Foundations of GAM Research. Methodological Guidelines for Designing and Conducting Research that Combines Games and Agent-based Models

This thesis presents the development of the games and agent-based model methodology and provides methodological guidelines for using GAM research, i.e., combining games and agent-based models in research. GAM research is rooted in complexity sciences and transdisciplinary research, offering valuable insights into complex, adaptable systems. GAM research has particular relevance in decision-making and complex-system management, thus fostering collaboration among scientists and non-academics from various disciplines. It is an engaging platform for data collection and stakeholder processes, thus enriching causal explanations. It should be noted that GAM research has the potential to overcome the limitations of traditional methods by facilitating hypothesis testing with simulation-based observations of human behaviours. Investigations in GAM research can change how social science addresses pressing global challenges. The immersive nature of games combined with agent-based models offers an innovative approach that attracts diverse participants, making it a promising tool for science that reaches beyond the classic academic spheres. As a comprehensive handbook, this thesis offers researchers inspiration and references for conducting GAM research across diverse application domains. This thesis presents an assessment of the state of research that combines games and agent-based models and proposes a structured approach to making progress in this field. Addressing the lack of a standardised methodology, this thesis is aimed at improving research practices, transparency, and replicability . Practical advice is provided for guiding researchers through designing and conducting GAM research, thus promoting rigorous and comprehensive studies

Quantum Leaper: A Methodology Journey From a Model in NetLogo to a Game in Unity

Combining Games and Agent-Based Models (ABMs) in a single research design (i.e. GAM design) shows potential for investigating complex past, present, or future social phenomena. Games offer engaging environments that can help generating insights into social dynamics, perceptions, and behaviours, while ABMs support the representation and analysis of complexity. We present here the first attempt to “discipline” the interdisciplinary endeavour of developing a GAM design in which an ABM is transformed into a game, thus the two becoming intertwined in one application. When doing this, we use as a GAM design exemplar the process of developing Quantum Leaper, a proof-of-concept video game made in Unity software and based on the NetLogo implementation of the well known “Artificial Anasazi” ABM. This study aims to consolidate the methodology component of the GAM field by proposing the GAM Reflection Framework, a tool that can be used by GAM practitioners, ABM modellers, or game designers looking for methodological guidance with developing an agent-based model that is a game (i.e. an agent-based game)

GAM on! Six ways to explore social complexity by combining games and agent-based models

GAM, combining games and agent-based models, shows potential for investigating complex social phenomena. Games offer engaging environments generating insights into social dynamics, perceptions, and behaviours, while agent-based models support the analysis of complexity. Games and agent-based models share the important ability both to input and output qualitative and quantitative data. Currently, there is no overview of GAM approaches. In a systematic literature review, we identified six research design types in empirical studies to date. The functional range of these design types is wide, with diverse application domains involving analogue, digital, and hybrid games. This makes GAM a highly versatile approach, appealing to researchers in both natural and social sciences, along with the gaming community itself. To consolidate the GAM field, we propose recording the design and implementation of studies that combine games and agent-based models by using a dedicated documentation scheme

Ten-year trends in epidemiology and outcomes of pediatric kidney replacement therapy in Europe : data from the ESPN/ERA-EDTA Registry

Background For 10 consecutive years, the ESPN/ERA-EDTA Registry has included data on children with stage 5 chronic kidney disease (CKD 5) receiving kidney replacement therapy (KRT) in Europe. We examined trends in incidence and prevalence of KRT and patient survival. Methods We included all children agedPeer reviewe

Sensemaking of causality in agent-based models

Even though agent-based modelling is seen as committing to a mechanistic, generative type of causation, the methodology allows for representing many other types of causal explanations. Agent-based models are capable of integrating diverse causal relationships into coherent causal mechanisms. They mirror the crucial, multi-level component of emergent phenomena and recognize the important role of single-level causes without limiting the scope of the offered explana- tion. Implementing various types of causal relationships to complement the generative causation offers insight into how a multi-level phenomenon happens and allows for building more complete causal explanations. The capacity to work with multiple approaches to causality is crucial when tackling the complex problems of the modern world

Sensemaking of causality in agent-based models

Even though agent-based modelling is seen as committing to a mechanistic, generative type of causation, the methodology allows for representing many other types of causal explanations. Agent-based models are capable of integrating diverse causal relationships into coherent causal mechanisms. They mirror the crucial, multi-level component of emergent phenomena and recognize the important role of single-level causes without limiting the scope of the offered explana- tion. Implementing various types of causal relationships to complement the generative causation offers insight into how a multi-level phenomenon happens and allows for building more complete causal explanations. The capacity to work with multiple approaches to causality is crucial when tackling the complex problems of the modern world.publishedVersio

Sensemaking of causality in agent-based models

Even though agent-based modelling is seen as committing to a mechanistic, generative type of causation, the methodology allows for representing many other types of causal explanations. Agent-based models are capable of integrating diverse causal relationships into coherent causal mechanisms. They mirror the crucial, multi-level component of emergent phenomena and recognize the important role of single-level causes without limiting the scope of the offered explana- tion. Implementing various types of causal relationships to complement the generative causation offers insight into how a multi-level phenomenon happens and allows for building more complete causal explanations. The capacity to work with multiple approaches to causality is crucial when tackling the complex problems of the modern world