The Archaeological Sampling Experimental Laboratory (tASEL) is an interactive tool for setting up and conducting experiments about sampling strategies for archaeological excavation, survey, and prospection.

This is extended version of the MERCRUY model (Brughmans 2015) incorporates a ‘transport-cost’ variable, and is otherwise unchanged. This extended model is described in this publication: Brughmans, T., 2019. Evaluating the potential of computational modelling for informing debates on Roman economic integration, in: Verboven, K., Poblome, J. (Eds.), Structural Determinants in the Roman World.

Brughmans, T., 2015. MERCURY: an ABM of tableware trade in the Roman East. CoMSES Comput. Model Libr. URL https://www.comses.net/codebases/4347/releases/1.1.0/

This model demonstrates how to illustrate a cluster pattern by counting turtles within i moving circle with a specified radius. The procedure is common in archaeological spatial analysis.

A simple model of random encounters of materials that produces distributions as found in the archaeological record.

A draft model teaching how a Roman transport model can be imported into Netlogo, and the issues confronted when importing and reusing open access Roman datasets. This model is used for the tutorial:
Brughmans, T. (2018). Importing a Roman Transport network with Netlogo, Tutorial, https://archaeologicalnetworks.wordpress.com/resources/#transport .

This model aims to explore how gambling-like behavior can emerge in loot box spending within gaming communities. A loot box is a purchasable mystery box that randomly awards the player a series of in-game items. Since the contents of the box are largely up to chance, many players can fall into a compulsion loop of purchasing, as the fear of missing out and belief in the gambler’s fallacy allow one to rationalize repeated purchases, especially when one compares their own luck to others. To simulate this behavior, this model generates players in different network structures to observe how factors such as network connectivity, a player’s internal decision making strategy, or even common manipulations games use these days may influence a player’s transactions.

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

NeoCOOP is an iteration-based ABM that uses Reinforcement Learning and Artificial Evolution as adaptive-mechanisms to simulate the emergence of resource trading beliefs among Neolithic-inspired households.

The purpose of this model is to help understand how prehistoric societies adapted to the prehistoric American southwest landscape. In the American southwest there is a high degree of environmental var

Simulation of irrigation system management using archaeological data from southern Arizona