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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.

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.

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.

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.

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

Matthias Müller makes a case for the particular role of the demand side in research on innovation. Based on a complex agent-based simulation model, he analyzes the versatile mutual relationships between consumers and producers within the innovation process. Instead of oversimplifying the demand side, the book aims to apply important aspects which too often are only applied to the supply side, e.g., the heterogeneity and bounded rationality of economic actors embedded in networks. The results offer a new perspective on the innovation process, proving that the demand side and consumers are important drivers of innovation, which must be included in future research for a full picture. Contents The Role of Consumers in Innovation Economics Evolutionary Economics The New Agent-Based Paradigm in Economics An ABM of Heterogeneous Consumers and Demand Consumer Networks Bounded Morality of Consumers Target Groups Researchers and students in the fields of innovation economics, evolutionary economics, complexity science, and computer simulation Policy making, public administration, and innovation management The Author Matthias Müller conducted his doctoral research at the University of Hohenheim, Germany. He currently works as a postdoctoral researcher in the field of innovation economics.

AbstractPolice corruption, especially in the form of bribery, is a severe social problem in many societies. However, neither the extent nor the factors contributing to police bribery are well understood because of data limitation issues. Understandably, it is incredibly challenging to observe and quantify such bribery, as it is usually considered illegal and/or unethical for police to accept and/or ask for bribes. Agent‐based modelling can solve such data limitation issues because it allows for the realistic modelling of hidden behaviours. This study uses an agent‐based modelling technique to investigate a threshold model of police corruption, more specifically, bribery. The authors assume that agents have a threshold regarding bribery, which may be conceptualised as either an honesty threshold or a risk threshold. The threshold value is a dynamic variable randomly assigned to each agent, and each interaction between citizens and officers possesses the potential to change the threshold of each agent.

In this paper, we employ techniques from artificial intelligence such as reinforcement learning and agent based modeling as building blocks of a computational model for an economy based on conventions. First we model the interaction among firms in the private sector. These firms behave in an information environment based on conventions, meaning that a firm is likely to behave as its neighbors if it observes that their actions lead to a good pay off. On the other hand, we propose the use of reinforcement learning as a computational model for the role of the government in the economy, as the agent that determines the fiscal policy, and whose objective is to maximize the growth of the economy. We present the implementation of a simulator of the proposed model based on SWARM, that employs the SARSA(λ) algorithm combined with a multilayer perceptron as the function approximation for the action value function.