This is a generic sub-model of animal territory formation. It is meant to be a reusable building block, but not in the plug-and-play sense, as amendments are likely to be needed depending on the species and region. The sub-model comprises a grid of cells, reprenting the landscape. Each cell has a “quality” value, which quantifies the amount of resources provided for a territory owner, for example a tiger. “Quality” could be prey density, shelter, or just space. Animals are located randomly in the landscape and add grid cells to their intial cell until the sum of the quality of all their cells meets their needs. If a potential new cell to be added is owned by another animal, competition takes place. The quality values are static, and the model does not include demography, i.e. mortality, mating, reproduction. Also, movement within a territory is not represented.

Studies of colonization processes in past human societies often use a standard population model in which population is represented as a single quantity. Real populations in these processes, however, are structured with internal classes or stages, and classes are sometimes created based on social differentiation. In this present work, information about the colonization of old Providence Island was used to create an agent-based model of the colonization process in a heterogeneous environment for a population with social differentiation. Agents were socially divided into two classes and modeled with dissimilar spatial clustering preferences. The model and simulations assessed the importance of gregarious behavior for colonization processes conducted in heterogeneous environments by socially-differentiated populations. Results suggest that in these conditions, the colonization process starts with an agent cluster in the largest and most suitable area. The spatial distribution of agents maintained a tendency toward randomness as simulation time increased, even when gregariousness values increased. The most conspicuous effects in agent clustering were produced by the initial conditions and behavioral adaptations that increased the agent capacity to access more resources and the likelihood of gregariousness. The approach presented here could be used to analyze past human colonization events or support long-term conceptual design of future human colonization processes with small social formations into unfamiliar and uninhabited environments.

This model looks at the effects of a “control” on agent populations. Much like farmers spraying pesticides/herbicides to manage pest populations, the user sets a control management regiment to be use

Demographic microsimulation model used in speed tests against LIAM 2.

This model illustrates a positive ‘growth’ feedback loop in which the areal extent of an entity increases through time.

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.

Current trends suggest that when individuals of different cultural backgrounds encounter one another, their social categories become entangled and create new hybridized or creole identities.

Simulates the construction of scientific journal publications, including authors, references, contents and peer review. Also simulates collective learning on a fitness landscape. Described in: Watts, Christopher & Nigel Gilbert (forthcoming) “Does cumulative advantage affect collective learning in science? An agent-based simulation”, Scientometrics.