Suchergebnisse

"Agent-Based Computational Modeling and International Relations Theory: Quo Vadis?" published on by Oxford University Press.

Research on loss absorption of financial group (bank network) -- The Mathematics of Human Contact -- Does it still matter in the new world where a refugee comes from? - Social network, Shocks, and Ethnicity - A multi-level analysis -- The Transferability of Human Capital, the Brain Drain, and the Brain Gain -- Evolution in Anonymous Population Games with Multiple Types -- Analysis of search actions on the Internet including the effect of blog and Twitter using Sociophysics approach -- Statistical analysis of a political demonstration using location-based big data -- Different Type of Interaction Plays a Role of Decision Error on Collective Behavior -- A financial network approach to unconventional monetary policy assessment - the case of Quantitative Easing in the euro area -- From Housing Locale Theory to Agent-Based Modeling Approach

"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 models (ABMs) provide a methodology to explore systems of interacting, adaptive, diverse, spatially situated actors. Outcomes in ABMs can be equilibrium points, equilibrium distributions, cycles, randomness, or complex patterns; these outcomes are not directly determined by assumptions but instead emerge from the interactions of actors in the model. These behaviors may range from rational and payoff-maximizing strategies to rules that mimic heuristics identified by cognitive science. Agent-based techniques can be applied in isolation to create high-fidelity models and to explore new questions using simple constructions. They can also be used as a complement to deductive techniques. Overall, ABMs offer the potential to advance social sciences and to help us better understand our complex world.

Agent-based models (ABMs) provide a methodology to explore systems of interacting, adaptive, diverse, spatially situated actors. Outcomes in ABMs can be equilibrium points, equilibrium distributions, cycles, randomness, or complex patterns; these outcomes are not directly determined by assumptions but instead emerge from the interactions of actors in the model. These behaviors may range from rational and payoff-maximizing strategies to rules that mimic heuristics identified by cognitive science. Agent-based techniques can be applied in isolation to create high-fidelity models and to explore new questions using simple constructions. They can also be used as a complement to deductive techniques. Overall, ABMs offer the potential to advance social sciences and to help us better understand our complex world. Adapted from the source document.

Agent-based computational economics (ACE) is the computational study of economic processes modeled as dynamic systems of interacting agents. Here agentrefers broadly to a bundle of data and behavioral methods representing an entity constituting part of a computationally constructed world. Examples of possible agents include individuals (e.g. consumers, producers), social groupings (e.g. families, firms, communities, government agencies), institutions (e.g. markets, regulatory systems), biological entities (e.g. crops, livestock, forests), and physical entities (e.g. infrastructure, weather, and geographical regions). Thus, agents can range from active data-gathering decision makers with sophisticated embodied cognitive capabilities to passive world features with no cognitive function.

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.

Aims to give a view of the scientific production in the fields of Agent-based Computational Economics, mainly in Market Finance and Game Theory. Based on communications given at AE'2005 (Lille, USTL, France), this book offers a panorama of advances in ACE, both theoretical and methodological that is of interest academics as well as practitioners