The purpose of the Digital Mobility Model (DMM) is to explore how a society’s adoption of digital technologies can impact people’s mobilities and immobilities within an urban environment. Thus, the model contains dynamic agents with different levels of digital technology skills, which can affect their ability to access urban services using digital systems (e.g., healthcare or municipal public administration with online appointment systems). In addition, the dynamic agents move within the model and interact with static agents (i.e., places) that represent locations with different levels of digitalization, such as restaurants with online reservation systems that can be considered as a place with a high level of digitalization. This indicates that places with a higher level of digitalization are more digitally accessible and easier to reach by individuals with higher levels of digital skills. The model simulates the interaction between dynamic agents and static agents (i.e., places), which captures how the gap between an individual’s digital skills and a place’s digitalization level can lead to the mobility or immobility of people to access different locations and services.

The model represents empirically observed recycling behaviour of Chinese citizens, based on the theory of reasoned action (TRA), the theory of planned behaviour (TPB) and the theory of planned behaviour extended with situational factors (TPB+).

Both models simulate n-person prisoner dilemma in groups (left figure) where agents decide to C/D – using a stochastic threshold algorithm with reinforcement learning components. We model fixed (single group ABM) and dynamic groups (bad-barrels ABM). The purpose of the bad-barrels model is to assess the impact of information during meritocratic matching. In the bad-barrels model, we incorporated a multidimensional structure in which agents are also embedded in a social network (2-person PD). We modeled a random and homophilous network via a random spatial graph algorithm (right figure).

We provide an agent-based model of collective action, informed by Granovetter (1978) and its replication model by Siegel (2009). We use the model to examine the role of ICTs in collective action under different cultural and political contexts.

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).

Simulation-Framework to study the governance of complex, network-like sociotechnical systems by means of ABM. Agents’ behaviour is based on a sociological model of action. A set of basic governance mechanisms helps to conduct first experiments.

The purpose of this model is to enhance a basic ABM through a simple set of rules identified using the activity-driven models in order to produce more realistic patterns of pedestrian movement.

The original Ache model is used to explore different distributions of resources on the landscape and it’s effect on optimal strategies of the camps on hunting and camp movement.

ABSAM model is an agent-based search and matching model of the local labor market. There are four types of agents in the economy, which cooperate in the artificial world, where behavioral rules were extracted from the labor market search theory.

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.