How can you tell who is a valid expert, and who is full of B.S.?

On almost any topic of importance you can find a mix of valid experts (who are giving you reliable information) and false but confident-seeming “experts” (who are giving you misinformation). To make matters even more confusing, sometimes the fake experts even have very impressive credentials, and every once in a while, the real, genuine experts are entirely self-taught.

Here are 12 signs we look for in an expert to help us determine whether they are trustworthy. 

1. They have deep factual knowledge

Let’s start with the obvious: for most topics, a lot of factual knowledge is required before you can have genuine expertise. This means that a genuine expert will have an impressive command of the relevant (non-debated) facts on the topic of their expertise. Thankfully, it’s a lot easier to tell if an expert has a strong command of the non-debated facts than whether they are correct about more controversial claims. 

2. They communicate their confidence levels

Not all knowledge is equally well-established. Even theories that are widely accepted enjoy different levels of support from the relevant evidence. When an expert regularly pretends that all their claims are equally well-established, they demonstrate they are willing to make you believe something is certain when it isn’t.

It’s a good sign that someone treats their subject with the nuance expected from genuine expertise, when they indicate how confident they are (e.g., “It’s been shown in many high-quality studies that…”, or “My best guess is…”), and they explain limitations in the evidence they are using (e.g., “this is unfortunately based on just one study, but that is all that currently exists”)

3. They admit not knowing

Genuine experts also sometimes say that they don’t know the answer to a question, or that the answer is generally not known by anyone. This is important because every topic will have some unknowns, and no expert can know everything about a topic. Telling you when they don’t know is a sign that, when they say they do know, they actually do know.

4. They tell you to look at sources other than themselves

This might happen when an expert doesn’t know the answer to a question, or when they want to help you go beyond the answer they can give you. Genuine experts don’t seek to be seen as a sole arbiter of knowledge or authority on a topic (which can be an indication that ego, rather than truth-seeking, is a primary motivation for them), but instead encourage you to look at resources other than the ones they have produced.

5. They use logic and evidence

Anyone can use rhetorical devices like emotional appeals, no matter how wrong they are, but a well-reasoned argument that uses valid logic and strong evidence will tend to point toward truth. Or, put another way, using strong logic and strong evidence is easier to do when you’re right, whereas emotional appeals are no easier when you’re right than when you’re wrong.  

6. They cite high-quality evidence

Some evidence is much more reliable than other evidence, and those who rely on the less reliable kinds when the more reliable kinds exist probably aren’t doing the best job they can at figuring out the truth. For this reason, genuine experts cite high-quality evidence when it exists (e.g., looking at multiple randomized controlled trials for causal claims) rather than low-quality evidence (e.g., just talking about personal anecdotes), and when high-quality evidence doesn’t exist, they cite the highest quality evidence that does exist.

7. They acknowledge the consensus

Consensus views among experts are more often correct than the idiosyncratic views of just one or two experts. The consensus will not always be right, of course, but often it will be the best understanding we have available. That’s why reliable experts are transparent about the degree to which their opinion differs from the majority of experts, provide reasoned explanations for any deviations, and they are cautious not to present fringe theories as mainstream. This shows a deep engagement with the topic of their expertise and also an adherence to ethical standards of honesty and accuracy in communication.

8. They change their mind

Genuine experts will change their minds about topics within their expertise in response to evidence and arguments. It’s hard to become an expert in something without having been wrong from time-to-time.

That means that anyone claiming to be an expert who has never changed their mind probably has not found and corrected their mistakes. Relatedly, changing one’s mind in response to evidence is also a sign of the epistemic humility associated with genuine expertise.

Of course, if someone has a long history of being wrong, that is evidence against them being a genuine expert, not in favor of it. But, since everyone makes some mistakes, if they make mistakes from time to time and then note they were wrong and improve their beliefs, that is a sign that they are following the evidence where it leads rather than continuing to believe what they do regardless of the evidence.

9. They Steelman

When you ‘straw man’ an argument, you misrepresent or oversimplify someone else’s position to make it easier to attack or refute. Instead of dealing with the actual argument, you replace it with a weaker version that distorts the original point, which you then argue against. The opposite of this is called ‘steelmanning’, and it involves presenting the strongest possible version of an argument you’re objecting to, even if it’s more robust than the one originally presented. This approach aims to strengthen the opposing case in order to facilitate a more genuine and constructive debate. 

