FIsheries Simulation with Human COmplex DEcision-making (FISHCODE) is an agent-based model to depict and analyze current and future spatio-temporal dynamics of three German fishing fleets in the southern North Sea. Every agent (fishing vessel) makes daily decisions about if, what, and how long to fish. Weather, fuel and fish prices, as well as the actions of their colleagues influence agents’ decisions. To combine behavioral theories and enable agents to make dynamic decision, we implemented the Consumat approach, a framework in which agents’ decisions vary in complexity and social engagement depending on their satisfaction and uncertainty. Every agent has three satisfactions and two uncertainties representing different behavioral aspects, i.e. habitual behavior, profit maximization, competition, conformism, and planning insecurity. Availability of extensive information on fishing trips allowed us to parameterize many model parameters directly from data, while others were calibrated using pattern oriented modelling. Model validation showed that spatial and temporal aggregated ABM outputs were in realistic ranges when compared to observed data. Our ABM hence represents a tool to assess the impact of the ever growing challenges to North Sea fisheries and provides insight into fisher behavior beyond profit maximization.

We propose an agent-based model leading to a decrease or an increase of hostility between agents after a major cultural threat such as a terrorist attack. The model is inspired from the Terror Management Theory and the Social Judgement Theory. An agent has a cultural identity defined through its acceptance segments about each of three different cultural worldviews (i.e., Atheist, Muslim, Christian) of the considered society. An agent’s acceptance segment is composed from its acceptable positions toward a cultural worldview, including its most acceptable position. An agent forms an attitude about another agent depending on the similarity between their cultural identities. When a terrorist attack is perpetrated in the name of an extreme cultural identity, the negatively perceived agents from this extreme cultural identity point of view tend to decrease the width of their acceptance segments in order to differentiate themselves more from the threatening cultural identity

To our knowledge, this is the first agent-based simulation of continuous-time PGGs (where participants can change contributions at any time) which are much harder to realise within both laboratory and simulation environments.

Work related to this simulation has been published in the following journal article:
Vu, Tuong Manh, Wagner, Christian and Siebers, Peer-Olaf (2019) ‘ABOOMS: Overcoming the Hurdles of Continuous-Time Public Goods Games with a Simulation-Based Approach’ Journal of Artificial Societies and Social Simulation 22 (2) 7 http://jasss.soc.surrey.ac.uk/22/2/7.html. doi: 10.18564/jasss.3995

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A hybrid predator-prey model of fish and plankton that switches dynamically between ABM and SD representations. It contains 6 related structural designs of the same model.

The simulation model SimPLS shows an application of the PLS agent concept, using SEM as empirical basis for the definition of agent architectures. The simulation model implements the PLS path model TAM about the decision of using innovative products.

The Social Identity Model of Protest Emergence (SIMPE), an agent-based model of national identity and protest mobilisations.

I developed this model for my PhD project, “Polarisation and Protest Mobilisation Around Secessionist Movements: an Agent-Based Model of Online and Offline Social Networks”, at the University of Glasgow (2019-2023).

The purpose of this model is to simulate protest emergence in a given country where there is an independence movement, fostering the self-categorisation process of national identification. In order to contextualised SIMPE, I have used Catalonia, where an ongoing secessionist movement since 2011 has been present, national identity has shown signs of polarisation, and where numerous mobilisations have taken place over the last decade. Data from the Catalan Centre of Opinion Studies (CEO) has been used to inform some of the model parameters.

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The model demonstrates how non-instantaneous sampling techniques produce bias by overestimating the number of counted animals, when they move relative to the person counting them.

This multi-model (i.e. a model composed of interacting submodels) is a multi-level representation of a collective motion phenomenon. It was designed to study the impact of the mutual influences between individuals and groups in collective motion.

This model explores a social mechanism that links the reversal of the gender gap in education with changing patterns in relative divorce risks in 12 European countries.

The HUMan Impact on LANDscapes (HUMLAND) 2.0.0 is an enhanced version of HUMLAND 1.0.0, developed to track and quantify the intensity of various impacts on landscapes at a continental scale. The model is designed to identify the most influential factors in the transformation of interglacial vegetation, with a particular focus on the burning practices of hunter-gatherers. HUMLAND 2.0.0 incorporates a wide range of spatial datasets as both inputs and targets (expected modelling results) for simulations across Last Interglacial (~130,000–116,000 BP) and Early Holocene (~11,700–8,000 BP).