The Sediba socio-ecolgoical rangeland model is an biomass growth model coupled with a social model of pastoralist behaviour in a commmon pool resource setting. The social subsystem is an empircal ABM.

The purpose of this model is to project the dynamics of technology adoption of autonomous weeding robots by sugar beet producing farmers in North Rhine-Westphalia (NRW). Moreover, the design of the model serves the purpose to investigate second-order effects of robot adoption on shifts in farm income and on production quantities of main crops produced in North Rhine-Westphalia. One aim is to analyse the impact of technology attributes and costs of pesticides on adoption patterns.

Due to teacher requests to represent changes in atomic radius, we developed a visualization of the first 36 elements in Netlogo

The purpose of this model is to analyze the dynamics of endogenously created oscillations in housing prices using a system dynamics simulation model, built from the perspective of construction companies.

ABM model studying impact of social cohesion on wellbeing of a society. Ibn Khaldun’s cyclical theory of history is being used as the theoretical lens along with some other theories. Social cohesion is measured as TSC = (TVE + 2 * (TPI * TPL - TNI * TNL))/((TPI+TNI))
Where
TSC total-social-cohesion ; Variable for social cohesion
TPI total-positive-interactions ; Count of positive interactions
TNI total-negative-interactions ; Count of negative interactions
TPL total-positive-learning ; Count of positive learning outcomes
…

This model was developed to study the combination of electric vehicles (EVs) and intermitten renewable energy sources. The model presents an EV fleet in a fictional area, divided into a residential area, an office area and commercial area. The area has renewable energy sources: wind and PV solar panels. The agents can be encouraged to charge their electric vehicles at times of renewable energy surplus by introducing different policy interventions. Other interesting variables in the model are the installed renewable energy sources, EV fleet composition and available charging infrastructure. Where possible, use emperical data as input for our model. We expand upon previous models by incorporating environmental self-identity and range anxiety as agent variables.

The Agent-Based Wildfire Simulation Environment (ABWiSE) translates the concept of a moving fire front as a set of mobile fire agents that respond to, and interact with, vegetation, wind, and terrain. Presently, the purpose of ABWiSE is to explore how ABM, using simple interactions between agents and a simple atmospheric feedback model, can simulate emergent fire spread patterns.

This is a NetLogo 7.0.1 agent-based model of interdependent team productivity under O-ring production logic. The model asks when collective problem-solving capacity (CPS) improves team output and when its effect is constrained by trust, burnout, specialization diversity, weak-link quality, and team-formation rules. Agents are heterogeneous in skill, CPS, trust, burnout, effort, learning rate, openness, specialization, adaptability, and aspiration. Each tick forms temporary teams, computes individual contribution quality, combines contributions through a geometric O-ring production function with an explicit weakest-link term, records output and success, and updates agents through feedback, learning, trust change, burnout, recovery, and turnover. The paper follows the ODD protocol, reports exact implementation equations, and analyzes six BehaviorSpace experiments with 50 stochastic repetitions per condition. Scenario results show that CPS is beneficial but not sufficient: productivity is highest when CPS is combined with trust, low burnout, effective coordination, and a reliable weakest-link floor. Diversity yields only modest gains at low CPS but larger gains when CPS is high. Formation-mode results are especially informative because mixed CPS-skill formation maximizes total output, whereas random formation has the highest binary success rate, showing that output magnitude and threshold success can diverge. The study contributes a transparent computational mechanism linking collective intelligence with O-ring production and identifies boundary conditions under which high-CPS teams can still be limited by weak links and interdependence.

Our model allows simulating repeated conservation auctions in low-income countries. It is designed to assess policy-making by exploring the extent to which non-targeted repeated auctions can provide biodiversity conservation cost-effectively, while alleviating poverty. Targeting landholders in order to integrate both goals is claimed to be overambitious and underachieving because of the trade-offs they imply. The simulations offer insight on the possible outcomes that can derive from implementing conservation auctions in low-income countries, where landholders are likely to be risk averse and to face uncertainty.

The model simulates the diffusion of four low-carbon energy technologies among households: photovoltaic (PV) solar panels, electric vehicles (EVs), heat pumps, and home batteries. We model household decision making as the decision marking of one person, the agent. The agent decides whether to adopt these technologies. Hereby, the model can be used to study co-adoption behaviour, thereby going beyond traditional diffusion models that focus on the adop-tion of single technologies. The combination of these technologies is of particular interest be-cause (1) using the energy generated by PV solar panels for EVs and heat pumps can reduce emissions associated with transport and heating, respectively, and (2) EVs, heat pumps, and home batteries can help to integrate PV solar panels in local electricity grids by offering flexible demand (EVs and heat pumps) and energy storage (home batteries and EVs), thereby reducing grid impacts and associated upgrading costs.

The purpose of the model is to represent realistic adoption and co-adoption behaviour. This is achieved by grounding the decision model on the risks-as-feelings model (Loewenstein et al., 2001), theory from environmental and social psychology, and empirically informing agent be-haviour by survey-data among 1469 people in the Swiss region Romandie.

The model can be used to construct scenarios for the diffusion of the four low-carbon energy technologies depending on different contexts, and as a virtual experimentation environment for ex ante evaluation of policy interventions to stimulate adoption and co-adoption.