This is a stylised agent-based model designed to explore the conditions that lead to lock-ins and transitions in agri-food systems.

The model represents interactions between four different types of agents: farmers, consumers, markets, and the state. Farmers and consumers are heterogeneous, and at each time step decide whether to trade with one of two market agents: the conventional or alternative. The state agent provides subsidies to the farmers at each time step.

The key emergent outcome is the fraction of trade in each time step that flows through the alternative market agent. This arises from the distributed decisions of farmer and consumer agents. A “sustainability transition” is defined as a shift in the dominant practices (and associated balance of power) towards the alternative paradigm.

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This ABM aims to introduce a new individual decision-making model, BNE into the ABM of pedestrian evacuation to properly model individual behaviours and motions in emergency situations. Three types of behavioural models has been developed, which are Shortest Route (SR) model, Random Follow (RF) model, and BNE model, to better reproduce evacuation dynamics in a tunnel space. A series of simulation experiments were conducted to evaluate the simulating performance of the proposed ABM.

MELBIS-V1 is a spatially explicit agent-based model that allows the geospatial simulation of the decision-making process of newcomers arriving in the bilingual cities and boroughs of the island of Montreal, Quebec in CANADA, and the resulting urban segregation spatial patterns. The model was implemented in NetLogo, using geospatial raster datasets of 120m spatial resolution.

MELBIS-V2 enhances MELBIS-V1 to implement and simulate the decision-making processes of incoming immigrants, and to analyze the resulting spatial patterns of segregation as immigrants arrive and settle in various cities in Canada. The arrival and segregation of immigrants is modeled with MELBIS-V2 and compared for three major Canadian immigration gateways, including the City of Toronto, Metro Vancouver, and the City of Calgary.

The model is intended to simulate visitor spatial and temporal dynamics, encompassing their numbers, activities, and distribution along a coastline influenced by beach landscape design. Our primary focus is understanding how the spatial distribution of services and recreational facilities (e.g., beach width, entrance location, recreational facilities, parking availability) impacts visitation density. Our focus is not on tracking the precise visitation density but rather on estimating the areas most affected by visitor activity. This comprehension allows for assessing the diverse influences of beach layouts on spatial visitor density and, consequently, on the landscape’s biophysical characteristics (e.g., vegetation, fauna, and sediment features).

Our aim is to demonstrate how conversational AI systems, exemplified by ChatGPT, can support the conceptualisation of Agent-Based Social Simulation (ABSS) models, leading to a full ABSS model design document. Through advanced prompt engineering and adherence to the Engineering ABSS framework (Siebers and Klügl 2017), we have constructed a comprehensive script that is easy to use and that supports the design of ABSS models with or even by AI. The performance of the script is demonstrated through an illustrative case study related to the use of adaptive architecture in museums. The repository contains (1) the comprehensive script in a format that allows copying and pasting prompts for use with ChatGPT, (2) the results of the illustrative case study in the form of two conceptual ABSS models, the ground truth and the autogenerated version.

Replication of the well known Artificial Anasazi model that simulates the population dynamics between 800 and 1350 in the Long House Valley in Arizona.

This is the code for the model described in an article in the International Journal of Microsimulation. Lawson (2013) ‘Modelling Household Spending Using a Random Assignment Scheme’, International Journal of Microsimulation, 6(2) Autumn 2013, 56-75.

The purpose of the model is to explore how the unique socioeconomic variables underlying Kibera, local interactions, and the spread of a rumor, may trigger a riot.

Logônia is a NetLogo model that simulates the growth response of a fictional plant, logônia, under different climatic conditions. The model uses climate data from WorldClim 2.1 and demonstrates how to integrate the LogoClim model through the LevelSpace extension.

Logônia follows the FAIR Principles for Research Software (Barker et al., 2022) and is openly available on the CoMSES Network and GitHub.

a computer-based role-playing game simulating the interactions between farming activities, livestock herding and wildlife in a virtual landscape reproducing local socioecological dynamics at the periphery of Hwange National Park (Zimbabwe).