This repository contains an agent-based simulation model exploring how status hierarchies influence the emergence and sustainability of cooperation in task-oriented groups. The model builds on evolutionary game theory to examine the dynamics of cooperation under single-leader and multi-leader hierarchies, investigating factors such as group size, assortativity, and hierarchical clarity. Key findings highlight the trade-offs between different leadership structures in fostering group cooperation and reveal the conditions under which cooperation is most stable.

The repository includes code for simulations, numerical analysis scripts, and visualization tools to replicate the results presented in the manuscript titled “Status hierarchies and the emergence of cooperation in task groups.”

Feel free to explore, reproduce the findings, or adapt the model for further research!

This spatially explicit agent-based model addresses how effective foraging radius (r_e) affects the effective size–and thus the equilibrium cultural diversity–of a structured population composed of central-place foraging groups.

Digital-Twin model of Sejong City – Source model code & data

We only shared model codes, excluding private data and simulation engine codes.
The followings are brief reasons for the items we cannot share.

1. Residence address data
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This is a model that explores how a few fishermen sharing a common fishery learn their harvesting strategies under different incentive settings, and how individual greed, cooperation, and sustainability penalties shape resource depletion and the tragedy of the commons.

This program was developed to simulate monogamous reproduction in small populations (and the enforcement of the incest taboo).

Every tick is a year. Adults can look for a mate and enter a relationship. Adult females in a Relationship (under the age of 52) have a chance to become pregnant. Everyone becomes not alive at 77 (at which point people are instead displayed as flowers).

User can select a starting-population. The starting population will be adults between the ages of 18 and 42.

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This model is to explore the changes of paddy field landscape and household livelihood structure in the village under different policy scenarios, evaluate the eco-social effects of different policies, and provide decision support tools for proposing effective and feasible policies.

A spatio-temporal Agent Based Modeling (ABM) framework is developed to probabilistically predict farmers’ decisions in the context of climate-induced water scarcity under varying utility optimization functions. The proposed framework forecasts farmers’ behavior assuming varying utility functions. The framework allows decision makers to forecast the behavior of farmers through a user-friendly platform with clear output visualization. The functionality of the proposed ABM is illustrated in an agriculturally dominated plain along the Eastern Mediterranean coastline.

Study area GIS data available upon request to gxh00@mail.aub.edu

Protein 2.0 is a systems model of the Norwegian protein sector designed to explore the potential impacts of carbon taxation and the emergence of cultivated meat and dairy technologies. The model simulates production, pricing, and consumption dynamics across conventional and cultivated protein sources, accounting for emissions intensity, technological learning, economies of scale, and agent behaviour. It assesses how carbon pricing could alter the competitiveness of conventional beef, lamb, pork, chicken, milk, and egg production relative to emerging cultivated alternatives, and evaluates the implications for domestic production, emissions, and food system resilience. The model provides a flexible platform for exploring policy scenarios and transition pathways in protein supply. Further details can be found in the associated publication.

This model is designed to investigate the impact of alternative policy approaches and changing land tenure dynamics on farmer adoption of conservation practices intended to increase the water quality.

Routes & Rumours is an agent-based model of (forced) human migration. We model the formation of migration routes under the assumption that migrants have limited geographical knowledge concerning the transit area and rely to a large degree on information obtained from other migrants.