LethalGeometry was developed to examine whether territory size influences the mortality risk for individuals within that territory. For animals who live in territoral groups and are lethally aggressive, we can expect that most aggression occurs along the periphery (or border) between two adjacent territories. For territories that are relatively large, the periphery makes up a proportionately small amount of the of the total territory size, suggesting that individuals in these territories might be less likely to die from these territorial skirmishes. LethalGeometry examines this geometric relationship between territory size and mortality risk under realistic assumptions of variable territory size and shape, variable border width, and stochastic interactions and movement.

The individuals (agents) are programmed to walk randomly about their environment, search for and eat food to obtain energy, reproduce if they can, and act aggressively toward individuals of other groups. During each simulation step, individuals analyze their environment and internal state to determine which actions to take. The actions available to individuals include moving, fighting, and giving birth.

GenoScope is a modular agent-based model designed to simulate how cells respond to environmental stressors or other treatment conditions across species. Genes, treatment conditions, and cell physiology outcomes are represented as interacting agents that influence each other’s behavior over time. Rather than imposing fixed interaction rules, GenoScope initializes with randomized regulatory logic and calibrates rule sets based on empirical data. Calibration is grounded in a common-garden experiment involving 16 mammalian species—including humans, dolphins, bats, and camels—exposed to varying levels of temperature, glucose, and oxygen. This comparative approach enables the identification of mechanisms by which animal cells achieve robustness under extreme environmental conditions.

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.

We seek to improve understanding of roles enzyme play in soil food webs. We created an agent-based simulation of a simple food web that includes enzymatic activity. The model was used in a publication, Moore et al. (in press; Biochemistry).

MoPAgrIB model simulates the movement of cultivated patches in a savannah vegetation mosaic ; how they move and relocate through the landscape, depending on farming practices, population growth, social rules and vegetation growth.

We present an agent-based model that maps out and simulates the processes by which individuals within ecological restoration organizations communicate and collectively make restoration decisions.

This is an extension of the original RAGE model (Dressler et al. 2018), where we add learning capabilities to agents, specifically learning-by-doing and social learning (two processes central to adaptive (co-)management).

The extension module is applied to smallholder farmers’ decision-making - here, a pasture (patch) is the private property of the household (agent) placed on it and there is no movement of the households. Households observe the state of the pasture and their neighrbours to make decisions on how many livestock to place on their pasture every year. Three new behavioural types are created (which cannot be combined with the original ones): E-RO (baseline behaviour), E-LBD (learning-by-doing) and E-RO-SL1 (social learning). Similarly to the original model, these three types can be compared regarding long-term social-ecological performance. In addition, a global strategy switching option (corresponding to double-loop learning) allows users to study how behavioural strategies diffuse in a heterogeneous population of learning and non-learning agents.

An important modification of the original model is that extension agents are heterogeneous in how they deal with uncertainty. This is represented by an agent property, called the r-parameter (household-risk-att in the code). The r-parameter is catch-all for various factors that form an agent’s disposition to act in a certain way, such as: uncertainty in the sensing (partial observability of the resource system), noise in the information received, or an inherent characteristic of the agent, for instance, their risk attitude.