This NetLogo model simulates the movement and foraging behavior of 20 bird species (classified as urban avoiders or utilizers) across a 20×20m grid landscape representing southern Bogotá, Colombia. Agents follow an optimal foraging strategy (90% of the time) or move randomly (10%), gaining energy from land-cover-dependent food sources and dying if energy falls below their basal metabolic rate. The model compares two landscape scenarios — current land cover (2016) and a proposed scenario incorporating the ‘Media Luna del Sur’ ecological corridor from Bogotá’s 2022–2035 Land Use Plan (POT) — to evaluate whether the proposed green infrastructure improves functional connectivity for avian biodiversity across the urban matrix. Outputs include cumulative patch visitation counts, exported as raster files, used to generate connectivity heatmaps.

Party Competition with Costly Voting builds upon the baseline model in Chapter 5 from Laver and Sergenti’s Party Competition (2011); it adds voting costs to generate variable turnout.

MASTOC-LLM extends the classic Multi-Agent System Tragedy of the Commons (MASTOC) model by replacing hard-coded behavioral rules with autonomous decision-making powered by large language models (LLMs). Three heterogeneous agents manage herds of cows on a shared grassland commons. Each tick, an agent receives a structured prompt describing current resource levels, its own herd size, peer behavior, and — optionally — a rolling memory of recent rounds and messages from neighboring agents. The LLM returns a stocking decision (add, remove, or hold cows) together with a natural-language rationale and, when communication is enabled, a short message to broadcast to peers.

The model is designed to test whether LLM agents spontaneously develop Ostrom-style common-pool resource governance (mutual monitoring, graduated sanctions, graduated rule revision) or instead fall into identifiable failure modes. Preliminary experiments with Claude Haiku 4.5, GPT-5.4-mini, and DeepSeek R1:32b have revealed four recurring collapse patterns — Cooperative Paralysis, Defection Cascade, Overshoot-Panic, and Hybrid Architecture Failure — whose onset timing is sensitive to memory length, inter-agent communication, and the post-training alignment approach of the underlying model.

MASTOC-LLM is intended as a laboratory for generative agent-based modelling (GABM) methodology: it provides a clean, well-understood commons baseline against which LLM behavioral hypotheses can be systematically tested and compared across models, parameter sweeps, and alignment regimes.

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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The model generates disaggregated traffic flows of pedestrians, simulating their daily mobility behaviour represented as probabilistic rules. Various parameters of physical infrastructure and travel behaviour can be altered and tested. This allows predicting potential shifts in traffic dynamics in a simulated setting. Moreover, assumptions in decision-making processes are general for mid-sized cities and can be applied to similar areas.

Together with the model files, there is the ODD protocol with the detailed description of model’s structure. Check the associated publication for results and evaluation of the model.

Installation
Download GAMA-platform (GAMA1.8.2 with JDK version) from https://gama-platform.github.io/. The platform requires a minimum of 4 GB of RAM.
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The purpose of this model is to explain the post-disaster recovery of households residing in their own single-family homes and to predict households’ recovery decisions from drivers of recovery. Herein, a household’s recovery decision is repair/reconstruction of its damaged house to the pre-disaster condition, waiting without repair/reconstruction, or selling the house (and relocating). Recovery drivers include financial conditions and functionality of the community that is most important to a household. Financial conditions are evaluated by two categories of variables: costs and resources. Costs include repair/reconstruction costs and rent of another property when the primary house is uninhabitable. Resources comprise the money required to cover the costs of repair/reconstruction and to pay the rent (if required). The repair/reconstruction resources include settlement from the National Flood Insurance (NFI), Housing Assistance provided by the Federal Emergency Management Agency (FEMA-HA), disaster loan offered by the Small Business Administration (SBA loan), a share of household liquid assets, and Community Development Block Grant Disaster Recovery (CDBG-DR) fund provided by the Department of Housing and Urban Development (HUD). Further, household income determines the amount of rent that it can afford. Community conditions are assessed for each household based on the restoration of specific anchors. ASNA indexes (Nejat, Moradi, & Ghosh 2019) are used to identify the category of community anchors that is important to a recovery decision of each household. Accordingly, households are indexed into three classes for each of which recovery of infrastructure, neighbors, or community assets matters most. Further, among similar anchors, those anchors are important to a household that are located in its perceived neighborhood area (Moradi, Nejat, Hu, & Ghosh 2020).

This model represents informal information transmission networks among medieval Genoese investors used to inform each other about cheating merchants they employed as part of long-distance trade operations.

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 documentation provides an overview and explanation of the NetLogo simulation code for modeling skilled workers’ migration in Iran. The simulation aims to explore the dynamics of skilled workers’ migration and their transition through various states, including training, employment, and immigration.

The flow of elite and talent migration, or “brain drain,” is a complex issue with far-reaching implications for developing countries. The decision to migrate is made due to various factors including economic opportunities, political stability, social factors and personal circumstances.
Measuring individual interests in the field of immigration is a complex task that requires careful consideration of various factors. The agent-based model is a useful tool for understanding the complex factors that are involved in talent migration. By considering the various social, economic, and personal factors that influence migration decisions, policymakers can provide more effective strategies to retain skilled and talented labor and promote sustainable growth in developing countries. One of the main challenges in studying the flow of elite migration is the complexity of the decision-making process and a set of factors that lead to migration decisions. Agent-based modeling is a useful tool for understanding how individual decisions can lead to large-scale migration patterns.

This is a tool to explore the effects of groups´ spatial segregation on the emergence of opinion polarization. It embeds two opinion formation models: a model of negative (and positive) social influence and a model of persuasive argument exchange.