Organizations are complex systems comprised of many dynamic and evolving interaction patterns among individuals and groups. Understanding these interactions and how patterns, such as informal structures and knowledge sharing behavior, emerge are crucial to creating effective and efficient organizations. To explore such organizational dynamics, the agent-based model integrates a cognitive model, dynamic social networks, and a physical environment.

This model allows for analyzing the most efficient levers for enhancing the use of recycled construction materials, and the role of empirically based decision parameters.

The goal of the AG-Innovation agent-based model is to explore and compare the effects of two alternative mechanisms of innovation development and diffusion (exogenous, linear and endogenous, non-linear) on emergent properties of food and income distribution and adoption rates of different innovations. The model also assesses the range of conditions under which these two alternative mechanisms would be effective in improving food security and income inequality outcomes. Our modelling questions were: i) How do cross-scalar social-ecological interactions within agricultural innovation systems affect system outcomes of food security and income inequality? ii) Do foreign aid-driven exogenous innovation perpetuate income inequality and food insecurity and if so, under which conditions? iii) Do community-driven endogenous innovations improve food security and income inequality and if so, under which conditions? The Ag-Innovation model is intended to serve as a thinking tool for for the development and testing of hypotheses, generating an understanding of the behavior of agricultural innovation systems, and identifying conditions under which alternated innovation mechanisms would improve food security and income inequality outcomes.

An artifcal stock market model that allows users to vary the number of risky assets as well as the network topology that investors forms in an attempt to understand the dynamics of the market.

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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Grasslands have a large share of the world’s land cover and their sustainable management is important for the protection and provisioning of grassland ecosystem services. The question of how to manage grassland sustainably is becoming increasingly important, especially in view of climate change, which on the one hand extends the vegetation period (and thus potentially allows use intensification) and on the other hand causes yield losses due to droughts. Fertilization plays an important role in grassland management and decisions are usually made at farm level. Data on fertilizer application rates are crucial for an accurate assessment of the effects of grassland management on ecosystem services. However, these are generally not available on farm/field scale. To close this gap, we present an agent-based model for Fertilization In Grasslands (FertIG). Based on animal, land-use, and cutting data, the model estimates grassland yields and calculates field-specific amounts of applied organic and mineral nitrogen on grassland (and partly cropland). Furthermore, the model considers different legal requirements (including fertilization ordinances) and nutrient trade among farms. FertIG was applied to a grassland-dominated region in Bavaria, Germany comparing the effects of changes in the fertilization ordinance as well as nutrient trade. The results show that the consideration of nutrient trade improves organic fertilizer distribution and leads to slightly lower Nmin applications. On a regional scale, recent legal changes (fertilization ordinance) had limited impacts. Limiting the maximum applicable amount of Norg to 170 kg N/ha fertilized area instead of farm area as of 2020 hardly changed fertilizer application rates. No longer considering application losses in the calculation of fertilizer requirements had the strongest effects, leading to lower supplementary Nmin applications. The model can be applied to other regions in Germany and, with respective adjustments, in Europe. Generally, it allows comparing the effects of policy changes on fertilization management at regional, farm and field scale.

This is a simple model replicating Hardin’s Tragedy of the Commons using reactive agents that have psychological behavioral and social preferences.

Cultural group selection model used to evaluate the conditions for agents to evolve who have other-regarding preferences in making decisions in public good games.

This multi-model (i.e. a model composed of interacting submodels) is a multi-level representation of a collective motion phenomenon. It was designed to study the impact of the mutual influences between individuals and groups in collective motion.

PPHPC is a conceptual model for studying and evaluating implementation strategies for spatial agent-based models (SABMs). It is a realization of a predator-prey dynamic system, and captures important SABMs characteristics.