The most reliable experts will accurately present the strongest arguments made by those that disagree with them while pointing out flaws in those arguments, rather than focusing on just weak arguments from the other side or just mocking the other side (including ad hominem attacks rather than focusing on the substance of the claims of the other side). This is important because knocking down a weak argument from the other side of a debate does little to show the other side is wrong; you have to refute the strongest claims of the other side to actually show they are wrong. Additionally, demonstrating a knowledge of the strongest arguments against your own position shows a deeper level of expertise than only understanding the opposing point of view at a superficial level.

10. They clearly explain their reasons for believing

The philosopher Daniel Dennett has said: “if I can’t explain something I’m doing to a group of bright undergraduates, I don’t really understand it myself.” This sentiment is echoed by philosopher John Searle, who said “In general, I feel if you can’t say it clearly you don’t understand it yourself.” 

When communicating with non-experts, genuine experts are often able to give clear, easy-to-follow (and, ideally, checkable) explanations for why they believe what they believe – without dumbing down the points. They avoid unnecessary jargon and technical language (which sounds smart but makes their arguments very difficult for their audience to follow). Not every genuine expert is able to do this, but the ability to do this well is a sign of genuine expertise. This is important because an expert who cannot explain their ideas clearly will end up requiring you to believe them based on their authority rather than engaging with the arguments themselves. And sometimes, people claiming to be experts will hide behind technical expertise and jargon so that you won’t notice that their arguments are actually weak.

11. They have a track record

Sometimes genuine experts will have track records of predictions or successes that you can check, and this provides direct evidence of their knowledge or skill. Unfortunately, this only applies to some fields, like chess masters, martial experts who fight in tournaments, experts who make public predictions about the economy or politics, etc.

12. They use multiple lenses

The world is complex and multi-faceted, and any one simple theory is going to fail to explain a lot of what’s really going on. For this reason, genuine experts tend to look at problems from multiple frames and perspectives; they don’t act as though one way of looking at things solves all problems, or that one solution works for all problems, or that one simple theory explains everything.

So the next time you hear claims from an alleged expert on a topic that is important to you, you may want to consider: how many of these signs of expertise do they exhibit? You can use this checklist, considering if they:

1. have deep factual knowledge
2. communicate their confidence levels
3. admit not knowing
4. tell you to look at sources other than themselves
5. use logic and evidence
6. cite high-quality evidence
7. acknowledge the consensus
8. change their mind
9. steelman
10. clearly explain their reasons for believing
11. have a track record
12. use multiple lenses

And if you’re seeking to be an expert in something yourself, you may want to ask yourself: “to what extent do I exhibit these traits?”Being able to discern genuine expertise from B.S. requires good judgment. If you’d like to improve your skills at making accurate judgments, why not try our Calibrate Your Judgment tool, created in partnership with Open Philanthropy.

This piece first appeared on Clearer Thinking.org on April 16, 2024, and first appeared on my website on April 22, 2024.

But there is also something soul-crushing about it. People spend decades learning a craft and then see an A.I. make something in a few seconds or minutes that other people see as comparable to their own work.
And it will be widely misused: to create ripoffs of copyrighted works without giving credit, to generate billions of pages of low-quality content in order to game search engines, to make hyper-personalized spam and commit phishing and fraud, to mislead the public with misinformation and customized persuasion and outrage bait, and to create even more intense social media and video addiction.

Then, there are the longer-term consequences of this kind of technology, which are hard to predict but may be truly catastrophic (even according to some of the leaders who run these very companies).

Humanity sometimes creeps along cautiously – as with the development of new medicines, where we (arguable) allow millions to die based on the fear of releasing something unsafe (which is, to a meaningful extent, a legitimate fear – getting the tradeoffs right is hard). And in the case of nuclear power, where regulation impedes its development due to (mostly) ungrounded fears.

Yet with A.I., humanity plunges forward into the abyss at full speed, with no breaks, with the train conductors announcing to the passengers that we can’t predict where the trains are going, that this all may end up a disaster, perhaps cataclysm, as they shovel exponentially more coal into their white-hot furnaces.

This piece was first written on April 11, 2024, and first appeared on my website on April 16, 2024.

This piece first appeared on ClearerThinking.org on February 28, 2024, was edited on February 29, 2024, and appeared here with minor edits on March 27, 2024.

Does money buy happiness? Intuitively, the answer is yes: common sense tells us that poverty and hardship make people unhappy. We can use money to buy a lot of things that might make us happier – things like a nicer home, fancier vacations, education for our children, or just the opportunity to have more free time. On the other hand, it’s a cliche that “money can’t buy happiness.” Many admire and aspire to the lifestyles of multi-millionaire celebrities, yet rich and famous people often seem desperately unhappy. 

