Non-traditional tools and mediums can provide unique methodological and interpretive opportunities for archaeologists. In this case, the Unreal Engine (UE), which is typically used for games and media, has provided a powerful tool for non-programmers to engage with 3D visualization and programming as never before. UE has a low cost of entry for researchers as it is free to download and has user-friendly “blueprint” tools that are visual and easily extendable. Traditional maritime mobility in the Salish Sea is examined using an agent-based model developed in blueprints. Focusing on the sea canoe travel of the Straits Salish northwestern Washington State and southwest British Columbia. This simulation integrates GIS data to assess travel time between Coast Salish archaeological village locations and archaeologically represented resource gathering areas. Transportation speeds informed by ethnographic data were used to examine travel times for short forays and longer inter-village journeys. The results found that short forays tended to half day to full day trips when accounting for resource gathering activities. Similarly, many locations in the Salish Sea were accessible in long journeys within two to three days, assuming fair travel conditions. While overall transportation costs to reach sites may be low, models such as these highlight the variability in transport risk and cost. The integration of these types of tools, traditionally used for entertainment, can increase the accessibility of modeling approaches to researchers, be expanded to digital storytelling, including aiding in the teaching of traditional ecological knowledge and placenames, and can have wide applications beyond maritime archaeology.

This is v0.01 of a UE5.2.1 agent based model.

This software simulates cars and bicycles as traffic participants while crossing different crossroad designs such as roundabouts, protected crossroads and standard crossroads. It is written in Netlogo 6.2 and aims to identify safety characteristics of these layouts using agent-based modeling. Participants track the line of sight to each other and print them as an output alongside with the adjacent destination, used layout, count of collisions/cars/bicycles and time.

Detailed information can be found within the info tab of the program itself.

This model examines the potential impact of market collapse on the economy and demography of fishing households in the Logone Floodplain, Cameroon.

This Agent-Based Model is designed to simulate how similarity-based partner selection (homophily) shapes the formation of co-offending networks and the diffusion of skills within those networks. Its purpose is to isolate and test the effects of offenders’ preference for similar partners on network structure and information flow, under controlled conditions.

In the model, offenders are represented as agents with an individual attribute and a set of skills. At each time step, agents attempt to select partners based on similarity preference. When two agents mutually select each other, they commit a co-offense, forming a tie and exchanging a skill. The model tracks the evolution of network properties (e.g., density, clustering, and tie strength) as well as the spread of skills over time.

This simple and theoretical model does not aim to produce precise empirical predictions but rather to generate insights and test hypotheses about the trade-off between partnership stability and information diffusion. It provides a flexible framework for exploring how changes in partner selection preferences may lead to differences in criminal network dynamics. Although the model was developed to simulate offenders’ interactions, in principle, it could be applied to other social processes involving social learning and skills exchange.

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This paper develops a spatial agent-based model to examine how fertility regime shifts reshape population concentration and wealth distribution in an abstract urban system. Migration decisions combine population preference, cultural homophily, expected net income, and resource endowment through a standardised softmax utility. The design is deliberately stylised: it is not calibrated to a particular country or city system, but is intended to isolate the feedbacks linking migration, fertility, urban scaling, and accumulated wealth.
The simulations reveal robust directional asymmetry. When fertility shifts from low to high, population concentration responds rapidly; when fertility shifts from high to low, concentration declines only after a detectable delay and may temporarily continue in the previous direction. Wealth adds a second layer of hysteresis: cell total-wealth concentration follows population concentration with delay, cell mean-wealth inequality and system-level wealth indicators are slower still, and phase-space trajectories form loops rather than collapsing onto a single population–wealth curve. Robustness experiments indicate that longer fertility cycles, wider mobility neighbourhoods, and smoother resource landscapes change the magnitude of delay and overshoot, but do not remove the qualitative asymmetry. The paper argues that demographic decline should be understood not as the mirror image of demographic expansion, but as a path-dependent transition mediated by fast migration-income feedbacks and slower fertility, cohort, culture, and wealth mechanisms.

The purpose of the simulation was to explore and better understand the process of bridging between an analysis of qualitative data and the specification of a simulation. This may be developed for more serious processes later but at the moment it is merely an illustration.
This exercise was done by Stephanie Dornschneider (School of Politics and International Relations, University College Dublin) and Bruce Edmonds to inform the discussion at the Lorentz workshop on “Integrating Qualitative and Quantitative Data using Social Simulation” at Leiden in April 2019. The qualitative data was collected and analysed by SD. The model specification was developed as the result of discussion by BE & SD. The model was programmed by BE. This is described in a paper submitted to Social Simulation 2019 and (to some extent) in the slides presented at the workshop.

Its a multi agent simulation environment, provided using JADE/Java. It gets the number of agents and tasks, then divides the physical environment to some segments, and then runs a greedy capability-based coalition formation and task allocation algorithm to assign tasks to groups of agents and complete the tasks.

The agent based model matches origins and destinations using employment search methods at the individual level.

B3GET simulates populations of virtual organisms evolving over generations, whose evolutionary outcomes reflect the selection pressures of their environment. The model simulates several factors considered important in biology, including life history trade-offs, investment in fighting ability and aggression, sperm competition, infanticide, and competition over access to food and mates. Downloaded materials include starting genotype and population files. Edit the these files and see what changes occur in the behavior of virtual populations!

View the B3GET user manual here.

This is a Netlogo model which simulates car and bus/tram traffic in Augsburg, specifically between the districts Stadtbergen, Göggingen and the Königsplatz. People either use their cars or public transport to travel to one of their random destinations (Stadtbergen or Göggingen), performing some activity and then returning to their home. Attributes such as travel and waiting time as well as their happiness upon arriving are stored and have an impact on individuals on whether they would consider changing their mode of transport or not.