This agent-based aquaponics simulation models the biological resilience and complex dynamics of a closed-loop agricultural system. Designed for educational use and agricultural research, the model helps aquaponic designers, farmers, and hobbyists understand systemic dynamics and stress-test species configurations prior to a physical build. The simulation dynamically models the nitrogen cycle by tracking aquatic bio-loads, bacterial filtration, and plant nutrient uptake. Utilizing real-world biological metrics from the Loyola Global Aquatic Excrement Dataset, the model calculates species-specific ammonia excretion rates (toxicity) for various aquatic life, including Trout, Tilapia, and Goldfish. Nitrifying bacteria agents convert this toxicity into usable nitrates, which are then absorbed by specific crop agents—such as Tomatoes, Swiss Chard, or Lettuce—at variable rates. Key features include dynamic stochastic harvesting, measurable agent health, vermaculture and crawdad integration, and reactive biological mitigators like emergency duckweed buffering[cite: 1, 3]. The system also introduces chaotic environmental variables, allowing users to test the agricultural setup’s resilience against random shocks like bio-filter pump failures and temperature crashes. This model is released under the GNU General Public License v3.0 (GPL-3.0) to encourage collaborative improvement within the academic and farming communities.

Replication package for a network diffusion model of how Protestant belief spreads through a
signed social network, used to compare three versions of balance theory.

Artificial Anasazi is an agent-based model of farming households in the Long House Valley, Arizona, from 800 to 1350 CE. Each household chooses where to farm and settle based on expected harvest, water sources and stored maize, and simulated household counts are compared with the archaeological record.

This release adapts the NetLogo Models Library version (Stonedahl & Wilensky 2010) of Janssen’s (2009) NetLogo replication of the original Ascape model (Dean et al. 2000; Axtell et al. 2002). It contains four configurations in one NetLogo 7.0.4 file, selected with two switches:

• Both switches off: the baseline, which is the Models Library model with small fixes that make it work as intended (the arable-uplands zone name, the setup base yield, the initial household count, one-tick-lag capacity reporting, and seed control with observation-only instruments).
• soil-on?: dynamic soil quality that depletes under cultivation and regrows, following Janssen (2010, eq. 4).
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An agent-based model in which fourteen European Union member states each carry a cooperation
propensity in [0,1], updated by a logistic link applied to a latent index that combines the unit’s
own previous state, a weighted average of the other units’ states, and four min-max normalised
exogenous indicators (cooperative benefit, opportunistic temptation, systemic exit cost,
intertemporal confidence). The logistic centres are anchored so that the observed 2024
configuration is an exact fixed point of the baseline map, the device that makes such models
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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

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.

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.

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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.

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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.