This agent-based model explores the dynamics between human behavior and vaccination strategies during COVID-19 pandemics. It examines how individual risk perceptions influence behaviors and subsequently affect epidemic outcomes in a simulated metropolitan area resembling New York City from December 2020 to May 2021.

Agents modify their daily activities—deciding whether to travel to densely populated urban centers or stay in less crowded neighborhoods—based on their risk perception. This perception is influenced by factors such as risk perception threshold, risk tolerance personality, mortality rate, disease prevalence, and the average number of contacts per agent in crowded settings. Agent characteristics are carefully calibrated to reflect New York City demographics, including age distribution and variations in infection probability and mortality rates across these groups. The agents can experience six distinct health statuses: susceptible, exposed, infectious, recovered from infection, dead, and vaccinated (SEIRDV). The simulation focuses on the Iota and Alpha variants, the dominant strains in New York City during the period.

We simulate six scenarios divided into three main categories:
1. A baseline model without vaccinations where agents exhibit no risk perception and are indifferent to virus transmission and disease prevalence.
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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.

A spatio-temporal Agent Based Modeling (ABM) framework is developed to probabilistically predict farmers’ decisions in the context of climate-induced water scarcity under varying utility optimization functions. The proposed framework forecasts farmers’ behavior assuming varying utility functions. The framework allows decision makers to forecast the behavior of farmers through a user-friendly platform with clear output visualization. The functionality of the proposed ABM is illustrated in an agriculturally dominated plain along the Eastern Mediterranean coastline.

Study area GIS data available upon request to gxh00@mail.aub.edu

The model is designed to simulate the behavior and decision-making processes of individuals (agents) in a social network. It aims to represent the changes in individual probability to take any action based on changes in attributes. The action is anything that can be reasonably influenced by the three influencing methods implemented in this model: peer pressure, social media, and state campaigns, and for which the user has a decision-making model. The model is implemented in the multi-agent programmable environment NetLogo 6.3.0.

Demographic microsimulation model used in speed tests against LIAM 2.

The agent based model presented here is an explicit instantiation of the Two-Factor Theory (Herzberg et al., 1959) of worker satisfaction and dissatisfaction. By utilizing agent-based modeling, it allows users to test the empirically found variations on the Two-Factor Theory to test its application to specific industries or organizations.

Iasiello, C., Crooks, A.T. and Wittman, S. (2020), The Human Resource Management Parameter Experimentation Tool, 2020 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation, Washington DC.

This is a Netlogo model which simulates car and bus/tram traffic in Augsburg, specifically between the districts Stadtbergen, Göggingen and the Königsplatz. People either use their cars or public transport to travel to one of their random destinations (Stadtbergen or Göggingen), performing some activity and then returning to their home. Attributes such as travel and waiting time as well as their happiness upon arriving are stored and have an impact on individuals on whether they would consider changing their mode of transport or not.

Abstract model investigating the determinants of inter- and intra-urban inequality in contact with nature. We explore the plausibility of a social integration hypothesis - whereby the primary factor in decisions to visit Urban Green Spaces (UGS) is an assessment of who else is likely to be using the space at the same time, and the assessment runs predominantly along class lines. The model simulates four cities in Scotland and shows the conditions under which the mechanisms theorised are sufficient to reproduce observed inequalities in UGS usage.

This model was created to investigate the potential impacts of large-scale recreational and transport-related physical activity promotion strategies on six United Nations Sustainable Development Goals (SDGs) related outcomes—road traffic deaths (SDG 3), transportation mode share (SDG 9), convenient access to public transport, levels of fine particulate matter, and access to public open spaces (SDG 11), and levels of carbon dioxide emissions (SDG 13)—in three cities designed as abstract representations of common city types in high-, middle-, and low-income countries.

The present model demonstrates that how two basic human features: (1) that in the human brain beliefs are interconnected, and (2) people strive to maintain a coherent belief system, gives rise to opinion polarization and the appearance of fake news.