Both models simulate n-person prisoner dilemma in groups (left figure) where agents decide to C/D – using a stochastic threshold algorithm with reinforcement learning components. We model fixed (single group ABM) and dynamic groups (bad-barrels ABM). The purpose of the bad-barrels model is to assess the impact of information during meritocratic matching. In the bad-barrels model, we incorporated a multidimensional structure in which agents are also embedded in a social network (2-person PD). We modeled a random and homophilous network via a random spatial graph algorithm (right figure).

A dynmaic microsimulation model to project the UK population over time

AgentEx aims to advance understanding of group processes for sustainable management of a common pool resource (CPR). By supporting the development and test explanations of cooperation and sustainable exploitation.

The model simulates the spatial patterns of secondary forest succession above the current alpine tree line in the context of land use and climate change. Three scenarios are offered: (1) climate change, (2) land use change, (3) species composition.

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/

Machine learning technologies have changed the paradigm of knowledge discovery in organizations and transformed traditional organizational learning to human-machine hybrid intelligent organizational learning. However, it remains unclear how human-machine trust, which is an important factor that influences human-machine knowledge exchange, affects the effectiveness of human-machine hybrid intelligent organizational learning. To explore this issue, we used multi-agent simulation to construct a knowledge learning model of a human-machine hybrid intelligent organization with human-machine trust.

We developed an agent-based model to explore underlying mechanisms of behavioral clustering that we observed in human online shopping experiments.

Studies on word-of-mouth identify two behaviors leading to transmission of information between individuals: proactive transmission of information, and information seeking. Individuals who are aware might be curious of it and start seeking for information; they might find around them the expertise held by another individual. Field studies indicate individuals do not adopt an innovation if they don’t hold the corresponding expertise. This model describes this information seeking behavior, and enables the exploration of the dynamics which emerges out of it.

The study goes back to a model created in the 1990s which successfully tried to replicate the changes of the percentages of female teachers among the teaching staff in high schools (“Gymnasien”) in the German federal state of Rheinland-Pfalz. The current version allows for additional validation and calibration of the model and is accompanied with the empirical data against which the model is tested and with an analysis program especially designed to perform the analyses in the most recent journal article.

PowerGen-ABM is an optimisation model for power plant expansions from 2010 to 2025 with Indonesian electricity systems as the case study. PowerGen-ABM integrates three approaches: techno-economic analysis (TEA), linear programming (LP), and input-output analysis (IOA) and environmental analysis. TEA is based on the revenue requirement (RR) formula by UCDavis (2016), and the environmental analysis accounts for resource consumption (i.e., steel, concrete, aluminium, and energy) and carbon dioxide equivalent (CO2e) emissions during the construction and operational stages of power plants.