An agent-based model to explore human–wild boar interactions in urban and suburban landscapes at the interface with agricultural land and natural habitats, by simulating wild boar population growth, mortality, and movements across the landscape

Diversity has become a defining feature of most Western societies, where boundaries of membership extend beyond ethnicity to encompass multiple intersecting categories, increasing the complexity of social integration. The aim of this model is to adapt the classical Schelling model of spatial segregation to this context and to identify mechanisms that may generate the hybrid segregation patterns where individuals are segregated from out-groups along one dimension while remaining integrated along others.

We extend the Schelling model using a discrete choice framework that incorporates dominant and secondary preferences among agents from two ethnic groups. Liberal agents prioritize cross-ethnic shared values over ethnicity, whereas conservative agents prioritize ethnic membership over shared values, although both retain some preference for the secondary dimension. The model is designed to explore how heterogeneous preferences and intersecting group memberships interact under different group compositions and initial distributions.

An agent-based model built in NetLogo that simulates a closed-loop recirculating aquaponics and integrated multitrophic aquaculture (IMTA) ecosystem, modeling the complex dynamics between aquatic species, vermaculture, and hydroponic plant beds. This model captures the operational mechanics of a complex, sustainable production network. It tracks nutrient flows, water recirculation pathways, biological growth stages, and environmental feedback penalties (such as nutrient toxicity or starvation) across multiple specialized tanks and grow beds. Model Architecture & HabitatsAquatic Subsystem: Duckweed Tank, Breeding Tank, Fry Tank, Fingerling Tank, Adult Tank, and a Holding Tank for harvested fish. Benthic & Waste Management: Crawdad Tank 1 & 2 for bottom-feeding and waste processing, alongside a Vermaculture node populated by composting worms. Hydroponic Subsystem: Five sequential Grow Beds and a recirculating loop feeding back into the system. Key Agent DynamicsWater Flow Visualization: Active links between tanks spawn animated droplet agents that visually simulate recirculating water dynamics across the network. Biological Growth & Aging: Fish and plants progress through age-based development thresholds governed by species-specific parameters. Feedback Loops:Toxicity Penalty: Excess nutrients (nutrient-per-day > 80) slow fish growth rates by 50%. Starvation Penalty: Insufficient nutrient levels (nutrient-per-day < 30) delay plant maturation by 50%. Interface Controlsfish-type Chooser: Selects the primary aquatic species (Tilapia, Channel Catfish, Trout, Goldfish), altering base growth thresholds. plant-type Chooser: Selects the cultivated crop (Lettuce, Basil, Tomatoes, Mint). nutrient-per-day Slider: Manages daily nutrient input to balance system load and avoid toxicity or starvation thresholds. Dashboard Plot: Real-time tracking of total-fish-harvested and total-plants-harvested.

An agent-based model of one colony of the Florida harvester ant, Pogonomyrmex badius, from the day a mated queen lands to the death of the colony. It runs in a web browser and, unchanged, headless in Node for replicate studies.

What the ants do: a claustral queen digs her own shaft and raises the first workers on her reserves; workers dig the nest under local rules, with no ant holding a plan; a one-way, age-based division of labour set by the season of birth, which no shortage reverses; trunk-trail foraging with site fidelity and recruitment; seed storage, opening and germination in the chambers, which is what feeds the larvae; daily weather from the climate normals of the study site, with drought years; nuptial flights after heavy rain; corpse removal; alarm at a disturbance on the foraging ground; and annual nest relocation, in which the store and then the brood are carried along the trail to a new nest the colony digs.

Every value in the model is tagged as measured in this species, borrowed from another ant, or invented, with its source, in a single parameter file. The model is deterministic: one seeded stream per system and a fixed one-minute timestep, so a seed reproduces a run bit for bit in the browser and in Node, which a test pins. A study writes a methods report listing the invented values the results rest on and the acceptance tests the model fails.

