The Development of Social Simulation as Reflected in the First Ten Years of JASSS: a Citation and Co-Citation Analysis

Social simulation is often described as a multidisciplinary and fast-moving field. This can make it difficult to obtain an overview of the field both for contributing researchers and for outsiders who are interested in social simulation. The Journal for Artificial Societies and Social Simulation (JASSS) completing its tenth year provides a good opportunity to take stock of what happened over this time period. First, we use citation analysis to identify the most influential publications and to verify characteristics of social simulation such as its multidisciplinary nature. Then, we perform a co-citation analysis to visualize the intellectual structure of social simulation and its development. Overall, the analysis shows social simulation both in its early stage and during its first steps towards becoming a more differentiated discipline.Citation Analysis, Co-Citation Analysis, Lines of Research, Multidisciplinary, Science Studies, Social Simulation

The PLS agent : agent behavior validation by partial least squares

Agent-based modeling is widely applied in the social sciences. However, the validation of agent behavior is challenging and identified as one of the shortcomings in the field. Methods are required to establish empirical links and support the implementation of valid agent models. This paper contributes to this, by introducing the PLS agent concept. This approach shows a way to transfer results about causalities and decision criteria from empirical surveys into an agent-based decision model, through processing the output of a PLS-SEM model. This should simplify and foster the use of empirical results in agent-based simulation and support collaborative studies over the disciplines

Economic aspects of the COVID-19 pandemic for higher education institutions

Like faculty across the globe, Iris Lorscheid, Professor for Digital Business and Data Science at Germany’s University of Europe for Applied Science (UE Germany), has had to cope with the disruption caused by the COVID-19 pandemic. In the last of Lingnan University’s series of Global Higher Education webinars, Prof Lorscheid examined the economic ramifications for higher education institutions of this disruption. Highlights: https://www.ln.edu.hk/global-higher-education-webinar-series/global06.htm

Toward a better understanding of team decision processes: combining laboratory experiments with agent-based modeling

Despite advances in the field, we still know little about the socio-cognitive processes of team decisions, particularly their emergence from an individual level and transition to a team level. This study investigates team decision processes by using an agent-based model to conceptualize team decisions as an emergent property. It uses a mixed-method research design with a laboratory experiment providing qualitative and quantitative input for the model's construction, as well as data for an output validation of the model. First, the laboratory experiment generates data about individual and team cognition structures. Then, the agent-based model is used as a computational testbed to contrast several processes of team decision making, representing potential, simplified mechanisms of how a team decision emerges. The increasing overall fit of the simulation and empirical results indicates that the modeled decision processes can at least partly explain the observed team decisions. Overall, we contribute to the current literature by presenting an innovative mixed-method approach that opens and exposes the black box of team decision processes beyond well-known static attributes

Recent development of social simulation as reflected in JASSS between 2008 and 2014 : a citation and co-citation analysis

The research field of social simulation comprises many topics and research directions. A previous study about the early years indicated that the community has evolved into a differentiated discipline. This paper investigates the recent development of social simulation as reflected in Journal of Artificial Societies and Social Simulation (JASSS) publications from 2008 to 2014. By using citation analysis, we identify the most influential publications and study the characteristics of citations. Additionally, we analyze the development of the field with respect to research topics and their structure in a co-citation analysis. The citation characteristics support the continuing highly multidisciplinary character of JASSS. Prominently cited are methodological papers and books, standards, and NetLogo as the main simulation tool. With respect to the focus of this research, we observe continuity in topics such as opinion dynamics and the evolution of cooperation. While some topics disappeared such as learning, new subjects emerged such as marriage formation models and tools and platforms. Overall, one can observe a maturing inter- and multidisciplinary scientific community in which both methodological issues and specific social science topics are discussed and standards have emerged

The PLS agent : agent behavior validation by partial least squares

Agent-based modeling is widely applied in the social sciences. However, the validation of agent behavior is challenging and identified as one of the shortcomings in the field. Methods are required to establish empirical links and support the implementation of valid agent models. This paper contributes to this, by introducing the PLS agent concept. This approach shows a way to transfer results about causalities and decision criteria from empirical surveys into an agent-based decision model, through processing the output of a PLS-SEM model. This should simplify and foster the use of empirical results in agent-based simulation and support collaborative studies over the disciplines

Individuals and their interactions in demand planning processes: an agent-based, computational testbed

<p>The demand planning process in semiconductor supply chains faces many challenges. In this process, individuals, their properties such as sensing capabilities and their interactions play a crucial role. This paper shows how agent-based modelling (ABM) can provide a computational testbed to investigate these aspects with respect to forecast accuracy. Based on the requirements of the demand planning context, we develop an empirically validated agent-based model of the demand planning process. In this model, we incorporate different concepts from behavioural science and the distributed cognition perspective. We show the usefulness of this agent-based computational testbed by using a case study from the semiconductor industry. Our model shows that demand planning accuracy does not depend on the planning capabilities of planners alone, but that the interactions of the individuals, emerging from the planning process design, may both positively and negatively affect accuracy.</p

The complexities of agent-based modeling output analysis

The proliferation of agent-based models (ABMs) in recent decades has motivated model practitioners to improve the transparency, replicability, and trust in results derived from ABMs. The complexity of ABMs has risen in stride with advances in computing power and resources, resulting in larger models with complex interactions and learning and whose outputs are often high-dimensional and require sophisticated analytical approaches. Similarly, the increasing use of data and dynamics in ABMs has further enhanced the complexity of their outputs. In this article, we offer an overview of the state-of-the-art approaches in analyzing and reporting ABM outputs highlighting challenges and outstanding issues. In particular, we examine issues surrounding variance stability (in connection with determination of appropriate number of runs and hypothesis testing), sensitivity analysis, spatio-temporal analysis, visualization, and effective communication of all these to non-technical audiences, such as various stakeholders.This material is based upon work supported by NWO DID MIRACLE (640-006-012), NWO VENI grant (451-11-033), and EU FP7 COMPLEX (308601)