But what does science say about this question? The answer is relevant for all of us as individuals: how important is it for our happiness to strive to make a high salary? It’s also relevant for states: if policy-makers want citizens to be happy, should they prioritize increasing their wealth, or other things?

So, what’s right? Does money make you happier, or is happiness something money can’t buy? In this article, we tell the tale of scholars’ attempts to find out whether money makes people happier, and why they ended up disagreeing on such an apparently simple question. We think you’ll find the results surprising — particularly the recent saga of how some scientists set out to understand the link between income and happiness.

As usual, we’ll discuss the studies’ methodologies and results in detail throughout this piece. However, if you’re short of time and/or just want to know what are the key takeaways, you jump to the “key takeaways” section, at the end of this article. 

Does money increase life satisfaction?

There’s a clear, cross-cultural relationship between income and ‘life satisfaction’ (or ‘life evaluation’). This is broadly how well someone thinks their life is going, relative to what’s realistic for them. For example, in the Gallup World Poll (a large survey, run in over 160 countries), surveyors measure life satisfaction by asking participants in their native language:

“Please imagine a ladder, with steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?”

This is known as the ‘Cantril ladder’. Participants with higher incomes tend to place themselves higher on the ladder than those with lower incomes. This is true both across countries and within countries. In poorer countries, people rate their life satisfaction as lower than in richer countries:

Source: Our World in Data

Citizens of poorer countries, where the GDP per capita is less than $5000, have low average life satisfaction (3-5 out of 10), whereas in richer countries like Switzerland, Singapore or the US, the average life satisfaction rating is higher (6 or 7 out of 10). Results are typically similar for other measures of life satisfaction, such as answers to the question “In general, how satisfied are you with your life?”

To complicate things somewhat, the relationship between income and life satisfaction is logarithmic. This means that every time you double someone’s income, their life satisfaction doesn’t double, but increases by a fixed amount (in this case, roughly 1 point on the life satisfaction scale that ranges from 1 to 10). 

Note that the chart above is a “log plot” – you’ll see that on the x-axis, income roughly doubles at every division, going from $1000 to $2000 to $5000 to $10,000. So a straight line on this plot means that every doubling of income is associated with an increase in happiness of a fixed number of points. On a regular (not logarithmic) scale, the graph instead looks like this:

Source: Our World in Data

This means that though an increased income is associated with greater life satisfaction, this is proportionate to how much a person is already making. Let’s say you start making an extra $1000 a year in income: this will impact your happiness a lot if you were originally making $2000 a year, but it won’t make a big difference if you were already making $100,000 a year. To put it another way, for those who are poor, life satisfaction will typically increase quite a bit as they make an additional $10,000, but for those who are rich, this amount of money has far less impact. .

The charts above show the relationship between income and life satisfaction across countries, but higher-income countries and lower-income countries differ in many ways other than wealth. For instance, lower income countries tend to have worse healthcare systems, so it’s hard to tell if they have lower average life satisfaction due to lower incomes or due to worse healthcare systems (or due to a myriad of other differences between wealthy countries and poorer countries). So it’s hard to be confident from this data alone that higher incomes cause greater life satisfaction, rather than some other factors increasing both income and life satisfaction.

That’s why it’s useful to bring in evidence from within countries, to see if the effect holds within a single country as well.

Source: Our World in Data

Each country in the chart above is represented by a line, connecting the average reported life satisfaction of people in five income quintiles within the country. The chart is messy, but in general, the story is the same: most of the lines trend upwards, with richer citizens reporting a higher life satisfaction than poorer citizens.

Source: “Subjective Well-Being and Income: Is There Any Evidence of Satiation?”, Betsey Stevenson and Justin Wolfers, NBER Working Paper 18992, April 2013. Reformatted in the Economist

The chart above shows a more limited sample of countries. This data is older, from 2013, but it shows a similar trend: if we look at the US line, we can see that average life satisfaction ranges from about 6.6 (out of 10) at the lowest incomes, to around 7.5 at the highest incomes. Compare this to the India line: average life satisfaction is lower in India overall, but just as in the US, poorer Indians on average report lower life satisfaction (about 4.2) than richer Indians (about 5.8).

Other ways of measuring life satisfaction also show within-country differences in life satisfaction depending on income. For example, in the chart below, participants in the US were asked whether they were ‘very satisfied’, ‘somewhat satisfied’, ‘somewhat dissatisfied’ or ‘very dissatisfied’ (rather than being asked to rate their satisfaction on a scale). The proportion of ‘very satisfied’ people is higher in higher income bands, and the proportion of dissatisfied people is lower. Although the number of participants is not high in the lowest and highest bands, on the right hand section of the table (Panel B) we see a fairly steady increase in the percent of people who are very satisfied as income rises. 

