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.

This model is designed to address the following research question: How does the amount and topology of intergroup cultural transmission modulate the effect of local group extinction on selectively neutral cultural diversity in a geographically structured population? The experimental design varies group extinction rate, the amount of intergroup cultural transmission, and the topology of intergroup cultural transmission while measuring the effects of local group extinction on long-term cultural change and regional cultural differentiation in a constant-size, spatially structured population. The results show that for most of the intergroup social network topologies tested here, increasing the amount of intergroup cultural transmission (similar to increasing gene flow in a genetic model) erases the negative effect of local group extinction on selectively neutral cultural diversity. The stochastic (i.e., preference attachment) network seems to stand out as an exception.

This agent-based model is supporting a simulation that has been used in participatory workshop in Vietnam. It was designed to foster discussion about the joint reading of territorial evolutions and the decrease of investment in the maintenance of hydraulic works, especially irrigation pumping station in a case study territory in the Red River Delta.
The code comprises a number of different experiment to facilitate discussion on specific variables during workshops. One of the experiment has been developped to allow users to interact with hydraulic works and decide to prioritise or de-prioritise their maintenance.
This model has been implemented using the GAMA platform, relying on the gaml language.

An agent-based model of saving and dissaving behaviour under quasi-hyperbolic (β–δ) discounting. Building on the individual decision problem of Cao and Werning (2018), the model embeds present-biased agents in a Watts–Strogatz small-world network and adds three configurable mechanisms of social influence — information diffusion, peer comparison, and social-norm conformity — across five heterogeneous behavioural profiles (Planners, Moderates, Procrastinators, Inverse Procrastinators, and Impulsive agents).
Each profile’s saving policy is approximated by value-function iteration over a discretised wealth grid; the solved policies are cached and applied as agents interact over their network neighbourhoods. The model tests whether each social mechanism can alter the saving and wealth trajectories that present-biased agents would otherwise follow in isolation, and characterises the direction and size of each effect on median wealth, wealth inequality (Gini), and the incidence of severely depleted agents.
The deposit includes the core model (Model.py), an analysis and visualisation pipeline (analyze_results.py), a standalone ODD description (ODD.md), and pinned dependencies.

This NetLogo model simulates the spread of climate change beliefs within a population of individuals. Each believer has an initial belief level, which changes over time due to interactions with other individuals and exposure to media. The aim of the model is to identify possible methods for reducing climate change denial.

Agent-based model simulating 60 undergraduate CS students over 8 semesters to investigate how differential AI tool access affects academic inequality, performance distributions, and dependency formation. Implements a three-phase AI dependency mechanism (effort reduction, metacognitive miscalibration, capability erosion) under five policy scenarios with a 4,050-run parameter sensitivity sweep.

This model compares rules for allocating a fixed budget of opportunity (places, training, funding) among people who differ in potential and will. Each agent’s effective potential, its denatotita, is potential multiplied by will. Realisation is denatotita multiplied by the opportunity received, and an agent succeeds when realisation reaches a threshold. Nine allocation regimes are compared: equal shares; shares proportional to denatotita; three threshold rules that reclaim and redistribute the allocations of those who would fail; an efficient benchmark that maximises successes; and three forward-looking rules (foresight, will-aware foresight and hindsight). A mixed regime blends targeting with equal shares. Over time, opportunity develops potential through a skill-formation technology, and will rises with success and falls when an agent is passed over. The model reproduces the article’s results: a closed-form crossover between equal and proportional allocation; a reversal in favour of broad allocation when development is productive; a hollowing-out of potential under success-maximising allocation; and the failure of mixtures of targeting and equal shares. Written in Python (NumPy, pandas, Matplotlib), with fixed random seeds so results are exactly reproducible.

This ABM simulates opinions on a topic (originally contested infrastructures) through the interactions between paired agents and based on the sociopsychological assumptions of social judgment theory (SJT; Sherif & Hovland, 1961).

This model is a spatial evolutionary game theory model in which animals with inherited red or blue phenotypes move, interact locally, gain or lose energy based on payoff structure, and reproduce across generations. Plants provide a stationary green component of the environment. The model can be used to examine how game payoffs, inheritance, and survival shape population and evolutionary dynamics over time.

A simple model to assess the effect of connectivity on interacting species (i.e. predator-prey type)