I'm sympathetic to the above viewpoint, but the argument presented in the article sounds really dumb.
> "But think about it, the authors suggest. If smarts and talents and even effort are so normally distributed and wealth is so abnormally distributed, what’s missing to explain the disparity? “We suggest that such an ingredient is just randomness,” write the authors."
Just because talent is normally distributed, but outcomes are abnormally distributed, doesn't suggest in any way that this is caused by randomness. A winner-take-all game-format would perfectly describe the above phenomenon.
This can be trivially seen when the same logic is applied to sporting competitions. "If Tennis ability is normally distributed across the population, how can we explain a small handful of players like Federer and Williams winning an abnormally high number of championships? Clearly it must be because they're more lucky than talented."
Exactly. That wealth and intelligence distributions are different is simply evidence that wealth is not a simple linear function of intelligence.
Wealth might have multivariate interaction: luck, race, parentage, etc. Or have no other variable interactions with intelligence, but the intelligence relationship is non-linear.
> A winner-take-all game-format would perfectly describe the above phenomenon.
That's not quite true, a winner-take-all with a normally distributed reward should probably also result in a normal distribution of wealth, due to the central limit theorem. If my memory serves me correctly - i am not a professional statistician - it's been long known (but also equally long forgotten) that markets (and for sure many natural risk distributions, like earthquakes), aka, rewards and penalties, are levy-distributed, which has undefined variance, resulting in a pareto distribution.
The model assigns opportunities every 6 months for 40 years and decides whether the talents of the potential beneficiary are adequate to exploit the opportunity.
It doesn't work this way. People waiting for opportunities don't encounter them with the same frequency as people actively looking for and/or trying to manufacture them. An opportunity for me to double my assets or income might exist, but will I even be aware of it? If I am, and my talent is adequate to exploit it, what kind of risk does it come with and what is my tolerance for such risk?
The last event in my life roughly conforming to the opportunities generated by this model was something I actively sought, carefully selected among hundreds of similar opportunities, and took enormous (stupid, I'd be saying now if it hadn't worked out) risk to exploit.
I think the whole point of the paper which the writers at PBS might have glossed over is simply "we can reproduce this outcome with an extremely simple set of hypotheses". That's how I read it, at least, and that's something that's bound to cross the mind of someone familiar with agent based modelling. And if all they want is produce a power law distribution over income, that's all they need. Further model complications only become relevant if they lead off to new conclusions, or different outcomes from the generally expected outcome under particular circumstances. What you're suggesting would be, on the simple basis of some academic pragmatism -- and the fact that they still have to start from somewhere -- left to further studies, adding stuff to this model while citing the original paper.
But all that you're suggest would indeed bring back at least some degree of agency to the whole thing, even if just as being able to find, sort and act upon opportunities.
Opportunity comes for those who can seek it out, which predisposes a certain level of security (for most, though not all). Risk taking is done on a scale close to risk tolerance, and risk tolerance is higher when the downsides are slimmer.
Some criticisms I have read about this paper include:
1) Their model assumes all persons start with the same initial wealth/capital, which is not true in real life
2) The model also assumes people's talent/ability remains constant and does not increase as they get older
In the end it is a model, and models are limited in how much they can match the real world. I do not believe the paper actually "proves" the hypothesis that "If you’re rich, you’re more lucky than smart" as the pbs.org title implies.
As I have gotten older, I have made more money and gotten wealthier. My skills have increased, my knowledge has increased, and I am more valuable.
Did the authors look at wealth as something that is static and to be distributed? Or did they look at it as something that can be created (or destroyed)?
I reckon they have never actually ran a business or created any wealth themselves - but that is just a guess.
I believe that the situation is simpler and more powerful than what the article claims.
Basically you need two things:
1) Some slight advantage
2) The network effect, that is, for example, the probability of competing depends on the current winnings.
If you have these two things, you get 80-20 like distributions, you get the explanation for why winners keep winning. If you are interested, you can find my simulation and analysis at
Kind of shocking how well this works. The intuition is, why has Coke won, well they had some initial advantage, and so they won a bit. Now that they have won a bit, they can finance themselves into more competition. For example, they can place themselves into more stores, into more restaurants etc. Now they get a chance to compete more. When I run with rules:
r1) Actors have normally distributed abilities,
r2) Actors are chosen randomly based on current winnings, the more you have won, the more you compete,
r3) Winner of competition wins one point from the loser,
You get interesting results, for example, in the two columns below, the left is Household income in 1970 broken into quintiles. The right column is simulation results.
Interesting how well the top 3 or 4 quintiles match between the simulation and the real world data.
If you run the simulation with different rules, the real world quintiles do not match the simulation quintiles nearly as well. You can tweak the simulation to see this as well.
The simulation can be tweaked to handle cases such as inheritance, so an actor with different ability inherits the wealth of a past actor. When I run this simulation, around 80-90% of top 20% actors lose all wealth in 3 generations. See for example:
But think about it, the authors suggest. If smarts and talents and even effort are so normally distributed and wealth is so abnormally distributed, what’s missing to explain the disparity?
“We suggest that such an ingredient is just randomness,” write the authors
I think it's more due to the power of compounding. If you're a little tall, that doesn't help you get any taller. But, if you get a little rich, that helps you get richer, which helps you get even richer, and so on.
The example from that book that stood out to me is that most professionAL hockey players have their birthday in the first 3 months of the year. It's suggested that's because when tryouts happen, those children in their age group are the oldest, and therefore most skilled at the time of selection for grooming (well, focused training for their team).
