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.

SeaROOTS ABM is a quite generic agent-based modeling system, for simulating and evaluating potential terrestrial and maritime mobility of artificial hominin groups, configured by available archaeological data and hypotheses. Necessary bathymetric, geomorphological and paleoenvironmental data are combined in order to reconstruct paleoshorelines for the study area and produce an archaeologically significant agent environment. Paleoclimatic and archaeological data are incorporated in the ABM in order to simulate maritime crossings and assess the emergent patterns of interaction between human agency and the sea.

SeaROOTS agent-based system includes completely autonomous, utility-based agents (Chliaoutakis & Chalkiadakis 2016), representing artificial hominin groups, with partial knowledge of their environment, for simulating their evolution and potential maritime mobility, utilizing alternative Least Cost Path analysis modeling techniques (Gustas & Supernant 2017, Gravel-Miguel & Wren 2021). Two groups of hominins, Neanderthals and Homo sapiens, are chosen in order to study the challenges and actions employed as a response to the fluctuating sea-levels, as well as probability scenarios with respect to sea-crossings via buoyant vessels (rafting) or the human body itself (swimming). SeaROOTS ABM aims to simulate various scenarios and investigate the degree climatic fluctuations influenced such activities and interactions in the Middle Paleolithic period.

The model focuses on simulating potential terrestrial and maritime routes, explore the interactions and relations between autonomous agents and their environment, as well as to test specific research questions; for example, when and under what conditions would Middle Paleolithic hominins be more likely to attempt a crossing and successfully reach the islands? By which agent type (Sapiens or Neanderthals) and how (e.g. swimming or by sea-vessels) could such short sea crossings be (mostly) attempted, and which (sea) routes were usually considered by the agents? When does a sea-crossing become a choice and when is it a result of forced migration, i.e. disaster- or conflict-induced displacement? Results show that the dynamic marine environment of the Inner Ionian, our case study in this work, played an important role in their decision-making process.

Least Cost Path (LCP) analysis is a recurrent theme in spatial archaeology. Based on a cost of movement image, the user can interpret how difficult it is to travel around in a landscape. This kind of analysis frequently uses GIS tools to assess different landscapes. This model incorporates some aspects of the LCP analysis based on GIS with the capabilities of agent-based modeling, such as the possibility to simulate random behavior when moving. In this model the agent will travel around the coastal landscape of Southern Brazil, assessing its path based on the different cost of travel through the patches. The agents represent shellmound builders (sambaquieiros), who will travel mainly through the use of canoes around the lagoons.

How it works?
When the simulation starts the hiker agent moves around the world, a representation of the lagoon landscape of the Santa Catarina state in Southern Brazil. The agent movement is based on the travel cost of each patch. This travel cost is taken from a cost surface raster created in ArcMap to represent the different cost of movement around the landscape. Each tick the agent will have a chance to select the best possible patch to move in its Field of View (FOV) that will take it towards its target destination. If it doesn’t select the best possible patch, it will randomly choose one of the patches to move in its FOV. The simulation stops when the hiker agent reaches the target destination. The elevation raster file and the cost surface map are based on a 1 Arc-second (30m) resolution SRTM image, scaled down 5 times. Each patch represents a square of 150m, with an area of 0,0225km². The dataset uses a UTM Sirgas 2000 22S projection system. There are four different cost functions available to use. They change the cost surface used by the hikers to navigate around the world.

The model generates disaggregated traffic flows of pedestrians, simulating their daily mobility behaviour represented as probabilistic rules. Various parameters of physical infrastructure and travel behaviour can be altered and tested. This allows predicting potential shifts in traffic dynamics in a simulated setting. Moreover, assumptions in decision-making processes are general for mid-sized cities and can be applied to similar areas.

Together with the model files, there is the ODD protocol with the detailed description of model’s structure. Check the associated publication for results and evaluation of the model.

Installation
Download GAMA-platform (GAMA1.8.2 with JDK version) from https://gama-platform.github.io/. The platform requires a minimum of 4 GB of RAM.
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This model introduces individual bias to the model of exploration and exploitation, simulates knowledge diffusion within organizations, aiming to investigate the effect of individual bias and other related factors on organizational objectivity.

The purpose of this model is to analyze the dynamics of endogenously created oscillations in housing prices using a system dynamics simulation model, built from the perspective of construction companies.

ForagerNet3_Demography is a non-spatial ABM for exploring hunter-gatherer demography. Key methods represent birth, death, and marriage. The dependency ratio is an imporant variable in many economic decisions embedded in the methods.

ForagerNet3_Demography_V2 is a non-spatial ABM for exploring hunter-gatherer demography. This version (developed from FN3D_V1) contains code for calculating the ratio of old to young adults (the “OY ratio”) in the living and dead populations.

This model is to simulate and compare the admission effects of 3 school matching mechanisms, serial dictatorship, Boston mechanism, and Chinese Parallel, under different settings of information released.

This model aims to simulate Competition and Displacement of Online Interpersonal Communication Platforms process from a bottom-up angle. Individual interpersonal communication platform adoption and abandonment serve as the micro-foundation of the simulation model. The evolution mode of platform user online communication network determines how present platform users adjust their communication relationships as well as how new users join that network. This evolution mode together with innovations proposed by individual interpersonal communication platforms would also have impacts on the platform competition and displacement process and result by influencing individual platform adoption and abandonment behaviors. Three scenes were designed to simulate some common competition situations occurred in the past and current time, that two homogeneous interpersonal communication platforms competed with each other when this kind of platforms first came into the public eye, that a late entrant platform with a major innovation competed with the leading incumbent platform during the following days, as well as that both the leading incumbent and the late entrant continued to propose many small innovations to compete in recent days, respectively.
Initial parameters are as follows: n(Nmax in the paper), denotes the final node number of the online communication network node. mi (m in the paper), denotes the initial degree of those initial network nodes and new added nodes. pc(Pc in the paper), denotes the proportion of links to be removed and added in each epoch. pst(Pv in the paper), denotes the proportion of nodes with a viscosity to some platforms. comeintime(Ti in the paper), denotes the epoch when Platform 2 joins the market. pit(Pi in the paper), denotes the proportion of nodes adopting Platform 2 immediately at epoch comeintime(Ti). ct(Ct in the paper), denotes the Innovation Effective Period length. In Scene 2, There is only one major platform proposed by Platform 2, and ct describes that length. However, in Scene 3, Platform 2 and 1 will propose innovations alternately. And so, we set ct=10000 in simulation program, and every jtt epochs, we alter the innovation proposer from one platform to the other. Hence in this scene, jtt actually denotes the Innovation Effective Period length instead of ct.