Suchergebnisse

"Agent-Based Modelling ist der zurzeit gängige Ansatz zur computergestützten Theoriebildung in den Sozialwissenschaften. Seine Wurzeln liegen in verschiedenen sozialwissenschaftlichen Simulationsverfahren der letzten fünf oder sechs Jahrzehnte und in der Forschung zur Künstlichen Intelligenz. Unter agentenbasierter Simulation versteht man die Benutzung eines aus vielen autonomen Software-Objekten (Agenten) bestehenden formalen Modells zum Verständnis, zur Vorhersage oder zur Veranschaulichung von Prozessen, die in der realen Welt zwischen menschlichen Individuen auf Mikro-, Meso- oder Makroebene ablaufen. Das Modell bildet wesentliche Züge des modellierten Weltausschnitts ab, unterschiedliche Anfangsbedingungen und Parameter bringen im Allgemeinen qualitativ oder mindestens quantitativ verschiedene Prozessverläufe hervor. In den Sozialwissenschaften, die sich mit besonders komplexen Systemen beschäftigen, in denen große Zahlen von Komponenten einander in höchst vielfältiger Weise beeinflussen, wird die Methode vielfältig genutzt, um aus Annahmen über Verhalten und Handlungsbedingungen von Individuen Schlussfolgerungen über strukturbildende Prozesse auf der Ebene von Gruppen oder gar Gesellschaften zu ziehen. Als computergestützte Variante des Plan- oder Rollenspiels, bei der Teile des modellierten Realitätsausschnitts durch Menschen, andere durch Computerprogramme abgebildet werden, setzt sich in letzter Zeit die partizipative agentenbasierte Simulation durch." (Autorenreferat)

Agent-based modelling in economics Lynne Hamill and Nigel Gilbert, Centre for Research in Social Simulation (CRESS), University of Surrey, UK New methods of economic modelling have been sought as a result of the global economic downturn in 2008. This unique book highlights the benefits of an agent-based modelling (ABM) approach. It demonstrates how ABM can easily handle complexity: heterogeneous people, households and firms interacting dynamically. Unlike traditional methods, ABM dus not require people or firms to optimise or economic systems to reach equilibrium. ABM offers a way to link micro foundations directly to the macro situation. Key features: -Introduces the concept of agent-based modelling and shows how it differs from existing approaches.-Provides a theoretical and methodological rationale for using ABM in economics, along with practical advice on how to design and create the models.-Each chapter starts with a short summary of the relevant economic theory and then shows how to apply ABM.-Explores both topics covered in basic economics textbooks and current important policy themes; unemployment, exchange rates, banking and environmental issues.-Describes the models in pseudocode, enabling the reader to develop programs in their chosen language.-Supported by a website featuring the NetLogo models described in the book. Agent-based Modelling in Economics provides students and researchers with the skills to design, implement, and analyze agent-based models. Third year undergraduate, master and doctoral students, faculty and professional economists will find this book an invaluable resource.

Agent-based models have played a prominent role in recent debates about the merits of democracy. In particular, the formal model of Lu Hong and Scott Page and the associated "diversity trumps ability" result has typically been seen to support the epistemic virtues of democracy over epistocracy (i.e., governance by experts). In this paper we first identify the modeling choices embodied in the original formal model and then critique the application of the Hong-Page results to philosophical debates on the relative merits of democracy. In particular we argue that the "best-performing agents" in the Hong-Page model should not be interpreted as experts. We next explore a closely related model in which best-performing agents are more plausibly seen as experts and show that the diversity trumps ability result fails to hold. However, with changes in other parameters (such as the deliberation dynamic) the diversity trumps ability result is restored. The sensitivity of this result to parameter choices illustrates the complexity of the link between formal modeling and more general philosophical claims; we use this debate as a platform for a more general discussion of when and how agent-based models can contribute to philosophical discussions.

Edited by André Grow and Jan Van BavelSpringer International Publishing 2017 ISBN: 978-3-319-32281-0Hardcover, $159.99 US, eBook, $119 US, 513 pp.

Decision makers in large scale interconnected network systems require simulation models for decision support. The behaviour of these systems is determined by many actors, situated in a dynamic, multi-actor, multi-objective and multi-level environment. How can such systems be modelled and how can the socio-technical complexity be captured? Agent-based modelling is a proven approach to handle this challenge. This book provides a practical introduction to agent-based modelling of socio-technical systems, based on a methodology that has been developed at Delft University of Technology and which has been deployed in a large number of case studies. The book consists of two parts: the first presents the background, theory and methodology as well as practical guidelines and procedures for building models. In the second part this theory is applied to a number of case studies, where for each model the development steps are presented extensively, preparing the reader for creating own models.

Although Agent Based Models (hereafter ABM) are now regularly reported in sociology journals, explaining the approach, describing models and reporting results leaves little opportunity to examine wider implications of ABM for sociological practice. This article uses an established ABM (the Schelling model) for this. The first part argues that ABM integrates qualitative and quantitative data distinctively, provides novel tools for understanding social causes and offers a significantly different perspective on theory building. The second part shows how the emerging ABM methodology is compatible with existing sociological practice while undermining several criticisms of ABM perceived to limit its sociological relevance.

