HOSS(-5) is an exploratory agent-based prototype of a small artificial society, written in Python. Each agent is a “ph” (punto humano, “human point”) that is born, is educated, works, consumes and dies. Its yearly decisions depend on a psychological profile (archetype × character), its economic status and its intentionality. Agents interact indirectly through families, firms in 10 sectors, a labour market with about 200 job categories, and a State that provides public services, employment and a Universal Basic Income, adjusting taxes and UBI each year to seek fiscal balance.

The model simulates a few thousand agents over decades of virtual time and records yearly aggregates (population, GDP, income, Gini index, HDI). A separate analysis script explores the results and fits Gamma distributions to individual incomes, comparing the shape parameter with Gini and HDI.

HOSS does not aim to predict real societies or claim empirical validation. It is a sandbox for comparing scenarios and thinking about the organizational design of society from an engineering perspective. Known limitations: no direct social relations between agents, prices and production follow simple rules rather than supply-demand dynamics, and organizations are black boxes.

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This model couples a two-asset heterogeneous-agent New Keynesian macroeconomy with a household layer that can be driven in two ways. One is a conventional calibration. The other is a set of large-language-model persona agents built from real survey households in Korea (NaSTaB), the United States (PSID) and Italy (SHIW). Each persona is given its balance sheet, income and prices, and reports consumption, saving and labour supply. A feasibility layer enforces the budget constraint. A conditional-mean surrogate then turns these decisions into a propensity surface. That surface reaches the macroeconomic block through a single channel, the intertemporal marginal propensity to consume matrix. Because of this, a calibrated “twin” can be aligned with the generative layer exactly, and the two can be compared policy by policy.

Both layers are then scored against the held-out consumption of the same households. The model’s role is to serve as a benchmark: it tests whether generative agents add information beyond the balance sheet, and whether that information matches real behaviour.

The deposit contains:

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Viking Sailing is a spatially explicit agent-based simulation for conditional exploration of how rasterized coastlines, wind, and
surface-current conditions interact with simplified ship movement, sailing, rowing, navigation, and land-avoidance rules. The
model compares simulated route trajectories and terminal outcomes for hypothetical Viking-era small-ship voyages. It includes an example dataset representing sea surface winds and currents from June 1000 CE. Additional data can are downloaded from Zenodo: https://zenodo.org/records/23213147.

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. 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.