Source: “Subjective Well-Being and Income: Is There Any Evidence of Satiation?”, Betsey Stevenson and Justin Wolfers, NBER Working Paper 18992, April 2013. 

Note that even this within-country data doesn’t prove that income causes greater life satisfaction: all these polls show is a strong association between income and life satisfaction. Perhaps even within countries, other factors (such as the quality of infrastructure in richer vs poorer areas of a single country) cause people to feel more satisfied with their lives and to earn more money. However, given that across-country and within-country evidence agree, and it’s reasonable to assume a priori that money can contribute to life satisfaction by enabling people to buy things they want and need as well as to have more control over their time, it stands to reason that there is probably a causal link. But we should keep in mind that the data don’t provide absolute proof that an increase in income causes an increase in life satisfaction. 

An additional complicating factor is that this data measures averages. Even if on average it’s the case that greater income causes greater life satisfaction, for any given person, income and life satisfaction could be more or less closely related than average. This means that two people of the exact same income may be impacted very differently by the same increase in income: perhaps the extra money allows one person to leave a career they hate and pursue one they love, whereas the other just puts the additional income into their savings and doesn’t end up using it for anything important. So we have to be careful not to make confident generalizations about individuals, based on the average. 

So, if more money is associated with greater life satisfaction, is it right to say that the wealthier tend to be happier? Not exactly: it depends what you mean by happiness. 

Does more money make you happier day-to-day?

The Gallup polls, and many other studies related to happiness, measure life satisfaction: participants are asked to reflect on their lives holistically and think about how things are going. However, life satisfaction is only one way to measure a person’s happiness. We might also consider how emotionally happy people feel day to day: whether they tend to feel joyous, content and calm, or stressed, depressed and anxious. This is sometimes known as ‘hedonic wellbeing’ or ‘experienced happiness’.

Psychologist Daniel Kahneman and economist Angus Deaton wondered if there was a relationship between this moment-to-moment hedonic wellbeing and money, just as there was with life satisfaction. In a 2010 experiment, they analyzed more Gallup data, this time from 1000 U.S. residents. This survey, rather than asking participants to rank themselves on Cantril’s ladder, instead asked them whether they had experienced specific emotions for a lot of the previous day (for example ‘enjoyment’ ‘sadness’ or ‘worry’).

The researchers then grouped these emotions together into ‘positive affect’ – happiness, laughter and enjoyment – and ‘blue affect’ – sadness and worry. They also measured stress. People with higher incomes were more likely to report experiencing positive emotions the previous day, and less likely to report sadness, worry or stress.

Source: “High income improves evaluation of life but not emotional well-being”, Daniel Kahneman and Angus Deaton, PNAS 107, September 2010

As with life satisfaction, the relationship between hedonic wellbeing and income was logarithmic in Kahneman and Deaton’s data for lower incomes, meaning that doubling a person’s income was associated with a fixed increase in their hedonic wellbeing. However, unlike with life satisfaction, this experiment seemed to show that increases in emotional wellbeing taper off at around $75,000 per year (in the U.S.); after that, extra money didn’t seem to increase wellbeing anymore.

On the graph above, ‘ladder’ refers to Cantril’s ladder, the life satisfaction question asked in other studies. You can see that this line continues to go up while the hedonic wellbeing lines flatten out. Again, note that this x-axis is on a log scale, with income doubling every division.

This provides an interesting contrast to the life satisfaction studies: it seems that if you already make $75,000 a year in the U.S., you might become more satisfied with your life if you made more money, but you’d be unlikely to have more positive emotional experiences day-to-day. This makes some intuitive sense: money helps us meet our basic needs and can solve a lot of problems, but there are other problems that it’s much harder to solve with money and that even billionaires have to face — for example relationship conflict, bereavement, or mental and physical illness. Maybe once you’re making $75,000 a year, you can solve all the problems that are soluble with money, leaving only those trickier problems.

But it turns out that story is wrong! 

Questioning the taper 

It makes intuitive sense that day-to-day, hedonic wellbeing would taper off at higher incomes. However, Kahneman’s and Deaton’s result did not hold up to scrutiny. A decade later in 2021, economist Matthew Killingsworth tried to replicate their conclusion on new data. He found something different: in his analysis, hedonic wellbeing did continue to increase with income, even above $75,000 a year. It didn’t taper off or plateau. The slope of the graph was also the same below $75,000 a year and above $75,000, meaning that on average, a doubling of income from $15,000 to $30,000 a year and a doubling from $75,000 to $150,000 a year are associated with the same increase in emotional wellbeing.

