This model aims to replicate the evolution of opinions and behaviours on a communal plan over time. It also aims to foster community dialogue on simulation outcomes, promoting inclusivity and engagement. Individuals (referred to as agents), grouped based on Sinus Milieus (Groh-Samberg et al., 2023), face a binary choice: support or oppose the plan. Motivated by experiential, social, and value needs (Antosz et al., 2019), their decision is influenced by how well the plan aligns with these fundamental needs.

The FRAMe (Flood Resilience Agent-Based Model) serves as a framework designed to simulate flood resilience dynamics at the community level, focusing on a rural settlement in the Mekong River Basin. Integrating empirical data from extensive surveys, Bayesian networks, and hydrological simulations, the framework quantifies resilience as a trade-off between robustness (resistance to damage) and adaptability (capacity for dynamic response). Agents include households, governments, and other actors, linked by social and governance networks that facilitate knowledge transfer, resource distribution, and risk communication. FRAMe incorporates mechanisms for flood forecasting, policy interventions (education, aid, insurance), and individual and collective decision-making, grounded in Protection Motivation Theory and MoHuB frameworks. The framework’s spatially explicit design leverages GIS data, which supports scenario testing of governance structures and stakeholder interactions. By examining policy scenarios and agent behavior, FRAMe aims to inform adaptive flood management strategies and enhance community resilience.

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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This Python module contain a function that is able to test the ergodicity of a given agent based model. It is sufficient to produce one long time series and many smaller time series. The function uses

Demographic microsimulation model used in speed tests against LIAM 2.

BESTMAP-ABM-DE is an agent-based model to determine the adoption and spatial allocation of selected agri-environmental schemes (AES) by individual farmers in the Mulde River Basin located in Western Saxony, Germany. The selected AES are buffer areas, cover crops, maintaining permanent grassland and conversion of arable land to permanent grassland. While the first three schemes have already been offered in the case study area, the latter scheme is a hypothetical scheme designed to test the impact of potential policy changes. For the first model analyses, only the currently offered schemes are considered. With the model, the effect of different scenarios of policy design on patterns of adoption can be investigated. In particular, the model can be used to study the social-ecological consequences of agricultural policies at different spatial and temporal scales and, in combination with biophysical models, test the ecological implications of different designs of the EU’s Common Agricultural Policy. The model was developed in the BESTMAP project.

This is a model of a game of Telephone (also known as Chinese Whishpers in the UK), with agents representing people that can be asked, to play. The first player selects a word from their internal vocabulary and “whispers” it to the next player, who may mishear it depending on the current noise level, who whispers that word to the next player, and so on.

When the game ends, the word chosen by the first player is compared to the word heard by the last player. If they match exactly, all players earn large prize. If the words do not match exactly, a small prize is awarded to all players for each part of the words that do match. Players change color to reflect their current prize-count. A histogram shows the distribution of colors over all the players.

The user can decide on factors like
* how many players there are,
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This model represents an agent-based social simulation for citizenship competences. In this model people interact by solving different conflicts and a conflict is solved or not considering two possible escenarios: when individual citizenship competences are considered and when not. In both cases the TKI conflict resolution styles are considered. Each conflict has associated a competence and the information about the conflicts and their competences is retrieved from an ontology which was developed in Protégé. To do so, a NetLogo extension was developed using the Java programming language and the JENA API (to make queries over the ontology).

The Simulating Agroforestry Adoption in Rural Indonesia (SAFARI) model aims at exploring the adoption of illipe rubber agroforestry systems by farming households in the case study region in rural Indonesia. Thereby, the ABM simulates the interdependencies of agroforestry systems and local livelihoods, income, land use, biodiversity, and carbon fixation. The model contrasts development paths without agroforestry (business as usual (BAU) scenario), corresponding to a scenario where the government promotes rubber monoculture, with the introduction of illipe rubber agroforestry systems (IRA scenario) as an alternative. It aims to support policy-makers to assess the potential of IRA over larger temporal and spatial scales.

The purpose of the model is to simulate the cultural hitchhiking hypothesis to explore how neutral cultural traits linked with advantageous traits spread together over time