contradictions.

If a mathematical system contained a contradiction (e.g., that 1=2), that would make the system useless, so it would be really nice to be able to show that such a system contains no contradictions.

However, Incompleteness Theorem 2 says that a mathematical system with no contradictions that’s powerful enough to include arithmetic can’t prove that it itself has no contradictions. Or, more formally, that means that you can’t use the system to prove: “This system’s axioms do not lead to a contradiction.”

Yet another way to put it is, if such a mathematical system proves that it itself has no contradictions, then that system actually has contradictions!

Why? Well, let’s return to our statement #4 from before. Recall that this statement refers to itself, as it’s the statement that says, “I can’t be proven in this system” (though in completely precise mathematics, not in words).

Note that if statement #4 is false, that would produce a contradiction. Since statement #4 claims that statement #4 can’t be proven to be true, if that’s false, then statement #4 *can* be proven to be true, which is the same as saying it’s actually a true statement – so it being false implies it is true, which is contradictory!

What this means is that if we can prove that the system we’re working in has no contradictions, then that proof ALSO implies that statement #4 is true (i.e., that the system can prove statement #4).

But we already know from the first theorem that statement #4 can’t be proven to be true using the system!

Hence, the system must not be able to prove that it itself contains no contradictions (because if it did, it would violate theorem 1).

This tells us that any mathematical system powerful enough to include arithmetic can’t be used to prove that it itself has no contradictions (unless it actually contains contradictions).

A big thanks goes to Norman Perlmutter for his very helpful feedback on an earlier draft of this post!

This post was first written on November 17, 2023, and first appeared on my website on January 3, 2024.

1. Mimicry: when our in-group promotes falsity that we copy

2. Incentives: when we predict that knowing the truth would feel bad or harm our objectives

3. Complexity: when the truth is hard to figure out

Examples:

1. Mimicry

• Some are Christians because all their friends and family are, too; some are atheists for the same reason.

• Some think that it makes sense to circumcise baby boys because the people they know think it’s healthy and normal; others think it’s bizarre because the people they know think foreskin is healthy and normal.

• Some believe it would harm Black Americans to defund police because their friends say so; others think it would help Black Americans because their friends say it.

2. Incentives

• If you make more money believing X, it’s going to be harder to stop believing it.

• If the idea of permanent death is terrifying to you, it’s going to be harder to stop believing in reincarnation.

• If it would make you feel really bad to find out you were wrong about something you posted online, your immediate reaction may be to deny being wrong (to others and to yourself) to shield yourself from the negative feelings.

Note that mimicry and incentives can blend together. Sometimes we mimic to fit in or to avoid being socially punished. But mimicry is even more basic than that: we seem to have a natural, in-built strong tendency to copy others. If all the people around us believe something, we usually will come to believe it too, without even questioning whether it might be false or even being aware that we copied the belief from others. 

If we see others all behave in a certain way, we’ll probably behave that way, too, unless we have strong reasons not to. This appears to be an evolutionary survival mechanism – it’s risky to (for example) eat plants that are different than the ones your tribe eats (they might be poisonous) or to avoid the behaviors everyone else does (those behaviors might be key to survival in some hidden way). In the wilderness, you can’t figure out how to survive from first principles (chances are you’ll die way too fast for that) – you need to mimic what has worked for centuries (some of which will be key to survival, some of which will be pointless, but evolutionary pressures will have weeded out most of the really harmful stuff and hung on to the most useful stuff).

3. Complexity

• It’s really not obvious how to prevent future risks from advanced artificial intelligence (though it often seems obvious to folks who’ve spent almost no time thinking about it).

• How best to prevent economic crashes is a fundamentally complicated question.

• Nobody actually seems to know how to cure cancers in general.

A weird thing about these three causes for believing falsehoods is that we are usually not aware of their effects.

1. We don’t usually realize it when we believe something just because we copied our social group.

2. We don’t usually realize when we believe something just because it would hurt us not to believe it.

3. We don’t usually realize when our analysis of a complex issue is oversimplified and misses important considerations.

Believing in falsehoods feels just like believing the truth – until the moment we genuinely face up to the possibility of being wrong.

So how do we be right more often?

1. To combat mimicry: we can keep our identities smaller (or have more of them), be more willing to be viewed as having “weird” beliefs, join social groups that value diversity of thought, learn to do less social mimicry (e.g., having greater skepticism towards in-group consensus). We can recognize that every in-group will get some things wrong (including ours) and that it’s worth investigating where ours is wrong. In summary, we can combat mimicry with social resilience and skepticism.

2. To combat harmful incentives: we can recognize that, while there can be short-term pain from accepting the truth, being truth-seeking is usually a better long-term strategy (especially because you can’t just suddenly decide to be truth-seeking when it’s convenient – it’s best to have a habit of being truth-seeking all the time). We can consider thought experiments like: “If X were true, would I rather believe it or be wrong about it?”. We can also leave “lines of retreat” so that we can decide what we’d do and how we’d move forward if we turn out to be wrong on something important. In summary, we can combat bad incentives with a Scout Mindset and a focus on seeking the truth.

