Development of a Multiagent System for the Analysis of Gentrification in Latin America, an Agent-Based Model

El modelo a continuación, fue desarrollado para el DATA CHALLENGE 2022. Es un análisis de la información descargada del Portal de datos abiertos de Ecuador. Dentro del modelo podemos realizar una breve exploración de la información así como una simulación respecto al proceso de vacunación en Ecuador.

This model, realized on the NetLogo platform, compares utility levels at home and abroad to simulate agents’ migration and their eventual return. Our model is based on two fundamental individual features, i.e. risk aversion and initial expectation, which characterize the dynamics of different agents according to the evolution of their social contacts.

This model simulates economic and epidemiological interaction between citrus production and the disease Huanglongbing (HLB), which is vectored by the Asian citrus psyllid. The model is used to evaluate area-wide coordinated spraying when free-riding is possible given individuals’ beliefs in other grower participation in area-wide spraying and in the information provided by extension on the threat as HLB spread.

The WaterScape is an agent-based model of the South African water sector. This version of the model focuses on potential barriers to learning in water management that arise from interactions between human perceptions and social-ecological system conditions.

The Labour Markets and Ethnic Segmentation (LaMESt) Model is a model of a simplified labour market, where only jobs of the lowest skill level are considered. Immigrants of two different ethnicities (“Latino”, “Asian”) compete with a majority (“White”) and minority (“Black”) native population for these jobs. The model’s purpose is to investigate the effect of ethnically homogeneous social networks on the emergence of ethnic segmentation in such a labour market. It is inspired by Waldinger & Lichter’s study of immigration and the social organisation of labour in 1990’s Los Angeles.

This model simulates a foraging system based on Middle Stone Age plant and shellfish foraging in South Africa.

This Agent-Based model intends to explore the conditions for the emergence and change of land use patterns in Central Asian oases and similar contexts.

This model (CharRec) creates simulated charcoal records, based on differing natural and anthropogenic patterns of ignitions, charcoal dispersion, and deposition.

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.