…

An Agent Based Model that explores the deployment of hydrogen among a regional industrial cluster in the Netherlands, consisting of 15 companies. The companies seek to decarbonize by replacing their natural gas by hydrogen.
The model integrates technical characteristics as well as company motivations to transition to hydrogen. The baseline model only considers individual investments where company can locally produce hydrogen. If they reach the backbone threshold, companies can also consider buying hydrogen through a connection to the national hydrogen network. The second scenario considers that companies can participate in a joint investment to get an electrolyzer to locally produce the hydrogen.
Two experiments look at the impact of the sectoral configuration and at the impact of subsidy conditions on the region’s hydrogen transition

Agent-based models of organizational search have long investigated how exploitative and exploratory behaviors shape and affect performance on complex landscapes. To explore this further, we build a series of models where agents have different levels of expertise and cognitive capabilities, so they must rely on each other’s knowledge to navigate the landscape. Model A investigates performance results for efficient and inefficient networks. Building on Model B, it adds individual-level cognitive diversity and interaction based on knowledge similarity. Model C then explores the performance implications of coordination spaces. Results show that totally connected networks outperform both hierarchical and clustered network structures when there are clear signals to detect neighbor performance. However, this pattern is reversed when agents must rely on experiential search and follow a path-dependent exploration pattern.

An agent-based model of urban travel behaviour in Dublin, Ireland, built in NetLogo and empirically grounded in 2016 travel survey data. Each agent represents a Dublin resident initialised with real socio-demographic attributes — including age, gender, household size and car ownership, income, driving licence status, and access to local amenities — alongside observed trip characteristics such as distance, travel time, and trip type (work, shopping, leisure).
At each time step, agents choose between four transport modes (car, public transport, cycling, and walking) across short, medium, and long trips. Mode choice is governed by a preference vector that weighs personal need satisfaction against social influence from neighbouring agents reflecting consumat framework. Satisfaction evolves dynamically based on cost (incorporating Irish motor tax bands and per-km operating rates), travel time, and trip-type suitability, with an uncertainty parameter capturing variability in perceived utility over time.
The model tracks aggregate modal shares and total CO2 emission at each tick, enabling exploration of how policy interventions — such as fuel taxation, public transport pricing, or active travel incentives — might shift the city’s travel demand profile over 100 simulated days.

patch_choice_enhanced.nlogox is an agent-based model (ABM) implementing the patch choice model from optimal foraging theory (OFT) in a multi-agent simulation environment. It extends the classical single-forager, equation-based Marginal Value Theorem (MVT) formulation to support multiple competing foragers, demographic processes, bounded memory, and stochastic resource dynamics on a spatially heterogeneous landscape.

Norms@Risk ABM aim is to develop an explanation for the conditions under which people jointly take action to prevent disasters from happening, e.g. impacts from climate crisis, pandemic. The aim of the Norms@Risk work is to explore understudied dynamics between risk, social norms and cooperative behaviour and their role in overcoming collective threats. More specifically, Norms@Risk seeks to refine existing theory on cooperation by unpacking the role of collective risk interacting with social norms in ‘Collective Risk Social Dilemmas’*.

This simulation study takes on the next scientific iteration after a series of behavioural experiments. With the model we target to refine existing theory by capturing the heterogeneity in contributions that existing theories cannot fully explain as the behavioural responses are assumed to be more homogenous, particularly diversity regarding sensitivity to risk and norms.

*Collective Risk Social Dilemmas reflect a special type of social dilemma, unlike typical public goods, the collective risk dilemma involves individual contribution not to realise a gain, but to avoid a collective loss.

…

We built a model of knowledges diffusion in networks. The particularity is that knowledges to be passed on are cumulative : rely on previous acquisition of specific knowledges of a lower level to be passed from an agent to another. We test for different types of networks, different selection rules for the knowledge to be passed and the possibility to introduce a turnover among agents.
We concentrate on the learning speed and the convergence level of the learning process.