THE LUCK IN “TALENT VERSUS LUCK” MODELING

Sean Elvidge ()
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Sean Elvidge: University of Birmingham, Birmingham, B15 2TT, UK

Advances in Complex Systems (ACS), 2020, vol. 23, issue 03, 1-14

Abstract: 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.

Keywords: Success; talent; luck; agent-based models; sensitivity analysis (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1142/S0219525920500071

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