What was going on here? Kahneman (the author of the first paper), Killingsworth (the author of the second paper) and psychologist Barbara Mellers, keen to get to the bottom of this, acted like ideal scientists: they embarked on an adversarial collaboration, all working together to try to work out why Kahneman’s and Killingsworth’s experiments had got such different results.

They discovered a few important differences between the 2010 and 2021 studies. First, the 2021 study used more accurate data collection methods. In Kahneman’s 2010 study, surveyors had asked participants to recall whether they’d felt certain emotions the previous day. But in Killingsworth’s experiment, participants instead received smartphone notifications several times per day that asked them to rate how they were feeling at that moment, on a scale of ‘very bad’ to ‘very good’. Since they asked participants multiple times and asked them to describe their feelings in the moment, rather than relying on memory, they got a more accurate measurement of each participant’s general hedonic well-being. There were also many more data points in the 2021 study: over a million data points from 33,000 people (vs 1000 people in the 2010 study, which when separated into different income buckets makes it a bit thin). Killingsworth’s experiment also surveyed more high-income people and had more different income bands, rather than lumping together all participants who made over $120,000 a year.

However, despite this, Kahneman and Killingsworth (when working together) did find some evidence for a plateau in hedonic wellbeing above $75,000 a year in income. So why didn’t it appear in Killingsworth’s study? Why did Killingsworth find that emotional wellbeing didn’t seem to plateau even at high incomes, up to $500,000 a year and beyond?

When the authors re-examined the data from the 2021 study, they discovered that the plateau did exist — for the 20% of the population with the lowest emotional wellbeing. That is, if you experience lots of negative emotion to begin with, additional income is not associated with increased hedonic wellbeing once you make over $75,000 a year, but if you have average or high hedonic wellbeing, additional income is associated with an increase in day-to-day positive emotions, even if you are already wealthy.

As they investigated, they realized that they had missed this because their methods of measuring wellbeing worked better as measures of unhappiness, or negative wellbeing: they couldn’t easily differentiate between people who were pretty happy, moderately happy, and very happy (in terms of their day-to-day emotions). This made it more difficult to spot that the plateau only existed for the 20% of the population with the lowest day-to-day wellbeing. 

So does that mean day-to-day wellbeing does increase the richer you get?

Well, yes…and no. Media discussions of this story focussed simply on whether happiness increases with income, since that was the main focus of the research. 

But another relevant question — probably more relevant for most people — is how much happier does more money make you? Let’s look at the data (on a regular, non-log scale):

Source: our reanalysis of data from Kahneman, Killingsworth and Mellers’ 2022 adversarial collaboration.

Does it look like average wellbeing increases in the higher income categories? It basically looks like it doesn’t! If you squint, you can just about notice that the bar in the $625,000-a-year band is a tiny bit higher than the bar in the $15,000-a-year band. 

You can only see that logarithmic upwards line if you zoom way in:

Source: our reanalysis of data from Kahneman, Killingsworth and Mellers’ 2022 adversarial collaboration.

Look at the y-axis. In this zoomed-in version, you can see that the lowest point of the curve — the average wellbeing of people making $15,000 a year — is at about 60.9, on a 100-point scale. The highest point — people making $400,000 a year — is 65.8. That is, the difference between the poorest and the wealthiest is only about 5 points, on a 100-point scale!

The takeaway from these hedonic wellbeing studies is often ‘more money makes you happier’. While this isn’t wrong, a more relevant lesson from this research might be that people with vastly different levels of wealth have surprisingly similar levels of emotional wellbeing. 

To put this another way, even if we assume this entire effect is causal (that is, that income is causing all of this increase in emotional well-being), then if you made 25 times more money, taking you from the bottom 10% to well within the top 10% of US incomes, you should only expect to get half a point happier on a 10-point scale (from 6 to 6.5). 

We suspect that many will find the small size of this effect surprising. 

To be fair, the authors of the adversarial collaboration did mention this, but it wasn’t the focus of the paper, and we suspect that many people who looked at the paper briefly would have missed it:

‘[Kahneman and Deaton] reported that the effect of an approximately fourfold difference in income is about equal to the effect of being a caregiver, twice as large as the effect of being married, and less than a third as large as the effect of a headache.’

What is the more interesting finding — that emotional wellbeing goes up logarithmically with income, or that emotional well-being goes up very little with income? We think that the second finding is likely to be the more important one for most people.

