An ABM to simulate the spatio-temporal distribution of cyclists across the road network of the city of Salzburg.

This abstract model explores the emergence of altruistic behavior in networked societies. The model allows users to experiment with a number of population-level parameters to better understand what conditions contribute to the emergence of altruism.

We compare the effect of four activation regimes by measuring the appropriate opinion clustering statistics and also the number of emergent extremists.

This code simulates the WiFi user tracking system described in: Thron et al., “Design and Simulation of Sensor Networks for Tracking Wifi Users in Outdoor Urban Environments”. Testbenches used to create the figures in the paper are included.

MigrAgent simulates migration flows of a population from a home country to a host country and mutual adaptation of a migrant and local population post-migration. Agents accept interactions in intercultural networks depending on their degree of conservatism. Conservatism is a group-level parameter normally distributed within each ethnic group. Individual conservatism changes as function of reciprocity of interaction in intergroup experiences of acceptance or rejection.

The aim of MigrAgent is to unfold different outcomes of integration, assimilation, separation and marginalization in terms of networks as effect of different degrees of conservatism in each group and speed of migration flows.

While the world’s total urban population continues to grow, this growth is not equal. Some cities are declining, resulting in urban shrinkage which is now a global phenomenon. Many problems emerge due to urban shrinkage including population loss, economic depression, vacant properties and the contraction of housing markets. To explore this issue, this paper presents an agent-based model stylized on spatially explicit data of Detroit Tri-county area, an area witnessing urban shrinkage. Specifically, the model examines how micro-level housing trades impact urban shrinkage by capturing interactions between sellers and buyers within different sub-housing markets. The stylized model results highlight not only how we can simulate housing transactions but the aggregate market conditions relating to urban shrinkage (i.e., the contraction of housing markets). To this end, the paper demonstrates the potential of simulation to explore urban shrinkage and potentially offers a means to test polices to alleviate this issue.

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

PopComp by Andre Costopoulos 2020
[email protected]
Licence: DWYWWI (Do whatever you want with it)

I use Netlogo to build a simple environmental change and population expansion and diffusion model. Patches have a carrying capacity and can host two kinds of populations (APop and BPop). Each time step, the carrying capacity of each patch has a given probability of increasing or decreasing up to a maximum proportion.

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A hybrid predator-prey model of fish and plankton that switches dynamically between ABM and SD representations. It contains 6 related structural designs of the same model.

The teamCognition model investigates team decision processes by using an agent-based model to conceptualize team decisions as an emergent property. It uses a mixed-method research design with a laboratory experiment providing qualitative and quantitative input for the model’s construction, as well as data for an output validation of the model. The agent-based model is used as a computational testbed to contrast several processes of team decision making, representing potential, simplified mechanisms of how a team decision emerges. The increasing overall fit of the simulation and empirical results indicates that the modeled decision processes can at least partly explain the observed team decisions.