This book thoroughly prepares intermediate-level readers for research in social science, organization studies, economics, finance, marketing science, and business science as complex adaptive systems. It presents the advantages of social simulation studies and business intelligence to those who are not familiar with the computational research approach, and offers experienced modelers various instructive examples of using agent-based modeling and business intelligence approaches to inspire their own work. In addition, the book discusses cutting-edge techniques for complex adaptive systems using their applications. To date, business science studies have focused only on data science and analyses of business problems. However, using these studies to enhance the capabilities of conventional techniques in the fields has not been investigated adequately. This book addresses managing the issues of societies, firms, and organizations to profit from interaction with agent-based modeling, human- and computer- mixed systems, and business intelligence approaches, an area that is fundamental for complex but bounded rational business environments. With detailed research by leading authors in the field, Innovative Approaches in Agent-Based Modelling and Business Intelligence inspires readers to join with other disciplines and extend the scope of the book with their own unique contributions. It also includes the common challenges encountered in computational social science and business science to enable researchers, students, and professionals to resolve their own problems.

We discuss the use of Agent-based Modelling for the development and testing of theories about emergent social phenomena in marketing and the social sciences in general. We address both theoretical aspects about the types of phenomena that are suitably addressed with this approach and practical guidelines to help plan and structure the development of a theory about the causes of such a phenomenon in conjunction with a matching ABM. We argue that research about complex social phenomena is still largely fundamental research and therefore an iterative and cyclical development process of both theory and model is to be expected. To better anticipate and manage this process, we provide theoretical and practical guidelines. These may help to identify and structure the domain of candidate explanations for a social phenomenon, and furthermore assist the process of model implementation and subsequent development. The main goal of this paper was to make research on complex social systems more accessible and help anticipate and structure the research process.

Growing and resource-intensive material consumption patterns are considered as major drivers of global resource use and growing waste streams contributing to increasing greenhouse gas emissions (Fleurbaey et al., 2014). Recently, the Circular Economy (CE) paradigm and related practices are discussed as a key vehicle to establish more production and sustainable consumption traditions by decoupling economic output from natural resource input and environmentally harmful emissions (Ghisellini et al., 2016). While a plethora of innovative conceptual contributions describing the target state of a CE on various levels exist, the transition towards this state seems to be the much greater challenge. Fundamental questions on regulatory, economic, organizational, and technical levels remain unanswered. Acknowledging the incomprehensible complexity and convolution of the CE approach – in terms of the numerous interrelated political, social, economic, and technical factors – and the transition towards it, I deliberately call this phenomenon a "wicked problem" (Ketter et al., 2016; Rittel and Webber, 1973). Agent-based modelling and simulation (ABMS) has proven to be a suitable scientific instrument to investigate such complex, socio-technical problems (Epstein, 2011; Bonabeau, 2002). Applications of the ABMS technique to the CE problem space, however, remain notably absent. In this workshop paper, I develop an argument explaining why the ABMS technique is specifically applicable to CE scenarios and how this research trajectory can offer valuable scientific contributions to the CE research community. Considering the societal relevance of the CE ap-proach and its current challenges, on the one hand, as well as the proven methodological suitability and promising scientific benefits of ABMS, on the other, I deem this so-far neglected sustainability phenomenon important to the ABMS community – and vice versa.

Preface -- Contents -- Contributors -- Part I Perspectives on Agent-Based Modelling in Population Studies -- 1 Introduction: Agent-Based Modelling as a Tool to Advance Evolutionary Population Theory -- 1.1 Introduction -- 1.2 Two Concepts of Population -- 1.3 How Agent-Based Modelling May Bridge the Two Approaches to Population -- 1.3.1 The Macro-Micro-Macro Model and Agent-Based Modelling -- 1.3.2 Empirical Calibration -- 1.3.3 Avoiding Potential Pitfalls -- 1.3.4 Bridging the Gap -- 1.4 Contributions to Agent-Based Modelling in this Book

This book examines the use of agent-based modelling (ABM) in population studies, from concepts to applications, best practices to future developments. It features papers written by leading experts in the field that will help readers to better understand the usefulness of ABM for population projections, how ABM can be injected with empirical data to achieve a better match between model and reality, how geographic information can be fruitfully used in ABM, and how ABM results can be reported effectively and correctly. Coverage ranges from detailing the relation between ABM and existing paradigms in population studies to infusing agent-based models with empirical data. The papers show the benefits that ABM offers the field, including enhanced theory formation by better linking the micro level with the macro level, the ability to represent populations more adequately as complex systems, and the possibility to study rare events and the implications of alternative mechanisms in artificial laboratories. In addition, readers will discover guidelines and best practices with detailed examples of how to apply agent-based models in different areas of population research, including human mating behaviour, migration, and socio-structural determinants of health behaviours. Earlier versions of the papers in this book have been presented at the workshop "Recent Developments and Future Directions in Agent-Based Modelling in Population Studies," which took place at the University of Leuven (KU Leuven), Belgium, in September 2014. The book will contribute to the development of best practices in the field and will provide a solid point of reference for scholars who want to start using agent-based modelling in their own research