This model is an agent-based simulation that consists of agents who play the spatial prisioner’s dilemma game with coalition formation. The coalition dynamics are mainly influenced by how much the agents trust their leaders. The main objective is provide a simulation model to enable the analysis of the impacts that the use of trust may cause in coalition formation.

Agents can influence each other if they are close enough in knowledge. The probability to convince with good knowledge and number of agents have an impact on the dissemination of knowledge.

We develop an agent-based model for collective behavior of routine medical check-ups, and specifically dental visits, in a social network.

The O.R.E. (Opinions on Risky Events) model describes how a population of interacting individuals process information about a risk of natural catastrophe. The institutional information gives the official evaluation of the risk; the agents receive this communication, process it and also speak to each other processing further the information. The description of the algorithm (as it appears also in the paper) can be found in the attached file OREmodel_description.pdf.
The code (ORE_model.c), written in C, is commented. Also the datasets (inputFACEBOOK.txt and inputEMAILs.txt) of the real networks utilized with this model are available.

For any questions/requests, please write me at daniele.vilone@gmail.com

The model’s purpose is to provide a potential explanation for the emergence, sustenance and decline of unpopular norms based on pluralistic ignorance on a social network.

The modeling includes citizens, bounded into families; firms and governments; all of them interacting in markets for goods, labor and real estate. The model is spatial and dynamic.

This research aims to uncover the micro-mechanisms that drive the macro-level relationship between cultural tolerance and innovation. We focus on the indirect influence of minorities—specifically, workers with diverse domain expertise—within collaboration networks. We propose that minority influence from individuals with different expertise can serve as a key driver of organizational innovation, particularly in dynamic market environments, and that cultural tolerance is critical for enabling such minority-induced innovation. Our model demonstrates that seemingly conflicting empirical patterns between cultural tightness/looseness and innovation can emerge from the same underlying micro-mechanisms, depending on parameter values. A systematic simulation experiment revealed an optimal cultural configuration: a medium level of tolerance (t = 0.6) combined with low consistency (κ = 0.05) produced the fastest adaptation to abrupt market changes. These findings provide evidence that indirect minority influence is a core micro-mechanism linking cultural tolerance to innovation.

This model simulate product diffusion on different social network structures.

An agent-based model for the diffusion of innovations with multiple characteristics and price-premiums

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.