Perspectives on Languages for Specifying Simulation Experiments

While domain specific languages are well established for describing the system of interest in modeling and simulation, the last years have seen increasingly domain specific languages also exploited for specifying experiments. This development, whose application areas range from computational biology to network simulation, is motivated by the desire to facilitate the reproducibility of simulation results. Thereby, the experimentation process is treated as a first class object of simulation studies. As the experimentation process contains different tasks such as configuration, observation, analysis, and evaluation, domain-specific languages can be exploited to specify experiments as well as individual sub-tasks or even the goal of the experiment, thus opening up new avenues of research. The focus of our discussion will be on what information to express, also based on existing approaches. Referring to how to express the required information, we will sketch some of the pros and cons of external and embedded domain specific languages

Reusing simulation experiments for model composition and extension

This thesis aims to reuse simulation experiments to support developing models via model reuse, with a focus on validating the resulting model. Individual models are annotated with their simulation experiments. Upon reuse of those models for building new ones, the associated simulation experiments are also reused and executed with the new model, to inspect whether the key behavior exhibited by the original models is preserved or not in the new model. Hence, the changes of model behavior resulting from the model reuse are revealed, and insights into validity of the new model are provided

Domain-specific languages for modeling and simulation

Simulation models and simulation experiments are increasingly complex. One way to handle this complexity is developing software languages tailored to specific application domains, so-called domain-specific languages (DSLs). This thesis explores the potential of employing DSLs in modeling and simulation. We study different DSL design and implementation techniques and illustrate their benefits for expressing simulation models as well as simulation experiments with several examples.Simulationsmodelle und -experimente werden immer komplexer. Eine Möglichkeit, dieser Komplexität zu begegnen, ist, auf bestimmte Anwendungsgebiete spezialisierte Softwaresprachen, sogenannte domänenspezifische Sprachen (\emph{DSLs, domain-specific languages}), zu entwickeln. Die vorliegende Arbeit untersucht, wie DSLs in der Modellierung und Simulation eingesetzt werden können. Wir betrachten verschiedene Techniken für Entwicklung und Implementierung von DSLs und illustrieren ihren Nutzen für das Ausdrücken von Simulationsmodellen und -experimenten anhand einiger Beispiele

Data Farming: The Meanings and Methods Behind the Metaphor

17 USC 105 interim-entered record; under review.The article of record as published may be found at https://doi.org/10.36819/SW21.002Operational Research Society Simulation Workshop 2021Data farming captures the notion of purposeful data generation from simulation models. The ready availability of computing power has fundamentally changed the way simulation and other computational models can be used to provide insights to decision makers. Large-scale designed experiments let us grow the simulation output efficiently and effectively. We can explore massive input spaces, use statistical and visualization techniques to uncover interesting features of complex response surfaces, and explicitly identify cause-and-effect relationships. Nonetheless, there are many opportunities for research methods that could further enhance this process. I will begin with a brief overview of key differences between physical and simulation experiments, as well as current data farming capabilities and their relationship to emerging techniques in data science and analytics. I will then share some thoughts about opportunities and challenges for further improving the state of the art, and transforming the state of the practice, in this domain

Integrating knowledge about complex adaptive systems: insights from modelling the Eastern Baltic cod

Currently, the Eastern Baltic cod (EBC) is in continuing decline. Supporting management efforts to assist in its recovery will require a functional understanding of the dynamics of the EBC and the Baltic ecosystem. However, aquatic environments are challenging to research as they are elusive, encompass many scientific disciplines and are complex adaptive systems. This thesis explores how modelling and simulation methods can be applied and adapted to meet the specific needs of fisheries biologies’ current challenges regarding the EBC and potentially those of other stocks in similar situations.Aktuell verschlechtert sich der Zustand des Ostdorsches anhaltend und unterstützende Bewirtschaftungsmaßnahmen zu identifizieren erfordert ein funktionales Verständnis des Bestands und des Ökosystems Ostsee. Die Erforschung aquatischer Systeme ist jedoch schwierig: sie sind flüchtig, umfassen eine Vielzahl an Disziplinen und sind komplexe adaptiver Systeme. Diese Arbeit untersucht, wie Modellierungs- und Simulationsmethoden angewendet und angepasst werden können, um den Anforderungen der Fischereibiologie beim Ostdorsch und potentiell bei anderer Bestände in ähnlichen Situationen zu begegnen

