This model implements two types of network diffusion from an initial group of activated nodes. In complex contagion, a node is activated if the proportion of neighbour nodes that are already activated exceeds a given threshold. This is intended to represented the spread of health behaviours. In simple contagion, an activated node has a given probability of activating its inactive neighbours and re-tests each time step until all of the neighbours are activated. This is intended to represent information spread.

A range of networks are included with the model from secondary school friendship networks. The proportion of nodes initially activated and the method of selecting those nodes are controlled by the user.

This model (CharRec) creates simulated charcoal records, based on differing natural and anthropogenic patterns of ignitions, charcoal dispersion, and deposition.

This is an extension of the basic Suceptible, Infected, Recovered (SIR) model. This model explores the spread of disease in two spaces, one a treatment, and one a control. Through the modeling options, one can explore how changing assumptions about the number of susceptible people, starting number of infected people, the disease’s infection probability, and average duration impacts the outcome. In addition, this version allows users to explore how public health interventions like social distancing, masking, and isolation can affect the number of people infected. The model shows that the interactions of agents, and the interventions can drastically affect the results of the model.

We used the model in our course about COVID-19: https://www.csats.psu.edu/science-of-covid19

Digital social networks facilitate the opinion dynamics and idea flow and also provide reliable data to understand these dynamics. Public opinion and cooperation behavior are the key factors to determine the capacity of a successful and effective public policy. In particular, during the crises, such as the Corona virus pandemic, it is necessary to understand the people’s opinion toward a policy and the performance of the governance institutions. The problem of the mathematical explanation of the human behaviors is to simplify and bypass some of the essential process. To tackle this problem, we adopted a data-driven strategy to extract opinion and behavioral patterns from social media content to reflect the dynamics of society’s average beliefs toward different topics. We extracted important subtopics from social media contents and analyze the sentiments of users at each subtopic. Subsequently, we structured a Bayesian belief network to demonstrate the macro patters of the beliefs, opinions, information and emotions which trigger the response toward a prospective policy. We aim to understand the factors and latent factors which influence the opinion formation in the society. Our goal is to enhance the reality of the simulations. To capture the dynamics of opinions at an artificial society we apply agent-based opinion dynamics modeling. We intended to investigate practical implementation scenarios of this framework for policy analysis during Corona Virus Pandemic Crisis. The implemented modular modeling approach could be used as a flexible data-driven policy making tools to investigate public opinion in social media. The core idea is to put the opinion dynamics in the wider contexts of the collective decision-making, data-driven policy-modeling and digital democracy. We intended to use data-driven agent-based modeling as a comprehensive analysis tools to understand the collective opinion dynamics and decision making process on the social networks and uses this knowledge to utilize network-enabled policy modeling and collective intelligence platforms.

The Urban Traffic Simulator is an agent-based model developed in the Unity platform. The model allows the user to simulate several autonomous vehicles (AVs) and tune granular parameters such as vehicle downforce, adherence to speed limits, top speed in mph and mass. The model allows researchers to tune these parameters, run the simulator for a given period and export data from the model for analysis (an example is provided in Jupyter Notebook).

The data the model is currently able to output are the following:

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EMMIT is an end-user developed agent-based simulation of malaria transmission. The simulation’s development is a case study demonstrating an approach for non-technical investigators to easily develop useful simulations of complex public health problems. We focused on malaria transmission, a major global public health problem, and insecticide resistance (IR), a major problem affecting malaria control. Insecticides are used to reduce transmission of malaria caused by the Plasmodium parasite that is spread by the Anopheles mosquito. However, the emergence and spread of IR in a mosquito population can diminish the insecticide’s effectiveness. IR results from mutations that produce behavioral changes or biochemical changes (such as detoxification enhancement, target site alterations) in the mosquito population that provide resistance to the insecticide. Evolutionary selection for the IR traits reduces the effectiveness of an insecticide favoring the resistant mosquito population. It has been suggested that biopesticides, and specifically those that are Late Life Acting (LLA), could address this problem. LLA insecticides exploit Plasmodium’s approximate 10-day extrinsic incubation period in the mosquito vector, a delay that limits malaria transmission to older infected mosquitoes. Since the proposed LLA insecticide delays mosquito death until after the exposed mosquito has a chance to produce several broods of offspring, reducing the selective pressure for resistance, it delays IR development and gives the insecticide longer effectivity. Such insecticides are designed to slow the evolution of IR thus maintaining their effectiveness for malaria control. For the IR problem, EMMIT shows that an LLA insecticide could work as intended, but its operational characteristics are critical, primarily the mean-time-to-death after exposure and the associated standard deviation. We also demonstrate the simulation’s extensibility to other malaria control measures, including larval source control and policies to mitigate the spread of IR. The simulation was developed using NetLogo as a case study of a simple but useful approach to public health research.

NetLogo model that allows scenarios concerning general social distancing, shielding of high-risk individuals, and informing contacts when symptomatic. Documentation includes a user manual with some simple scenarios, and technical information including descriptions of key procedures and parameter values.

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.

In recent years we have seen multiple incidents with a large number of people injured and killed by one or more armed attackers. Since this type of violence is difficult to predict, detecting threats as early as possible allows to generate early warnings and reduce response time. In this context, any tool to check and compare different action protocols can be a further step in the direction of saving lives. Our proposal combines features from continuous and discrete models to obtain the best of both worlds in order to simulate large and crowded spaces where complex behavior individuals interact. With this proposal we aim to provide a tool for testing different security protocols under several emergency scenarios, where spaces, hazards, and population can be customized. Finally, we use a proof of concept implementation of this model to test specific security protocols under emergency situations for real spaces. Specifically, we test how providing some users of a university college with an app that informs about the type and characteristics of the ongoing hazard, affects in the safety performance.

Schelling famously proposed an extremely simple but highly illustrative social mechanism to understand how strong ethnic segregation could arise in a world where individuals do not necessarily want it. Schelling’s simple computational model is the starting point for our extensions in which we build upon Wilensky’s original NetLogo implementation of this model. Our two NetLogo models can be best studied while reading our chapter “Agent-based Computational Models” (Flache and de Matos Fernandes, 2021 [forthcoming]). In the chapter, we propose 10 best practices to elucidate how agent-based models are a unique method for providing and analyzing formally precise, and empirically plausible mechanistic explanations of puzzling social phenomena, such as segregation, in the social world. Our chapter addresses in particular analytical sociologists who are new to ABMs.

In the first model (SegregationExtended), we build on Wilensky’s implementation of Schelling’s model which is available in NetLogo library (Wilensky, 1997). We considerably extend this model, allowing in particular to include larger neighborhoods and a population with four groups roughly resembling the ethnic composition of a contemporary large U.S. city. Further features added concern the possibility to include random noise, and the addition of a number of new outcome measures tuned to highlight macro-level implications of the segregation dynamics for different groups in the agent society.

In SegregationDiscreteChoice, we further modify the model incorporating in particular three new features: 1) heterogeneous preferences roughly based on empirical research categorizing agents into low, medium, and highly tolerant within each of the ethnic subgroups of the population, 2) we drop global thresholds (%-similar-wanted) and introduce instead a continuous individual-level single-peaked preference function for agents’ ideal neighborhood composition, and 3) we use a discrete choice model according to which agents probabilistically decide whether to move to a vacant spot or stay in the current spot by comparing the attractiveness of both locations based on the individual preference functions.

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