• Purpose of the post:

I'm struggling to understand the significance of the fat-tailed distribution especially in career choice. 80000hours career guide argues that the more accurate distribution for career choice is the long-tailed one.

I'm trying to understand how the implication would differ between a normal bell-curve and a long-tailed distribution. My request: are the implications I wrote in "Part 2: Significance of the fat-tail distribution" accurate? Please focus on points 1 and 2.

Other Names: heavy-tailed distribution, long-tailed distribution, , pareto distribution

• Part 1: Description of Fat-Tail Distribution phenomenon in World Problems and Career Success in 80000hours career guide:

The guide is available for free download at https://80000hours.org/book/

"the most effective actions achieve far more than average. These big differences in expected impact mean that it’s really important to focus on the best areas. Of course, making these comparisons is really hard, but if we don’t, we could easily end up working on something with comparatively little impact. This is why many of our readers have changed which problem they work on. "p.60

"Each change took serious effort, but if changing area can enable you to have many times as much impact, and be more successful, then it’s worth it." (p.61)

"the top 10% of the most prolific elite can be credited with around 50% of all contributions, whereas the bottom 50% of the least productive workers can claim only 15% of the total work" (p. 89)

Simonton, Dean K. ʺAge and outstanding achievement: What do we know after a century of research?ʺ Psychological bulletin 104.2 (1988): 251 as cited in p.89 of 80000hours guide

"Areas like research and advocacy are particularly extreme, but a major study still found that the best people in almost any field have significantly more output than the typical person." Hunter, J. E., Schmidt, F. L., Judiesch, M. K., (1990) “Individual Differences

in Output Variability as a Function of Job Complexity”, Journal of Applied as cited in P.90 of 80000hours guide

"Moreover, success in almost any field gives you influence that can be turned into positive impact by using your position to advocate for important problems (p.91).

This all also means you should probably avoid taking a “high impact” option that you don’t enjoy, and lacks the other ingredients of job satisfaction, like engaging work". (p.91)

"Finally, because the most successful people in a field achieve far more than the typical person, choose something where you have the potential to excel. Don’t do something you won’t enjoy in order to have more impact. " (p.95)

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Figure Assumed (NOT actual) Bell-Curve for problems (p.59)

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Figure: Actual fat-tail distribution for problems (p.60)

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Figure: Actual Fat-Tail distribution of Careers (p.90)

• Part 2: Significance of the fat-tail distribution especially for individuals (All points are comparing fat-tail to bell-curve) :

1. It seems to me that in both distributions we have a motivation to aim for the top (obviously the top would be better) but the nature of motivation changes.

The nature of the carrot and stick changes from "aim to be among the best" because

(bell-curve carrot): if you do great things will happen to you.

(bell curve stick): if you don't, you will remain in the average and mean range

To" aim to be among the best" because

(fat-tail carrot): if you do, VERY VERY VERY great things will happen to you.

(fat-tail stick):If you don't, you will remain in the average (BELOW mean)

2. The more variance there is in a statistical sample, the wiser it is to aim to move to the exploration direction in the exploration-exploitation (deliberation-action) spectrum (the relationship between variance and value of new information). In terms of career, the variance is great so the exploration investment should be great as well.

3. Median does not equal average. The average could be misleading and the median could be misleading

4. If all other factors are moderate or weak but you have a good reason to think you can reach the top, go for it because average can be misleading, your rank is as important as your field).

5. Choosing a job at random is not advised because the median is lower than we think.

6. Even though below mean is much more likely in this distribution, probability of being a hyper-performer is higher , which makes the hyper-performer goal a more realistic goal hence, it's easier to become motivated by it.

7. Looking at the outliers becomes more important because they become more influential.

8. Pareto principle (80/20) can encourage generalist over specialization because the effort you put in one field result in a better impact if distributed over many fields but at the same time it can encourage specialization over generalism because the pareto distribution makes you want to aim for the very top because its impact is disproportionate.

9. Does the long-tailed distribution favor an "all-star" "super-star" or "SWAT" team approach of fewer more qualified people (quality over quantity)?

10. Prioritization becomes more important?

11. We often behave as if the bell-curve distribution is true while it's not. So the significance lies in adjusting our behaviour?

12. Why does personal fit become more important under the long-tailed distribution? Because the long-tailed distribution has a higher variance. If success in a specific field had zero variance, then individual differences wouldn't matter much so the person-environment fit would not be v. important because as persons change nothing changes as a result. However, the reality is that there is great variance so individual differences matter a lot (how would you "react" to a field NOT JUST how good is a career).