Now, of course this result doesn’t mean that you yourself wouldn’t have much higher emotional wellbeing if you were to increase your income substantially — but the link seems weak enough that we should be wary about assuming we’ll feel much happier moment to moment if we make more money. 

Life satisfaction varies much more than hedonic wellbeing 

If hedonic wellbeing varies so little, what about life satisfaction, the variable we discussed at the beginning of this article? The data show that people with greater incomes have higher life satisfaction, on average, but is this difference equally tiny? 

No: if we look again at the within-country data on life satisfaction, we can see that the US line spans more than 1 point on the 10-point scale. What’s more, the difference is even greater if we compare countries: the poorest in India are about 3.5 points less satisfied, on average, than the richest in the US.

Source: “Subjective Well-Being and Income: Is There Any Evidence of Satiation?”, Betsey Stevenson and Justin Wolfers, NBER Working Paper 18992, April 2013. Reformatted in the Economist

So, life satisfaction, in general, seems to vary much more with income than hedonic wellbeing. 

–

To make a more direct comparison, looking at US data, every doubling in income is associated with an increase of 0.6 on 10-point scale in life satisfaction, and an increase of only 0.1 on a 10-point scale in hedonic wellbeing (or equivalently, 6 points on a 100-point scale for life satisfaction, and just 1 point for hedonic wellbeing).

Another way to understand this difference is to measure the variance from the mean of each measurement, which Killingsworth did in his 2020 study. 

Source: ‘Experienced well-being rises with income, even above $75,000 per year’, Matthew Killingsworth

Note that the x-axis here is logarithmic (with incomes doubling at each division), and the y-axis uses not absolute scores but z-scores, which measure how many standard deviations each participant’s score is from the mean. The blue “life satisfaction” line slopes up more sharply than the red hedonic wellbeing line, meaning that as income goes up, life satisfaction rises faster than experienced well-being (it increases by a greater number of standard deviations).   

This is counter-intuitive: if people’s day-to-day feelings are middling (as they seem to be, on average), shouldn’t this lead to a middling level of life satisfaction? Are people bad at remembering their day-to-day emotions, so that when they answer life satisfaction questions, they overestimate or underestimate how good their life is?Perhaps. But more plausibly, life satisfaction and hedonic wellbeing are different elements of happiness, rather than two different ways to measure a single, unitary trait. Most people value positive emotions, but they also value other things, including career, family, status, material goods, achieving their goals, and money itself. And sometimes, striving to achieve something you aspire to — something that would increase your life satisfaction — will cause more negative emotions in the short run — for example, when you overwork yourself and take on lots of stress to hit a work milestone. 

Key takeaways – Are people, on average, happier when they make more money? 

We’ve learned that:

• Making more money is associated with moderately greater life satisfaction 
• Making more money is associated with a very small increase in hedonic wellbeing 
• …unless we’re talking about is the unhappiest 20% of people who already make $75,000 a year or more in the U.S., in which case additional income was not associated with greater hedonic wellbeing
• In both cases (life satisfaction and hedonic wellbeing), extra income has a greater effect when you start off with less money – or, more precisely, each doubling of income is associated with the same effect.  

So, the link between income and happiness  depends on what you mean by happiness, and how much money we’re talking about. Ultimately, you can make some predictions about the way you might feel if you had more or less money, but like so many scientific questions, the answer is messy, and you’re unlikely to know for certain how things will turn out. Perhaps this is why our cultural clichés give us such mixed signals about this question! 

Building Happiness Habits

You’ve learned how income is related to happiness, but if you’re interested in exploring habits that could help you improve your day-to-day hedonic wellbeing, we encourage you to test Clearer Thinking’s “Building Happiness Habits” interactive tool.

It works by pairing certain happiness techniques – namely mindfulness and gratitude – with everyday activity triggers such as walking or checking social media, thereby cultivating happiness-improving mental habits. 

We tested each technique on hundreds of users and found that they made a positive difference for those who practiced them, in just 3 days.

Each technique is:

• Easy to learn – there’s no need for long, difficult training in order to be able to perform them.
• Simple to apply — there’s just one main thing to do, not a bunch of steps.
• Time-efficient — it takes just a few minutes a day to practice them.

Ready to make your days a little brighter?

This confuses the public and undermines trust in science and scientists.

Some interesting examples:

1) As COVID-19 spread early in the pandemic, epidemiologists confidently stated many true things about it that were scientifically measured (e.g., rate of spread). Some of them were also equally confidently stating things that were just speculation (e.g., its origin being natural).

