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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.

While wealth distribution in the world is highly skewed and heavy-tailed, human talent — as the majority of individual features — is normally distributed. In a recent computational study by Pluchino et al. [Talent vs luck: The role of randomness in success and failure, Adv. Complex Syst. 21(03–04) (2018) 1850014], it has been shown that the combined effects of both random external factors (lucky and unlucky events) and multiplicative dynamics in capital accumulation are able to clarify this apparent contradiction. We introduce here a simplified version (STvL) of the original Talent versus Luck (TvL) model, where only lucky events are present, and verify that its dynamical rules lead to the same very large wealth inequality. We also derive some analytical approximations aimed to capture the mechanism responsible for the creation of such wealth inequality from a Gaussian-distributed talent. Under these approximations, our analysis is able to reproduce quite well the results of the numerical simulations of the simplified model in special cases. On the other hand, it also shows that the complexity of the model lies in the fact that lucky events are transformed into an increase of capital with heterogeneous rates, which yields a nontrivial generalization of the role of multiplicative processes in generating wealth inequality, whose fully generic case is still not amenable to analytical computations.

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.

The role of luck on individual success is hard to be investigated empirically. Simplified mathematical models are often used to shed light on the subtle relations between success and luck. Recently, a simple model called “Talent versus Luck” showed that the most successful individual in a population can be just an average talented individual that is subjected to a very fortunate sequence of events. Here, we modify the framework of the TvL model such that in our model the individuals’ success is modelled as an ensemble of one-dimensional random walks. Our model reproduces the original TvL results and, due to the mathematical simplicity, it shows clearly that the original conclusions of the TvL model are the consequence of two factors: first, the normal distribution of talents with low standard deviation, which creates a large number of average talented individuals; second, the low number of steps considered, which allows the observation of large fluctuations. We also show that the results strongly depend on the relative frequency of good and bad luck events, which defines a critical value for the talent: in the long run, the individuals with high talent end up very successful and those with low talent end up ruined. Last, we considered two variations to illustrate applications of the ensemble of random walks model.

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.

The FinTech disruption has affected the global financial industry, which required many countries to cater to this disruption by building the right FinTech Innovation Ecosystems. For these types of ecosystems to succeed, they need to be based on open innovation concept, where multiple players in the ecosystem can work together to ensure FinTech innovation. The FinTech industry itself is formed based on various collaborations between different stakeholders to come up with innovative products. However, on a macro level, for countries to become a global FinTech hub, they need to ensure the existence of nine important components of FinTech Innovation Ecosystem. These components are FinTech Start-ups, Traditional Financial Institutions (FIs), Government, Financial Customers, Technology Providers, Human Capital, Supporting Platforms, Associations, and International Profile. These nine components, using an open innovation concept, can play a major role in establishing the right FinTech Innovation Ecosystems, which shall lead countries to become global FinTech hubs.

The Rise of the Creative Class, which was originally published in 2002, has generated widespread conversation and debate over both its theory and empirics. This chapter recaps the key tenets of the creative class theory of economic development and discusses the key issues in the conceptual and analytical debate it has inspired.

The world witnessed soaring numbers of overseas Chinese students and scholars migrating back to China in recent years. According to the Ministry of Education of China, in 2012 alone, over 272,900 overseas Chinese students returned. The number of migrants returning back to China rose to 353,500 in 2013. In total, nearly 500,000 overseas Chinese scholars returned to China in the last 30 years since China opened up to the world. Chinese called these returned migrants as haigui or “sea turtles”, because it sounds the same as the phrase “returned from overseas”. These haigui have new knowledge, skills, technologies and global networks that are not available in China. They are helping China transform from a merely manufacturing capital into a global hub of innovation. With the rise of China, the haigui has become a global phenomenon that has been an interest of and reported by global media, as well as studied by the Chinese and western scholars. This paper attempts to explore and analyze (1) Why the haigui are returning to China? (2) What roles they are playing in helping in China’s transformation and rise? (3) How they are helping China transform into a knowledge-based economy ruled by laws of free market society? (4) What the future holds for the haigui?