Towards Bayesian Model-Based Demography

This open access book presents a ground-breaking approach to developing micro-foundations for demography and migration studies. It offers a unique and novel methodology for creating empirically grounded agent-based models of international migration – one of the most uncertain population processes and a top-priority policy area. The book discusses in detail the process of building a simulation model of migration, based on a population of intelligent, cognitive agents, their networks and institutions, all interacting with one another. The proposed model-based approach integrates behavioural and social theory with formal modelling, by embedding the interdisciplinary modelling process within a wider inductive framework based on the Bayesian statistical reasoning. Principles of uncertainty quantification are used to devise innovative computer-based simulations, and to learn about modelling the simulated individuals and the way they make decisions. The identified knowledge gaps are subsequently filled with information from dedicated laboratory experiments on cognitive aspects of human decision-making under uncertainty. In this way, the models are built iteratively, from the bottom up, filling an important epistemological gap in migration studies, and social sciences more broadly

Towards Bayesian Model-Based Demography

This open access book presents a ground-breaking approach to developing micro-foundations for demography and migration studies. It offers a unique and novel methodology for creating empirically grounded agent-based models of international migration – one of the most uncertain population processes and a top-priority policy area. The book discusses in detail the process of building a simulation model of migration, based on a population of intelligent, cognitive agents, their networks and institutions, all interacting with one another. The proposed model-based approach integrates behavioural and social theory with formal modelling, by embedding the interdisciplinary modelling process within a wider inductive framework based on the Bayesian statistical reasoning. Principles of uncertainty quantification are used to devise innovative computer-based simulations, and to learn about modelling the simulated individuals and the way they make decisions. The identified knowledge gaps are subsequently filled with information from dedicated laboratory experiments on cognitive aspects of human decision-making under uncertainty. In this way, the models are built iteratively, from the bottom up, filling an important epistemological gap in migration studies, and social sciences more broadly

Exploring the spatio-temporal dynamics of lipid rafts and their role in Signal transduction: a modeling and simulation approach

The aim of this thesis is to elucidate the biological as well as methodological implications that arise from modeling the spatio-temporal dynamics of lipid rafts. Therefore the effect of raft-dependent receptors dynamics on both, individual signaling events as well as the canonical Wnt signaling pathway, is thoroughly analyzed. To explore the effect of lipid rafts on individual signaling events, a Cellular-Automata based membrane model is developed. The specific involvement of lipid rafts in Wnt/β-catenin signaling is explored by means of an integrated in silico and in vitro approach

ML-Space: hybrid spatial Gillespie and Brownian motion simulation at multiple levels, and a rule-based description language

Computer simulations of biological cells as well-stirred systems are well established but neglect the spatial distribution of key actors. In this thesis, a simulation algorithm "ML-Space" for spatial models with dynamic hierarchies is presented. It combines stochastic spatial algorithms in discretized space with individual particles moving in continuous space that have spatial extensions and can contain other particles. For formal descriptions of the systems to be simulated spatially, ML-Space provides a rule-based specification language.Computersimulationen mikrobiologischer Prozesse, bei denen eine homogene Verteilung der Akteure einer Zelle angenommen wird, sind gut etabliert. In dieser Arbeit wird ein räumlicher Simulationsalgorithmus "ML-Space" für Mehrebenenmodelle vorgestellt, der auch dynamische Hierarchien abdeckt. Er vereint stochastische räumliche Algorithmen in diskretisiertem Raum mit individuellen Partikeln mit kontinuierlichen Koordinaten, die andere Partikel enthalten können. Zur formalen Beschreibung der räumlich zu simulierenden Systeme bietet ML-Space eine regelbasierte Modellierungssprache

Toward guiding simulation experiments

To face the variety of simulation experiment methods, tools are needed that allow their seamless integration, guide the user through the steps of an experiment, and support him in selecting the most suitable method for the task at hand. This work presents techniques for facing such challenges. To guide users through the experiment process, six typical tasks have been identified for structuring the experiment workflow. The M&S framework JAMES II and its plug-in system is exploited to integrate various methods. Finally, an approach for automatic selection and use of such methods is realized