We provide a full description of the model following the ODD protocol (Grimm et al. 2010) in the attached document. The model is developed in NetLogo 5.0 (Wilenski 1999).

We represent commuters and their preferences for transportation cost, time and safety. Agents assess their options via their preferences, their environment, and the modes available. The model has policy levers to test impact on last-mile problem.

In the “World of Cows”, dairy farmers run their farms and interact with each other, the surrounding agricultural landscape, and the economic and political framework. The model serves as an exemplary case of an interdependent human-environment system.

With the model, users can analyze the influence of policies and markets on land use decisions of dairy farms. The land use decisions taken by farms determine the delivered ecosystem services on the landscape level. Users can choose a combination of five policy options and how strongly market prices fluctuate. Ideally, the choice of policy options fulfills the following three “political goals” 1) dairy farming stays economically viable, 2) the provision of ecosystem services is secured, and 3) government spending on subsidies is as low as possible.

The model has been designed for students to practice agent-based modeling and analyze the impacts of land use policies.

The HUMan impact on LANDscapes (HUMLAND) model has been developed to track and quantify the intensity of different impacts on landscapes at the continental level. This agent-based model focuses on determining the most influential factors in the transformation of interglacial vegetation with a specific emphasis on burning organized by hunter-gatherers. HUMLAND integrates various spatial datasets as input and target for the agent-based model results. Additionally, the simulation incorporates recently obtained continental-scale estimations of fire return intervals and the speed of vegetation regrowth. The obtained results include maps of possible scenarios of modified landscapes in the past and quantification of the impact of each agent, including climate, humans, megafauna, and natural fires.

The model aims at reproducing the evolution of the land-use in an agricultural territory at the plot scale. It enables to simulate the affectation of land-use, the crop rotation and technical operations for each plot of the different farms of the territory. It allows as well for crop farms to simulate the daily state of plots (sowed, plowed, harvested, biomass indicator). The model is used as an input for the water pollution model allowing to determine the flow of nitrate, phosphorus and suspended matter in the territory according to the landscape configuration.

Agent-based modeling and simulation (ABMS) is a class of computational models for simulating the actions and interactions of autonomous agents with the goal of assessing their effects on a system as a whole. Several frameworks for generating parallel ABMS applications have been developed taking advantage of their common characteristics, but there is a lack of a general benchmark for comparing the performance of generated applications. We propose and design a benchmark that takes into consideration the most common characteristics of this type of applications and includes parameters for influencing their relevant performance aspects. We provide an initial implementation of the benchmark for RepastHPC one of the most popular parallel ABMS platforms, and we use it for comparing the applications generated by these platforms.

Agent-based modeling and simulation (ABMS) is a class of computational models for simulating the actions and interactions of autonomous agents with the goal of assessing their effects on a system as a whole. Several frameworks for generating parallel ABMS applications have been developed taking advantage of their common characteristics, but there is a lack of a general benchmark for comparing the performance of generated applications. We propose and design a benchmark that takes into consideration the most common characteristics of this type of applications and includes parameters for influencing their relevant performance aspects. We provide an initial implementation of the benchmark for FLAME one of the most popular parallel ABMS platforms, and we use it for comparing the applications generated by these platforms.

Contact Tracing Repast HPC agent model

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 presents an autonomous, two-lane driving environment with a single lane-closure that can be toggled. The four driving scenarios - two baseline cases (based on the real-world) and two experimental setups - are as follows:

• Baseline-1 is where cars are not informed of the lane closure.
• Baseline-2 is where a Red Zone is marked wherein cars are informed of the lane closure ahead.
• Strategy-1 is where cars use a co-operative driving strategy - FAS. ^[1]
• Strategy-2 is a variant of Strategy-1 and uses comfortable deceleration values instead of the vehicle’s limit.
  …