3. To tackle complexity: we can use probabilistic thinking, consider multiple hypotheses, and consider the evidence for and against each one. We can train ourselves in evidence and argument evaluation, practice steel-manning arguments, talk to smart and knowledgeable people with different views and fight back against overconfidence (e.g., through calibration practice). We can also do large amounts of research when it’s important to be right. In summary, we can combat complexity with good epistemic hygiene, honed thinking skills, and self-skepticism.

This piece was first written on June 3, 2021, and first appeared on this site on November 11, 2022.

Increasingly, people in the U.S. are concluding that society is really F’d up. But the reasons are subtle, hidden, and complex, so each group picks their favorite simple enemy to blame. Problems go unfixed.

Points in favor of the claim that people are increasingly coming to this realization:

1. This helps explain Trump’s election in 2016. Many rural Republicans and disaffected independents believed society was going down the tubes. They didn’t think that electing the same people (who would use the same old solutions) would fix it.

2. COVID-19 exposed malfunctions of various authorities (the FDA, Surgeon General, CDC, etc.) that are pretty hard to ignore. Liberals blame Trump for these failures, which may be fair, but another possibility is that these American institutions have been rotting for a lot longer. I don’t know enough to say with confidence.

3. Costs for health care, education, housing, infrastructure projects, and energy ($ per kWh) have (as far as I can tell?) been rising for a long time, with debates raging over the (surely complex) causes. People feel the sting of these rising costs. Meanwhile, many argue that wages have stagnated (which has been vigorously debated). What’s more, inequality seems to have risen, though this, too, is debated.

4. An appalling number of people in the U.S. end up in jails and prisons. Shockingly, we have one of the highest incarceration rates in the world. This is obvious to the communities that are most impacted. Though still awful, the numbers have fortunately been improving somewhat in recent years [H/T Alyssa Vance].

5. Student debt growth is out of control, and it’s hard for most students to ignore this.

6. People don’t have adequate savings, the saving rate seems to be relatively low, and a disturbing number say they don’t have enough cash to deal with emergencies. “About four out of 10 Americans said they had enough in savings to cover a surprise $500 expense. Another 21 percent said they would rely on a credit card, while 20 percent said they’d cut back on other expenses. Another 11 percent said they’d turn to family or friends for the money” (source). That being said, the wording of the survey that generated those results was rather confusing, so the results should be interpreted with caution. Note that “a 2016 study [found] that 76% of households had $400 in liquid assets, after…monthly expenses” (source).

7. There is a huge racial wealth gap (even more so than an income gap, I believe), contributing significantly to poorer quality of life for Black Americans.

8. Eric Weinstein’s “the Portal” podcast, which seeks to give explanations about what the hell has gone wrong in America, rose extremely rapidly to popularity. Its theories are quite interesting, in my opinion. It is nuanced (and hard to summarize), but it blames a significant portion of the problem on broken institutions that were predicated on rapid economic/productivity growth that we didn’t end up having, the collective behaviors of the Baby Boomer generation, misleading justifications given to us about immigration, a broken scientific establishment, and a propped up system of harmful deceptions.

So what’s really to blame for the U.S.’s problems?

Trump blames immigration, China, and the left.

The left blames Trump, Wall Street, the right, and (sometimes) capitalism/wealthy people/big tech.

Yang points to automation and globalization.

The BLM movement points to racism and police violence.

The right blames the left, declining moral values, and political correctness.

Libertarians (and the right, too) blame regulation and bloated government.

What seems to me to be the case: many of the explanations by different groups have some truth in them. But they are each only pointing at a part of a much greater problem without really getting to the core of it.

I think that many of the biggest issues in society are simply not being addressed, and their root causes go largely undiscussed. We are spoon-fed simple, misleading explanations for very complex things.

To solve complex problems, we have to know what’s true. To know what’s true, we need to resist political bias, wishful thinking, scapegoating, and overly simplistic (yet appealing) explanations.

To fix society, we must unearth the (hard to spot) root causes and cooperate in order to implement reasonable solutions.

What’s at stake here is way bigger than left, right, democrat, republican, or libertarian. What’s at stake here is the flourishing or decay of our society.

Is society actually getting worse, though? I think the perception is that it is. Is it actually worse than 50 years ago? I really doubt that. Is it worse than 15 years ago? It’s hard to say, and depends on what you care about, but it seems to be worse on at least some metrics, like: the cost of healthcare, the cost of education, student debt, inequality, political polarization, and so on. It’s also improved on some metrics, like GDP per capita. Any way you slice it, society has serious problems. Whether they’ve been getting worse on net is harder to say.

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This piece was first written on September 1, 2020, and first appeared on this site on March 19, 2023.

