Suchergebnisse

This article uses computer simulations as a means of assessing two different models for the competition between two languages from the interdisciplinary perspective of complex systems. These models allow for the analysis of the role of bilingual speakers and they consider two of the basic factors determining the use of each language: their relative prestige and the language loyalty of their speakers. Here we assess the effect of these parameters in different social networks, with the aim of contributing to the understanding of the role of bilingualism and social networks in processes of language contact. We first present a qualitative analysis based on a simulation tool that we have designed, in order to visualize the dynamics of the models. Secondly, we consider a qualitative analysis of the role of complex networks, identifying mechanisms for the rapid extinction of a language and for situations of language segregation. Finally, we present a quantitative analysis which shows, counter-intuitively, a transition from a final scenario of language maintenance to one of language shift as language loyalty increases. Other findings are: (i) that the scenario of language coexistence is reduced in the presence of bilingual agents; and (ii) that networks which allow for local effects reduce further the situations for which language maintenance is possible. ; In addition, for generous financial support, Xavier Castelló and Maxi San Miguel thank the Spanish Ministry for Science and Innovation and the European Regional Development Fund (grant FIS2007-60327, FISICOS), and Lucía Loureiro-Porto thanks the Autonomous Government of Galicia (INCITE grant 08PXIB204016PR) and the Spanish Ministry for Science and Innovation and the European Regional Development Fund (grants HUM2007–60706 and FFI2011-26693-C02-02). ; Peer reviewed

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.

Agent-based simulation models can reproduce the interactions between members of an organization or between different organizations in an artificial environment where "agents" make decisions and communicate with one another. This article discusses possible applications to core issues in organization science and provides an introductory guide to simulation platforms. Agent-based modeling requires writing computer code, a skill that, if properly mastered, may turn into a career opportunity.

Foreword -- Acknowledgements -- Table of Contents -- List of Figures -- List of Tables -- List of Equations -- List of Symbols -- 1 Introduction -- 1.1 The Invention of the Wheel -- 1.2 Methodological and Modelling Framework -- 1.3 Research Question and Outline -- 2 The Role of Consumers in Innovation Economics -- 2.1 The Neglected Demand Side -- 2.1.1 The Linear Innovation Model -- 2.1.2 The Demand-Pull Modell of Innovation -- 2.1.3 A Multidimensional Perspective on Innovation Processes -- 2.2 An Evolutionary Perspective -- 2.3 The Role of the Demand Side Today -- 3 The New Agent-Based Paradigm in Economics -- 3.1 Three Pillars of ABM -- 3.1.1 Modelling from an Agent-Based Perspective -- 3.1.2 A Definition of Agents -- 3.1.3 Simulation as In-Silicio Laboratories -- 3.2 Using ABM as a Scientific Tool -- 3.2.1 Why Do We Need Agent-Based Modelling? -- 3.2.2 Managing the Complexity -- 3.2.3 Two Ways of Using Agent-Based Models -- 3.2.4 The Need for Verification, Validation and Calibration -- 3.3 Implications for the Following Analysis -- 4 An ABM of Heterogeneous Consumers and Demand -- 4.1 Introducing Remarks -- 4.2 The Baseline Simulation Model -- 4.2.1 Modelling Multi-Dimensional Product Characteristics -- 4.2.2 Basic Procedure of the Simulation Model -- 4.3 Simulation Experiments -- 4.3.1 Innovations in a Multidimensional Characteristic Space -- 4.3.2 Markets in-between Homogeneous and Heterogeneous Demand -- 4.3.3 Implications for Innovation Policies -- 4.4 Discussion -- 5 Networks of Heterogeneous Agents -- 5.1 Informal Knowledge Exchange in Firm Networks -- 5.1.1 Introducing Remarks -- 5.1.2 Knowledge Exchange and Network Formation Mechanisms -- 5.1.3 Model Analysis -- 5.2 The Importance of Consumer Networks -- 5.2.1 Introducing Remarks -- 5.2.2 Bounded Rationality of Consumers -- 5.2.3 The Effects of Consumer Networks -- 5.3 Discussion

The provision of valuable e-government services depends upon the capacity to integrate the disperse provision of services by the public administration and thus upon the availability of interoperability platforms. These platforms are commonly built according to the principles of service oriented architectures, which raise the question of how to dynamically orchestrate services while preserving information security. Recently, it was presented an e-government interoperability model that preserves privacy during the dynamic orchestration of services. In this paper we present a prototype that implements that model using software agents. The model and the prototype are briefly described; an illustrative use case is presented; and the advantages of using software agents to implement the model are discussed. © Springer International Publishing Switzerland 2013.

Agent-based modeling has become increasingly popular in recent years, but there is still no codified set of recommendations or practices for how to use these models within a program of empirical research. This article provides ideas and practical guidelines drawn from sociology, biology, computer science, epidemiology, and statistics. We first discuss the motivations for using agent-based models in both basic science and policy-oriented social research. Next, we provide an overview of methods and strategies for incorporating data on behavior and populations into agent-based models, and review techniques for validating and testing the sensitivity of agent-based models. We close with suggested directions for future research.

Multi-agent Systems (MAS) are one of the most exciting research areas in Artificial Intelligence meanwhile Environmental Studies is a research area of strategic interest. Both areas can provide society with solutions for many real applications, in order to use and protect the environment. Human activities imply intervention into nature, but properly managed, these interventions can not be only ecologically sound but also favourable to the sustainable development of civilisation. The encounter between these fields is a new challenge for many researchers of both communities. This book presents a comprehensive reference of state-of-the-art efforts. Specifically, it presents current and future ways in which adaptive information technologies, techniques, protocols and architectures, such as software agent technologies and multi-agent systems, can be used to support the development of real-world agent-based systems in the area of e-Environment.

Virtually all current major social and environmental challenges such as financial crises, migration, the erosion of democratic institutions, and the loss of biodiversity involve complex systems comprising decision-making, interacting, adaptive agents. To understand how such agent-based complex systems function and respond to change and disturbances, agent-based modeling (ABM) is increasingly recognized as the main way forward. Many motivating examples of agent-based models exist that are realistic enough to successfully support the management of complex systems, but these solutions are case-specific and contribute few general insights into the functioning of systems. General theory, though, is highly needed because we cannot model each system and question. Still, across disciplines, a critical mass of expertise has accumulated that could transform ABM into a more coherent and efficient approach to discover the functioning of complex social-economic-ecological systems. To this end, we need a cross-disciplinary discussion among researchers and a goal-oriented synthesis to identify the general principles and theories essential to improve our understanding and management of complex systems.

Cover -- Abstract -- Contents -- 1 Introduction -- 1.1 Agent-Based Modeling: A Brief Historical Review -- 1.2 Agent Network Dynamics -- 1.3 Outline of the Book -- 2 Network Awareness in Agent-based Models -- 2.1 Network Awareness -- 2.2 First-Generation Models: Lattices -- 2.3 Second-Generation Models: Graphs -- 2.4 Additional Notes -- 3 Collective Dynamics of Adaptive Agents -- 3.1 Collectives -- 3.2 Binary Choices with Externalities -- 3.3 Adaptive Choices with Reinforcement -- 3.4 Network Effects on Adaptive Choices -- 4 Agent-Based Models of Social Networks -- 4.1 Introduction