This model is designed to show the effects of personality types and student organizations have on ones chance to making friendships in a university setting. As known from psychology studies, those that are extroverted have an easier chance making friendships in comparison to those that are introverted.
Once every tick a pair of students (nodes) will be randomly selected they will then have the chance to either be come friends or not (create an edge or not) based on their personality type (you are able to change what the effect of each personality is) and whether or not they are in the same club (you can change this value) then the model triggers the next tick cycle to begin.

An integrated wildfire and urban growth model for informal settlements.

Fire-WCISG is developed to test the impact of different fire management strategies on the number and type of residents staying in an informal settlement, and the fire-dependent fynbos vegetation that it is next to.

The model uses a hybrid protection motivation theory, place attachment and Turner’s informal residents as described by Christ et al. (2023) (10.1016/j.habitatint.2023.102815), for the agents decision making. Wildland vegetation is based on published understanding of fynbos ecology.

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This models simulate how people choose their food buyings based on a limited budget and a food quality index. In order to make the choice the agents call a genetic algorithm who optimizes the buying. The agents are distributed randomly and there are 5 store options where agents can buy meat, fruits and vegetables, bread, ultraprocessed and medications. Agents need all of them, each of one has a different price and a different quality index. The genetic algorithm perform an optimization procedure in order to clasiffy what food set is the best for each agent. The agents are coloured based on their budget if the’ve got less than 100, then they are pink otherwise they are white.

The purpose of this model is to examine equity and efficiency in crop production across a system of irrigated farms, as a function of maintenance costs, assessed water fees, and the capacity of farmers to trade water rights among themselves.

The model explores the impact of journal metrics (e.g., the notorious impact factor) on the perception that academics have of an article’s scientific value.

This model is developed as a theoretical agent-based model to study the general phenomena of network-based targeting strategies on eco-innovation adoption and diffusion through inter-firm networks.

Comparing impact of alternative behavioral theories in a simple social-ecological system.

An agent-based model of urban travel behaviour in Dublin, Ireland, built in NetLogo and empirically grounded in 2016 travel survey data. Each agent represents a Dublin resident initialised with real socio-demographic attributes — including age, gender, household size and car ownership, income, driving licence status, and access to local amenities — alongside observed trip characteristics such as distance, travel time, and trip type (work, shopping, leisure).
At each time step, agents choose between four transport modes (car, public transport, cycling, and walking) across short, medium, and long trips. Mode choice is governed by a preference vector that weighs personal need satisfaction against social influence from neighbouring agents reflecting consumat framework. Satisfaction evolves dynamically based on cost (incorporating Irish motor tax bands and per-km operating rates), travel time, and trip-type suitability, with an uncertainty parameter capturing variability in perceived utility over time.
The model tracks aggregate modal shares and total CO2 emission at each tick, enabling exploration of how policy interventions — such as fuel taxation, public transport pricing, or active travel incentives — might shift the city’s travel demand profile over 100 simulated days.

Exploring how learning and social-ecological networks influence management choice set and their ability to increase the likelihood of species coexistence (i.e. biodiversity) on a fragmented landscape controlled by different managers.

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.