1. Parkinson’s Law: Work expands so as to fill the time available for its completion.
2. Hofstadter’s Law: It always takes longer than you expect, even when you take into account Hofstadter’s Law.
3. Gates’ Law: Most people overestimate what they can do in one year and underestimate what they can do in ten years.
4. Goodhart’s Law: When a measure becomes a target, it ceases to be a good measure.
5. Hanlon’s Razor: Never attribute to malice that which is adequately explained by stupidity (or, don’t invoke conspiracy when ignorance and incompetence will suffice, as conspiracy implies intelligence).
6. Acton’s Dictum: Power tends to corrupt, and absolute power corrupts absolutely.
7. Amara’s Law: We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.
8. Benford’s Law: In a diverse collection of unrelated statistics, a given statistic has roughly a 30% chance of starting with the digit 1.
9. Betteridge’s Law: Any headline which ends in a question mark can be answered by the word ‘no’.
10. Brooks’ Law: Adding manpower to a late software project makes it later.
11. Chesterton’s Fence: Reforms should not be made until the reasoning behind the existing state of affairs is understood.
12. Claasen’s Law: Usefulness = log(technology).
13. Clarke’s First Law: When a distinguished elderly scientist states that something is possible, they are almost certainly right, but when they state something is impossible, they are probably wrong.
14. Cromwell’s Rule: Nothing but logical impossibilities have a prior probability of 0 or 1.
15. Cunningham’s Law: The best way to get the right answer on the Internet is not to ask a question, it’s to post the wrong answer.
16. Doctorow’s Law: When someone puts a lock on a thing you own, against your wishes, and doesn’t give you the key, they’re not doing it for your benefit.
17. Dunbar’s Number: Most people can’t maintain stable social relationships with more than 150 people.
18. Eroom’s Law: Drug discovery is becoming slower and more expensive over time, despite improvements in technology.
19. Gell-Mann Amnesia Effect: You’ll believe articles outside your area of expertise, even after acknowledging that neighboring articles in your area of expertise are completely wrong.
20. Gibson’s Law (or the Expert Witness Law): For each PhD (to use as an expert witness for one side) there’s an equal and opposite PhD.
21. Godwin’s Law: As an online discussion grows longer, the probability of a comparison involving Nazis or Hitler approaches one.
22. Morley-Souter’s Law (Rule 34): There is porn of it (no exceptions).
23. Greenspun’s Tenth Rule: Any sufficiently complicated C program contains an ad hoc, informally specified, bug-ridden, slow implementation of half of Common Lisp.
24. Hebb’s Law: Neurons that fire together wire together.
25. Hubble’s Law: Galaxies recede from an observer at a rate proportional to their distance to that observer.
26. Hume’s Guillotine (Is-Ought Problem): Normative statements (about what’s moral/immoral/right/wrong) cannot be deduced exclusively from descriptive statements.
27. Humphrey’s Law: Conscious attention to a task normally performed automatically can impair its performance.
28. Kranzberg’s Law: Technology is neither good nor bad; nor is it neutral.
29. Lamarck’s Principle (or “Use it or Lose it”): Use it or lose it (evolutionarily speaking, but also in the brain).
30. Lewis’s Law: The comments you’ll inevitably find on any article about feminism justify feminism.
31. Littlewood’s Law: Individuals can expect miracles to happen to them, at the rate of about one per month.
32. Maes–Garreau Law: Favorable predictions about future technology will fall at the latest possible date they can come true and still remain in the lifetime of the predictor.
33. Metcalfe’s Law: The value of a system grows as approximately the square of the number of users of the system.
34. Miller’s Law: To understand what another person is saying, you must assume that it is true and try to imagine what it could be true of.
35. Moore’s Law: Computation per dollar grows exponentially (or: number of transistors per circuit doubles roughly every 24 months).
36. Murphy’s Law: Anything that can go wrong will go wrong.
37. Alder’s Law: What cannot be settled by experiment is not worth debating.
38. O’Sullivan’s Law: All organizations that are not actually right-wing will over time become left-wing.
39. Pareto’s Principle (80/20 Rule): For many phenomena 80% of consequences stem from 20% of the causes.
40. Peter’s Principle: In a hierarchy, every employee tends to rise to his level of incompetence.
41. Poisson’s Law (or Law of Large Numbers): For independent random variables with a common distribution, the average tends to the mean as sample size increases.
42. Pournelle’s Iron Law of Bureaucracy: In bureaucracy, those devoted to the bureaucracy get control, those devoted to what it’s supposed to achieve lose influence.
43. Putt’s Law: Technology is dominated by two types of people: those who understand what they do not manage and those who manage what they do not understand.
44. Rosenthal Effect (Pygmalion Effect): High expectations lead to an increase in performance, low expectations to a decrease in performance.
45. Schneier’s Law: Any person can invent a security system so clever that she or he can’t think of how to break it.
46. Shermer’s Law: Any sufficiently advanced extraterrestrial intelligence is indistinguishable from God.
47. Zipf’s Law: The frequency of use of the nth-most-frequently-used word in any natural language is approximately inversely proportional to n (few words are used often, most are used rarely).
48. Wirth’s Law: Software gets slower more quickly than hardware gets faster.
49. Sturgeon’s Law: Ninety percent of everything is crud.
50. Stigler’s Law: No discovery is named after its original discoverer, including this one.

This piece was first written on July 6, 2020, and first appeared on my website on May 30, 2026.