The strong role of randomness in successful outcomes undermines meritocracy is rewards and resources are given to those who have already succeeded.
In this paper, starting from very simple assumptions, we have presented an agent-based model which is able to quantify the role of talent and luck in the success of people’s careers.
The simulations show that although talent has a Gaussian distribution among agents, the resulting distribution of success/capital after a working life of 40 years, follows a power law which respects the ”80-20” Pareto law for the distribution of wealth found in the real world.
An important result of the simulations is that the most successful agents are almost never the most talented ones, but those around the average of the Gaussian talent distribution - another stylized fact often reported in the literature. The model shows the importance, very frequently underestimated, of lucky events in determining the final level of individual success.
Since rewards and resources are usually given to those that have already reached a high level of success, mistakenly considered as a measure of competence/talent, this result is even a more harmful disincentive, causing a lack of opportunities for the most talented ones.
Our results are a warning against the risks of what we call the ”naive meritocracy” which, underestimating the role of randomness among the determinants of success, often fail to give honors and rewards to the most competent people. In this respect, several different scenarios have been investigated in order to discuss more efficient strategies able to counterbalance the unpredictable role of luck and give more opportunities and resources to the most talented ones - a purpose that we think should be the goal of a real meritocratic approach. Such strategies have also been shown to be the most beneficial for the entire society, since they tend to increase diversity in research and foster in this way also innovation.
From here.
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