Our model shows how disinformation spreads on a random network of individuals. The network is weighted and directed. We are looking at how different factors affect how fast, or how many people get “infected” with the misinformation. One of the main factors that we were curious about was perceived trustworthiness. This is because we want to see if people of power, or a high degree of perceived trustworthiness, were able to push misinformation to more people and convert more people to believe the information.

The purpose of the AdaptPumpa model is to analyze the robustness of the Pumpa irrigation system in Nepal to climate change.

This model illustrates the processes underlying the social construction of reality through an agent-based genetic algorithm. By simulating the interactions of agents within a structured environment, we have demonstrated how shared information and popularity contribute to the formation of emergent social structures with diverse cultures. The model illustrates how agents balance environmentally valid information with socially reliable information. It also highlights how social interaction leads to the formation of stable, yet diverse, social groups.

This is an agent-based model with two types of agents: customers and insurers. Insurers are price-takers who choose how much to spend on their service quality, and customers evaluate insurers based on premium, brand preference, and their perceived service quality. Customers are also connected in a small-world network and may share their opinions with their network.

The ABM contains two types of agents: insurers and customers. These act within the environment of a motor insurance market. At each simulation, the model undergoes the following steps:

1. Network generation: At the start of the simulation, the model generates a small world network of social links between the customers, and randomly assigns each customer to an initial insurer
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This model simulates the motion picture industry and tests how social influences affect market shares. It is empirically validated at the micro level by a cross-cultural survey.

This model reimplement Weiner et al. 2001 Zone Of Influence model to simulate plant growth under competition. The reimplementation in Netlogo and the ODD description in the “info” tab try to be as consistent as possible with the original paper.

AgentEx aims to advance understanding of group processes for sustainable management of a common pool resource (CPR). By supporting the development and test explanations of cooperation and sustainable exploitation.

This model simulates the dynamics of agricultural land use change, specifically the transition between agricultural and non-agricultural land use in a spatial context. It explores the influence of various factors such as agricultural profitability, path dependency, and neighborhood effects on land use decisions.

The model operates on a grid of patches representing land parcels. Each patch can be in one of two states: exploited (green, representing agricultural land) or unexploited (brown, representing non-agricultural land). Agents (patches) transition between these states based on probabilistic rules. The main factors affecting these transitions are agricultural profitability, path dependency, and neighborhood effects.
-Agricultural Profitability: This factor is determined by the prob-agri function, which calculates the probability of a non-agricultural patch converting to agricultural based on income differences between agriculture and other sectors. -Path Dependency: Represented by the path-dependency parameter, it influences the likelihood of patches changing their state based on their current state. It’s a measure of inertia or resistance to change. -Neighborhood Effects: The neighborhood function calculates the number of exploited (agricultural) neighbors of a patch. This influences the decision of a patch to convert to agricultural land, representing the influence of surrounding land use on the decision-making process.

Scilab version of an agent-based model of societal well-being, based on the factors of: overvaluation of conspicuous prosperity; tradeoff rate between inconspicuous/conspicuous well-being factors; turnover probability; and individual variation.

This code simulates the WiFi user tracking system described in: Thron et al., “Design and Simulation of Sensor Networks for Tracking Wifi Users in Outdoor Urban Environments”. Testbenches used to create the figures in the paper are included.