A key purpose of the original paper is to inform policy to improve public funding of research [1]. Thus, to a first-order approximation, it should reflect reali

A key purpose of the original paper is to inform policy to improve public funding of research [1]. Thus, to a first-order approximation, it should reflect realities. However, ... The number of lucky and unlucky events in the paper (figure 5, page 8) are each around 5-7 events per agent on average, where 'lucky' is defined as doubling the capital (or success) accumulated so far and 'unlucky' as halving it. In the real world, how many regular people/scientists have that many career-defining events? Most are quite risk-averse, especially later in their careers. Some entrepreneurs could be exceptions but they are a small minority (single-digit percentage or fewer) [2]. This parameter (no. of big lucky/unlucky events) diminishes the role of talent in the simulation. In real life, most talented people can avoid having 5-7 big 'unlucky' events in their career. This renders the simulations questionable. --> It's like saying talent doesn't matter much for blackjack players who regularly bet half of all their chips. Yes, if one gambles that much, then talent would probably matter relatively little. Do most people do something like that with their career? [3] Research has shown that Log-Normal Distribution could fit data on real-world success as well as Power Law and sometimes better. [4] This suggests that multiple independent factors could play multiplicative roles in success. Talent, luck, grit, location of birth/childhood, educational opportunity, parental guidance, ambition, etc are some candidates. [5] If several factors interacting multiplicatively influence success, the number of individuals who achieve success at a very high level would be quite small, as they need to 'score' high on several of them. This can be modeled quite well with Log-Normal Distribution. [1] https://arxiv.org/abs/1802.07068 https://arxiv.org/abs/1802.07068 [2] As an entrepreneur myself, I would avoid betting half my capital unless the potential gain is far, far higher than doubling my capital. I believe most other entrepreneurs think similarly. [3] Note that capital/success appears to be quite broadly defined in the paper, and can be interpreted to include expertise, reputation, and other resources as well as tangible capital. [4] https://arxiv.org/pdf/1304.0212.pdf https://arxiv.org/pdf/1304.0212.pdf "Moreover, even if the data do not rule out the power-law model usually the evidence in its favour is not conclusive – some rivals, most notably the log-normal and stretched exponential distributions, are also plausible fits to wealth data." [5] Note that luck as defined in the paper does not reflect factors like location of birth, educational opportunity, or parental guidance.