This model is pertinent to our JASSS publication “Raising the Spectrum of Polarization: Generating Issue Alignment with a Weighted Balance Opinion Dynamics Model”. It shows how, based on the mechanisms of our Weighted Balance Theory (a development of Fritz Heider’s Cognitive Balance Theory), agents can self-organize in a multi-dimensional opinion space and form an emergent ideological spectrum. The degree of issue alignment and polarization realized by the model depends mainly on the agent-specific ‘equanimity parameter’ epsilon.

This model takes concepts from a JASSS paper this is accepted for the October, 2023 edition and applies the concepts to empirical data from counties surrounding and including Cleveland Ohio. The agent-based model has a proportional number of agents in each of the counties to represent the correct proportions of adults in these counties. The adoption decision probability uses the equations from Bass (1969) as adapted by Rand & Rust (2011). It also includes the Outgroup aversion factor from Smaldino, who initially had used a different imitation model on line grid. This model uses preferential attachment network as a metaphor for social networks influencing adoption. The preferential network can be adjusted in the model to be created based on both nodes preferred due to higher rank as well as a mild preference for nodes of a like group.

We develop an agent-based model (U-TRANS) to simulate the transition of an abstract city under an industrial revolution. By coupling the labour and housing markets, we propose a holistic framework that incorporates the key interacting factors and micro processes during the transition. Using U-TRANS, we look at five urban transition scenarios: collapse, weak recovery, transition, enhanced training and global recruit, and find the model is able to generate patterns observed in the real world. For example, We find that poor neighbourhoods benefit the most from growth in the new industry, whereas the rich neighbourhoods do better than the rest when the growth is slow or the situation deteriorates. We also find a (subtle) trade-off between growth and equality. The strategy to recruit a large number of skilled workers globally will lead to higher growth in GDP, population and human capital, but it will also entail higher inequality and market volatility, and potentially create a divide between the local and international workers. The holistic framework developed in this paper will help us better understand urban transition and detect early signals in the process. It can also be used as a test-bed for policy and growth strategies to help a city during a major economic and technological revolution.

We introduce a model of prediction markets that uses opinion dynamics as its underlying mechanism for price formation. We base the opinion dynamics on the Deffuant model of bounded rationality. We have used this model to show that price formation in prediction markets can be robustly explained by opinion dynamics, and that the model can also explain phase transitions depending on just two parameters.

This model has been developed together with the publication ‘Modelling Value Change - An Exploratory Approach’

Value change and moral change have increasingly become topics of interest in the philosophical literature. Several theoretical accounts have been proposed. Such accounts are usually based on certain theoretical and conceptual assumptions and their strengths and weaknesses are often hard to determine and compare, also because they are based on limited empirical evidence.

We propose that a step forward can be made with the help of agent-based modelling (ABM). ABM can be used to investigate whether a simulation model based on a specific account of value change can reproduce relevant phenomena. To illustrate this approach, we built a model based on the pragmatist account of value change proposed in van de Poel and Kudina (2022). We show that this model can reproduce four relevant phenomena, namely 1) the inevitability and stability of values, 2) how different societies may react differently to external shocks, 3) moral revolutions, and 4) lock-in.

Viable North Sea (ViNoS) is an Agent-based Model of the German North Sea Small-scale Fisheries in a Social-Ecological Systems framework focussing on the adaptive behaviour of fishers facing regulatory, economic, and resource changes. Small-scale fisheries are an important part both of the cultural perception of the German North Sea coast and of its fishing industry. These fisheries are typically family-run operations that use smaller boats and traditional fishing methods to catch a variety of bottom-dwelling species, including plaice, sole, and brown shrimp. Fisheries in the North Sea face area competition with other uses of the sea – long practiced ones like shipping, gas exploration and sand extractions, and currently increasing ones like marine protection and offshore wind farming. German authorities have just released a new maritime spatial plan implementing the need for 30% of protection areas demanded by the United Nations High Seas Treaty and aiming at up to 70 GW of offshore wind power generation by 2045. Fisheries in the North Sea also have to adjust to the northward migration of their established resources following the climate heating of the water. And they have to re-evaluate their economic balance by figuring in the foreseeable rise in oil price and the need for re-investing into their aged fleet.

The model explores the informational causes of polarization and bi-polarization of opinions in groups. To this end it expands the model of the Argument Communication Theory of Bi-polarization. The latter is an argument-based multi-agent model of opinion dynamics inspired by Persuasive Argument Theory. The original model can account for polarization as an outcome of pure informational influence, and reproduces bi-polarization effects by postulating an additional mechanism of homophilous selection of communication partners. The expanded model adds two dimensions: argument strength and more sophisticated protocols of informational influence (argument communication and opinion update).

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.

Organizations are complex systems comprised of many dynamic and evolving interaction patterns among individuals and groups. Understanding these interactions and how patterns, such as informal structures and knowledge sharing behavior, emerge are crucial to creating effective and efficient organizations. To explore such organizational dynamics, the agent-based model integrates a cognitive model, dynamic social networks, and a physical environment.

The Mobility Transition Model (MoTMo) is a large scale agent-based model to simulate the private mobility demand in Germany until 2035. Here, we publish a very much reduced version of this model (R-MoTMo) which is designed to demonstrate the basic modelling ideas; the aim is by abstracting from the (empirical, technological, geographical, etc.) details to examine the feed-backs of individual decisions on the socio-technical system.