MayaSim is an agent-based, cellular automata and network model of the ancient Maya. Biophysical and anthropogenic processes interact to grow a complex social ecological system.

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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CoDMER v. 2.0 was parameterized with ethnographic data from organizations dealing with prescribed fire and seeding native plants, to advance theory on how collective decisions emerge in ecological restoration.

This model implements two types of network diffusion from an initial group of activated nodes. In complex contagion, a node is activated if the proportion of neighbour nodes that are already activated exceeds a given threshold. This is intended to represented the spread of health behaviours. In simple contagion, an activated node has a given probability of activating its inactive neighbours and re-tests each time step until all of the neighbours are activated. This is intended to represent information spread.

A range of networks are included with the model from secondary school friendship networks. The proportion of nodes initially activated and the method of selecting those nodes are controlled by the user.

a computer-based role-playing game simulating the interactions between farming activities, livestock herding and wildlife in a virtual landscape reproducing local socioecological dynamics at the periphery of Hwange National Park (Zimbabwe).

Purpose of the model is to perform a “virtual experiment” to test the predator satiation hypothesis, advanced in literature to explain the mast seeding phenomenon.

Designed to capture the evolutionary forces of global society.

This Bicycle encounter model builds on the Salzburg Bicycle model (Wallentin & Loidl, 2015). It simulates cyclist flows and encounters, which are locations of potential accidents between cyclists.

The model of Chinese and Western civilization patterns can help understand how civilizations formed, how they evolved by themselves, and the difference between the unity of China and the disunity of the Western. The previous research had examined historical phenomena about civilization patterns with subjective, static, local, and inductive methods. Therefore, we propose a general model of history dynamics for civilizations pattern, which contains both China and the West, to improve our understanding of civilization formation and the factors influencing the pattern of civilization. And at the same time, the model is used to find the boundary conditions of two different patterns.

This model aims to investigate how different type of learning (social system) and disturbance specific attributes (ecological system) influence adoption of treatment strategies to treat the effects of ecological disturbances.