“Food for all” (FFD) is an agent-based model designed to study the evolution of cooperation for food storage. Households face the social dilemma of whether to store food in a corporate stock or to keep it in a private stock.

This purpose of this model is to understand how the coupled demographic dynamics of herds and households constrain the growth of livestock populations in pastoral systems.

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

Provided is a landscape of properties where pastoralists make decisions how much livestock they put on their property and how much to suppress fire from occuring. Rangelands can be grass dominated, or unproductive shrubb dominated. Overgrazing and fire suppresion lead to shrub dominated landscapes. What management strategies evolve, and how is this impacted by policies?
The model is discussed in Introduction to Agent-Based Modeling by Marco Janssen. For more information see https://intro2abm.com/.

This model simulates a group of farmers that have encounters with individuals of a wildlife population. Each farmer owns a set of cells that represent their farm. Each farmer must decide what cells inside their farm will be used to produce an agricultural good that is self in an external market at a given price. The farmer must decide to protect the farm from potential encounters with individuals of the wildlife population. This decision in the model is called “fencing”. Each time that a cell is fenced, the chances of a wildlife individual to move to that cell is reduced. Each encounter reduces the productive outcome obtained of the affected cell. Farmers, therefore, can reduce the risk of encounters by exclusion. The decision of excluding wildlife is made considering the perception of risk of encounters. In the model, the perception of risk is subjective, as it depends on past encounters and on the perception of risk from other farmers in the community. The community of farmers passes information about this risk perception through a social network. The user (observer) of the model can control the importance of the social network on the individual perception of risk.

3spire is an ABM where farming households make management decisions aimed at satisficing along the aspirational dimensions: food self-sufficiency, income, and leisure. Households decision outcomes depend on their social networks, knowledge, assets, household needs, past management, and climate/market trends

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 DMASON parallel ABMS platform, and we use it for comparing the applications generated by these platforms.

The model simulates the decisions of residents and a water authority to respond to socio-hydrological hazards. Residents from neighborhoods are located in a landscape with topographic complexity and two problems: water scarcity in the peripheral neighborhoods at high altitude and high risk of flooding in the lowlands, at the core of the city. The role of the water authority is to decide where investments in infrastructure should be allocated to reduce the risk to water scarcity and flooding events in the city, and these decisions are made via a multi-objective site selection procedure. This procedure accounts for the interdependencies and feedback between the urban landscape and a policy scenario that defines the importance, or priorities, that the authority places on four criteria.
Neighborhoods respond to the water authority decisions by protesting against the lack of investment and the level of exposure to water scarcity and flooding. Protests thus simulate a form of feedback between local-level outcomes (flooding and water scarcity) and higher-level decision-making. Neighborhoods at high altitude are more likely to be exposed to water scarcity and lack infrastructure, whereas neighborhoods in the lowlands tend to suffer from recurrent flooding. The frequency of flooding is also a function of spatially uniform rainfall events. Likewise, neighborhoods at the periphery of the urban landscape lack infrastructure and suffer from chronic risk of water scarcity.
The model simulates the coupling between the decision-making processes of institutional actors, socio-political processes and infrastructure-related hazards. In the documentation, we describe details of the implementation in NetLogo, the description of the procedures, scheduling, and the initial conditions of the landscape and the neighborhoods.
This work was supported by the National Science Foundation under Grant No. 1414052, CNH: The Dynamics of Multi-Scalar Adaptation in Megacities (PI Hallie Eakin).