# The universal law of success

###### tags: `thread`

In the last two days I read [Albert-László Barabási](https://twitter.com/barabasi)'s "The universal law of success", written by a cyber and physicist, who turned to the subject of success after his assistant (Dasun Wang) published a paper on "catastrophe" that was not well received.

This book is worth reading because it uses very professional quantitative data and scientific methods, which are completely different from the common success books on the market.

### Two very interesting studies

Two very interesting studies were found in this book, the first being Michel Klug and James Bagrow's "Understanding the group dynamics and success of teams".

This paper counted a large amount of data from GitHub (only public repo), and they focused on high star repo and team.

First they found that teamwork is more successful than solo work, and that projects backed by a team have, on average, much more star than projects backed by only individuals.

Also James found that the contribution of each person in the team is imbalanced, in most cases the most pieces of code in a high star repo are done by one developer (reminds me of Gavin in the early days of ethereum), the bigger the team, the more the main developer contributes.

### Q-factor

The second study is "Quantifying the evolution of Individual science Impact" by barabasi and Dasun Wang, in which they define the Q-factor.

The Q-factor can refer to the power of action to turn ideas into reality, or it can refer to innate talent and prior knowledge in an industry/profession.

In this paper they define the formula success=Qr, Q is the above mentioned factors, r is the value of an idea, and success is the impact of success.

What amazes me most is that they found a way to measure the Q-factor of scientists and that the Q-factor of scientists does not fluctuate drastically during their career, remaining at the same level as it was at the beginning, and for them the only parameter that can change is r.

This reminds me of the famous "identical twins" experiment in 1990, which simply looked for twins who were born with identical DNA (identical), but were forced to grow up apart in different environments.

Many years later they were subjected to detailed personality measurements, and the final results showed that although they grew up in different environments, they were extremely similar in certain key indicators.


The book does not explain how the Q-factor was discovered and defined, so I hope to read this study by him and Dasun Wang (which appears to be posted in Science) in detail when I get a chance afterwards.

The above-mentioned experiment with identical twins is a paper called "Sources of psychological differences: The Minnesota study of twins reared apart".

### More output, more success

They also created a database of data on a large number of scientists and papers, and they found that the most influential research (relativity) was not necessarily related to the age of the scientist (e.g. Einstein proposed relativity in his twenties).

They numbered each of the scientists' papers and found an extremely strong correlation between the most valuable output and the number of total outputs.

That is, the more output, the greater the probability of success, regardless of age.

It's just that most scientists have nothing when they are young, so they are more eager to prove themselves, create value, work harder than most older scientists, and produce and iterate faster, and innovation almost inevitably always happens at this age.