2) String theorists told the public many true and interesting things about string theory (e.g., why they feel it’s exciting). Some also confidently claimed very uncertain stuff like:”Superstring theory successfully merges general relativity and quantum mechanics.”

Being charitable, perhaps this could be interpreted not as a claim about superstring theory providing a correct theory of physics but rather as a statement about what superstring theory is doing mathematically. Even if so, though, this is – at the very least – going to be very confusing to those who read it. The statement also makes superstring theory seem like it can claim great achievements that perhaps it can’t.

3) Biologists confidently tell the public many true things about how cells form, how evolution works, and so on. Some, unfortunately, have made overconfident claims about a subject that is extremely uncertain: how life formed on Earth. We have only highly speculative theories.

Let me be clear: most scientists don’t engage in what I’m describing above. But when people claim something has been scientifically PROVEN when it actually hasn’t, this tends to reduce trust in the scientific enterprise and causes people to doubt scientists.

My field (psychology) is squishy enough that (unlike physics/biology) little has truly been PROVEN beyond a doubt. At best, we can usually say that studies have found a relationship or that (based on our own interpretation of the evidence) we believe a certain thing.

This piece was first written on August 13 and first appeared on this site on August 23, 2023.

(1) Linus Pauling won TWO Nobel prizes – one in peace and one in chemistry. Unfortunately, he eventually became obsessed with and widely promoted the false (and sometimes still repeated) idea that high-dose vitamin C cures many diseases, including HIV and snakebites.

(2) Isaac Newton, who co-invented calculus and discovered the laws of gravity, also was convinced the Bible had hidden messages he could decode for prophetic purposes, and spent a lot of time trying to create the mythical philosopher’s stone, so he could turn metal into gold.

(3) Alan Turing, often considered to be the father of theoretical computer science and artificial intelligence, seemingly was convinced by the existence of extrasensory perception. He wrote: “the statistical evidence, at least for telepathy, is overwhelming.”

(4) C. Langan, who appears to have one of the highest IQs ever recorded, believes “you can prove the existence of God, the soul, and an afterlife, using mathematics.” and has claimed that 9/11 was an inside job staged by the Bush administration.

My point is not that these people were stupid – they are the opposite of stupid – they are far smarter than 99.9% of the people that have ever lived (by at least some reasonably common ways of thinking about intelligence). My point is that even the smartest among us hold some silly, false beliefs – intelligence is not enough to avoid error.

Rationality (in the sense of evaluating evidence in such a way as to effectively arrive at the truth on important topics) and intelligence, while related, are also not the same things. Rationality involves actively working to disprove your own beliefs – which intelligent people may or may not do. For instance, intelligence is often used to come up with clever and convincing arguments for why what you already think must indeed be correct. In other words, intelligence can be deployed for rationality but also for rationalization.

Of course, it may also be me that’s wrong. Perhaps there’s a philosopher’s stone, vitamin C cures a ton of diseases, 9/11 was an inside job. But more likely, I’m wrong about other things (that I have no clue I’m wrong about). It’s useful to remember: we all believe false things.

This piece was first written on July 16, 2023, and first appeared on this site on August 16, 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.

• You know that “something” exists.
• If you have mental experiences, then you know that “you” exist (though coming up with a reasonable definition for what “you” means can be remarkably tricky).
• If your mental experiences are varied, then you know that whatever exists creates varied mental experiences.

With some cleverness, you may be able to add to this list a few more things that you know with total certainty, but not many. In fact, almost everything that we think we know we cannot be completely sure about. For instance:

• You take it for granted that five minutes from now you will still be alive, but there is a non-zero probability that your heart will give out before then.
• You feel as though the world you see around you is reality, and yet, that is also what it would feel like if you were in fact in an extremely detailed simulation of reality (for instance, your brain could be hooked up to some sort of computer). So you cannot know for certain that you are not now living in a simulation rather than having experiences based on physical reality itself.
• You cannot know, with total certainty, that you are not dreaming right now.
• You cannot know, absolutely, that you have not gone mad. Many things that you think are real could be delusions. Of course, you’d expect your delusions to feel totally correct to you, and not seem delusional at all.

Virtually everything involves some amount of uncertainty (even the best predictions will contain some error), and many things are highly uncertain (in the sense that our attempts to model them lead to uncertain predictions). Consider our uncertainty about processes on different physical scales.

