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The largely dominant meritocratic paradigm of highly competitive Western cultures is rooted on the belief that success is mainly due, if not exclusively, to personal qualities such as talent, intelligence, skills, smartness, efforts, willfulness, hard work or risk taking. Sometimes, we are willing to admit that a certain degree of luck could also play a role in achieving significant success. But, as a matter of fact, it is rather common to underestimate the importance of external forces in individual successful stories. It is very well known that intelligence (or, more in general, talent and personal qualities) exhibits a Gaussian distribution among the population, whereas the distribution of wealth — often considered as a proxy of success — follows typically a power law (Pareto law), with a large majority of poor people and a very small number of billionaires. Such a discrepancy between a Normal distribution of inputs, with a typical scale (the average talent or intelligence), and the scale-invariant distribution of outputs, suggests that some hidden ingredient is at work behind the scenes. In this paper, we suggest that such an ingredient is just randomness. In particular, our simple agent-based model shows that, if it is true that some degree of talent is necessary to be successful in life, almost never the most talented people reach the highest peaks of success, being overtaken by averagely talented but sensibly luckier individuals. As far as we know, this counterintuitive result — although implicitly suggested between the lines in a vast literature — is quantified here for the first time. It sheds new light on the effectiveness of assessing merit on the basis of the reached level of success and underlines the risks of distributing excessive honors or resources to people who, at the end of the day, could have been simply luckier than others. We also compare several policy hypotheses to show the most efficient strategies for public funding of research, aiming to improve meritocracy, diversity of ideas and innovation.

This paper further investigates the Talent versus Luck (TvL) model described by [Pluchino et al. Talent versus luck: The role of randomness in success and failure, Adv. Complex Syst.21 (2018) 1850014] which models the relationship between ‘talent’ and ‘luck’ on the impact of an individuals career. It is shown that the model is very sensitive to both random sampling and the choice of value for the input parameters. Running the model repeatedly with the same set of input parameters gives a range of output values of over 50% of the mean value. The sensitivity of the inputs of the model is analyzed using a variance-based approach based upon generating Sobol sequences of quasi-random numbers. When using the model to look at the talent associated with an individual who has the maximum capital over a model run it has been shown that the choice for the standard deviation of the talent distribution contributes to 67% of the model variability. When investigating the maximum amount of capital returned by the model the probability of a lucky event at any given epoch has the largest impact on the model, almost three times more than any other individual parameter. Consequently, during the analysis of the model results one must keep in mind the impact that only small changes in the input parameters can have on the model output.

This paper analyzes the Talent versus Luck model, which examines the impact of talent and luck on an individual’s career success. The original simulation-based model demonstrated that the distribution of capital has a heavy tail, and the most successful individuals are not necessarily the most talented. While the implications of the original model are intriguing, those findings were based solely on numerical calculations, and it was unclear how generally valid they are. Challet et al. generalize the original model using an analytical approach and successfully clarify the relationship between talent, lucky events, and capital when talent is constant and follows a uniform distribution. We reformulate a simplified model and derive more general propositions about the relationship between luck and talent in individual success by introducing the new concept of luck of outcome in addition to the luck of opportunity in previous models. We show that the capital distribution generated from a simplified talent versus luck model follows a lognormal distribution even when the talent is subject to a normal distribution. Moreover, we specify the relationship between the inequality of the distribution, which is indicated by the Gini coefficient, and the parameters of talent distribution.