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.

This model examines how financial and social top-down interventions interplay with the internal self-organizing dynamics of a fishing community. The aim is to transform from hierarchical fishbuyer-fisher relationship into fishing cooperatives.

ARISE is a hybrid energy model incorporating macroeconomic data, micro socio-economic data, engineering data and environmental data. This version of ARISE can simulate scenarios of solar energy policy for Indonesia case.

The model employs an agent-based model for exploring the victim-centered approach to identifying human trafficking and the approach’s effectiveness in an abstract representation of migrant flows.

The model attempts to explore the trade-offs between immigration policies and successfully identifying human trafficking victims.

Innovation a byproduct of the intellectual capital, requires a new paradigm for the production constituents. Human Capital HC,Structural capital SC and relational capital RC become key for intellectual capital and consequently for innovation.

We extend the Flache-Mäs model to incorporate the location and dyadic communication regime of the agents in the opinion formation process. We make spatially proximate agents more likely to interact with each other in a pairwise communication regime.

MarPEM is an agent-based model that can be used to study the effects of policy instruments on the transition away from HFO.

We present an Agent-Based Stock Flow Consistent Multi-Country model of a Currency Union to analyze the impact of changes in the fiscal regimes that is permanent changes in the deficit-to-GDP targets that governments commit to comply.

This is a stylized model based on Alonso’s model investigating the relationship between urban sprawl and income segregation.