In short, yeah talent is good, but often skill is the result of some luck factor amplifying your talent.
"Lucky" has a negative connotation. Is there a better word that implies something closer to "he made good choices and they worked out even better than expected" or at least something neutral?
[+] [-] whack|7 years ago|reply
> "But think about it, the authors suggest. If smarts and talents and even effort are so normally distributed and wealth is so abnormally distributed, what’s missing to explain the disparity? “We suggest that such an ingredient is just randomness,” write the authors."
Just because talent is normally distributed, but outcomes are abnormally distributed, doesn't suggest in any way that this is caused by randomness. A winner-take-all game-format would perfectly describe the above phenomenon.
This can be trivially seen when the same logic is applied to sporting competitions. "If Tennis ability is normally distributed across the population, how can we explain a small handful of players like Federer and Williams winning an abnormally high number of championships? Clearly it must be because they're more lucky than talented."
[+] [-] paulddraper|7 years ago|reply
Wealth might have multivariate interaction: luck, race, parentage, etc. Or have no other variable interactions with intelligence, but the intelligence relationship is non-linear.
Shoddy conclusion.
[+] [-] Avshalom|7 years ago|reply
[+] [-] dnautics|7 years ago|reply
That's not quite true, a winner-take-all with a normally distributed reward should probably also result in a normal distribution of wealth, due to the central limit theorem. If my memory serves me correctly - i am not a professional statistician - it's been long known (but also equally long forgotten) that markets (and for sure many natural risk distributions, like earthquakes), aka, rewards and penalties, are levy-distributed, which has undefined variance, resulting in a pareto distribution.
[+] [-] trappist|7 years ago|reply
It doesn't work this way. People waiting for opportunities don't encounter them with the same frequency as people actively looking for and/or trying to manufacture them. An opportunity for me to double my assets or income might exist, but will I even be aware of it? If I am, and my talent is adequate to exploit it, what kind of risk does it come with and what is my tolerance for such risk?
The last event in my life roughly conforming to the opportunities generated by this model was something I actively sought, carefully selected among hundreds of similar opportunities, and took enormous (stupid, I'd be saying now if it hadn't worked out) risk to exploit.
[+] [-] tpeo|7 years ago|reply
But all that you're suggest would indeed bring back at least some degree of agency to the whole thing, even if just as being able to find, sort and act upon opportunities.
[+] [-] taurath|7 years ago|reply
[+] [-] tristanj|7 years ago|reply
1) Their model assumes all persons start with the same initial wealth/capital, which is not true in real life
2) The model also assumes people's talent/ability remains constant and does not increase as they get older
In the end it is a model, and models are limited in how much they can match the real world. I do not believe the paper actually "proves" the hypothesis that "If you’re rich, you’re more lucky than smart" as the pbs.org title implies.
[+] [-] jweir|7 years ago|reply
Did the authors look at wealth as something that is static and to be distributed? Or did they look at it as something that can be created (or destroyed)?
I reckon they have never actually ran a business or created any wealth themselves - but that is just a guess.
[+] [-] tnzn|7 years ago|reply
[+] [-] arnold8020|7 years ago|reply
Basically you need two things:
1) Some slight advantage
2) The network effect, that is, for example, the probability of competing depends on the current winnings.
If you have these two things, you get 80-20 like distributions, you get the explanation for why winners keep winning. If you are interested, you can find my simulation and analysis at
http://www.cs.toronto.edu/~arnold/research/80-20/
Kind of shocking how well this works. The intuition is, why has Coke won, well they had some initial advantage, and so they won a bit. Now that they have won a bit, they can finance themselves into more competition. For example, they can place themselves into more stores, into more restaurants etc. Now they get a chance to compete more. When I run with rules:
r1) Actors have normally distributed abilities,
r2) Actors are chosen randomly based on current winnings, the more you have won, the more you compete,
r3) Winner of competition wins one point from the loser,
You get interesting results, for example, in the two columns below, the left is Household income in 1970 broken into quintiles. The right column is simulation results.
Interesting how well the top 3 or 4 quintiles match between the simulation and the real world data.If you run the simulation with different rules, the real world quintiles do not match the simulation quintiles nearly as well. You can tweak the simulation to see this as well.
The simulation can be tweaked to handle cases such as inheritance, so an actor with different ability inherits the wealth of a past actor. When I run this simulation, around 80-90% of top 20% actors lose all wealth in 3 generations. See for example:
https://www.theglobeandmail.com/globe-investor/globe-wealth/....
[+] [-] stephengillie|7 years ago|reply
[+] [-] imgabe|7 years ago|reply
“We suggest that such an ingredient is just randomness,” write the authors
I think it's more due to the power of compounding. If you're a little tall, that doesn't help you get any taller. But, if you get a little rich, that helps you get richer, which helps you get even richer, and so on.
[+] [-] pmoriarty|7 years ago|reply
[+] [-] bmcusick|7 years ago|reply
[+] [-] tnzn|7 years ago|reply
[+] [-] swsieber|7 years ago|reply
The example from that book that stood out to me is that most professionAL hockey players have their birthday in the first 3 months of the year. It's suggested that's because when tryouts happen, those children in their age group are the oldest, and therefore most skilled at the time of selection for grooming (well, focused training for their team).
In short, yeah talent is good, but often skill is the result of some luck factor amplifying your talent.
[+] [-] jeffdavis|7 years ago|reply
[+] [-] jeffdavis|7 years ago|reply
[+] [-] tpeo|7 years ago|reply