On a large scale there are weather systems and economies, which depend on very large numbers of variables that are difficult to measure accurately. Weather depends on temperatures, pressures, geography, sun output, cloud cover, pollution, bodies of water, etc. Economies are impacted by spending behavior, regulation, war, technology, disease, culture, and so forth. What makes large systems especially hard to predict is that many of them are chaotic in nature, so small changes in the values of the variables by which they are influenced can lead to large changes down the line. For instance, a relatively small shift in the pressure and temperature in one area of the world may eventually lead to a hurricane hitting a far away area. Or, in an economy, a piece of legislation passed today that deregulates certain company behaviors could lead to increased use of leverage, which could result in a market collapse a decade later. Political events are another good example of large-scale uncertainty, because they tend to be highly complex and chaotic. That means they should be hard to predict. In fact, the empirical data says that most political forecasting “experts” are not significantly more accurate than random guessing or a non-expert predictor, though some fairly small fraction of such experts do seem to have meaningful predictive power.

On a medium scale, there are humans and roughly human-sized objects. Objects on a human scale, like balls and bookcases, tend to be quite each to predict, in part because they are accurately modeled by the relatively straightforward laws of Newtonian Mechanics. If you throw a ball you have a decent idea of where it is going to go, though the details of its trajectory will still be uncertain. If you drop a bookcase off of the Empire State Building, you know it will fall downward, and can roughly estimate when it will hit the ground, though exactly where it will land will depend on unpredictable factors involving air flow across its surface. However, predicting the location of the cracks along which it will shatter when it finally hits the ground is far beyond our ability to forecast.

Unlike balls, which are fairly straightforward, human minds are very unpredictable. The problem is three-fold: the complexity of the brain, our lack of knowledge about brains in general, and the inaccessibility of the present state of any particular brain. It can be hard enough to tell if someone you’re talking to is annoyed, engaged in a pleasant day-dream, or feeling awkward. But predicting what they will say next will be even more difficult. Brains can even act as uncertainty generators. You can give a man a physical object, like a book, which can lead him to believe some idea that he then feels compelled to spread. If he is successful enough in his proselytizing, he may spread this idea to hundreds of others, who themselves spread the idea to tens of thousands more. The large-scale fate of the world may be altered due to one particular person having seen one particular book, a result that was dependent on the great complexity of the human brain.

Knowledge of psychological studies does, of course, allow us to predict some things about human behavior. For instance, in Milgram’s famous experiment, it was found that if a person who seems sufficiently authoritative insists that a test subject administer high voltage shocks to another person, even though it seems obviously dangerous to do so, about two-thirds of the test subjects will comply. The trouble is that even robust psychological findings like this one involve much uncertainty. For one thing, it is difficult to predict in advance who the one-third of non-compliers are going to be. Furthermore, the fraction of participants that comply is influenced by a variety of factors, so one cannot assume that this two-thirds number will hold in similar situations. For instance, when the authority was not in the room with the test subject, communicating only by telephone, compliance fell from two-thirds to only about twenty percent. When other test subjects (actually actors who are confederates of the experimenter) refused to comply with the authority, only ten percent were willing to continue with the experiment.

While there are some human behaviors that can be predicted very accurately (if you touch a hot pan, I predict you will pull your hand away, if you are handed a deep-fried oreo, I predict you will eat it, if you attempt a certain extremely difficult logic puzzle, I predict you will not be able to solve it in 5 minutes). But, at this stage of our understanding, much of the human behavior that we care about predicting can only be modeled in a probabilistic fashion, with a lot of uncertainty in our estimates. We are forced to use statements like “most of the time when you put a person in situation X, they will have behavior Y.”

While one might hope that at the small-scale things become certain, this is unfortunately not the case. It is now known that atoms are governed by the laws of quantum mechanics, which involve inherent, fundamental unpredictability. In fact, in quantum mechanics it doesn’t make sense to even talk about the exact position and velocity of a particle. We can only know the probability of a particle being measured to be within a certain distance of a particular location, and having a velocity within a certain range of values. The Heisenberg Uncertainty Principle makes this idea more precise, encapsulating the idea that the more certain we are of the position of a particle, the less certain we must be of its velocity, and vice versa. As systems of particles become larger and larger (forming objects such as carrots and boulders) the uncertainty principle can be essentially ignored, because the size of the quantum uncertainty is very small compared to the size of the object itself.

Uncertainty is inescapable. It manifests in large-scale chaotic systems, like economies and weather systems, in the details of the trajectories of medium-scale objects, in human behavior, and in small-scale systems . For many of the most interesting predictions we would care to make, we cannot avoid making them in terms of probability. Rather than being able to say, “X will happen,” we end up only being able to speak in terms of “the probability that X will happen.”