Why do career outcomes in organizations follow highly skewed distributions when the individual traits presumed to drive success — competence, effort, and social skill — are approximately normally distributed in the population? This agent-based model investigates the relative contributions of individual attributes versus random events (“luck”) to career outcomes in hierarchically structured organizations.

The model places 500 agents in a five-level pyramid-shaped organization over 80 six-month evaluation periods (40 simulated years). Each agent is characterized by four attributes drawn from normal distributions at initialization: competence, effort, social skill, and adaptability. In each period, agents may encounter stochastic career events (good or bad projects, supportive or poor managers, market booms or busts), which modify their performance and visibility scores. Promotion decisions are made competitively when vacancies arise at higher levels, using a weighted combination of recent performance, accumulated visibility, network position, and tenure. Social connections form and decay through a proximity-based model influenced by agents’ social skills.

The model extends the talent-versus-luck framework of Pluchino et al. (2018) in four substantive directions: (1) it embeds agents in a hierarchical organization with finite positions at each level, making promotion a zero-sum competition rather than an abstract encounter with random events; (2) it represents individual differences along four independent attribute dimensions rather than a single talent score; (3) it incorporates dynamic social network formation that mediates access to career-relevant opportunities; and (4) it implements the Peter Principle mechanism through which promoted agents must acquire competence appropriate to their new role.

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This documentation provides an overview and explanation of the NetLogo simulation code for modeling skilled workers’ migration in Iran. The simulation aims to explore the dynamics of skilled workers’ migration and their transition through various states, including training, employment, and immigration.

The flow of elite and talent migration, or “brain drain,” is a complex issue with far-reaching implications for developing countries. The decision to migrate is made due to various factors including economic opportunities, political stability, social factors and personal circumstances.
Measuring individual interests in the field of immigration is a complex task that requires careful consideration of various factors. The agent-based model is a useful tool for understanding the complex factors that are involved in talent migration. By considering the various social, economic, and personal factors that influence migration decisions, policymakers can provide more effective strategies to retain skilled and talented labor and promote sustainable growth in developing countries. One of the main challenges in studying the flow of elite migration is the complexity of the decision-making process and a set of factors that lead to migration decisions. Agent-based modeling is a useful tool for understanding how individual decisions can lead to large-scale migration patterns.

Extension of Pluchino et al.’s 2018 success vs talent model, to allow talented individuals to mitigate unlucky events.

The largely dominant meritocratic paradigm of highly competitive Western cultures is rooted on the belief that success is due mainly, 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 material 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 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 a recent paper, with the help of this very simple agent-based model realized with NetLogo, we suggest that such an ingredient is just randomness. In particular, we show 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 mediocre but sensibly luckier individuals. As to our knowledge, 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. With the help of this model, several policy hypotheses are also addressed and compared to show the most efficient strategies for public funding of research in order to improve meritocracy, diversity and innovation.

An agent-based model which explores Creativity and Urban Development