The field of artificial economics (AE) embraces a broad range of methodologies relying on computer simulations in order to model and study the complexity of economic and social phenomena. The overarching principle of AE is the analysis of aggregate properties of artificial economies populated by adaptive agents that are equipped with behavioural rules and specific individual targets. These aggregate properties are neither foreseen nor intended by the artificial agents; conversely they are emerging characteristics of such artificially simulated systems. The book presents a peer-reviewed collection of papers addressing a variety of issues related to macroeconomics, industrial organization, networks, management and finance, as well as purely methodological issues.

Managing Complexity is the first book that clearly defines the concept of Complexity, explains how Complexity can be measured and tuned, and describes the seven key features of Complex Systems: ConnectivityAutonomyEmergencyNonequilibriumNon-linearitySelf-organisationCo-evolution The thesis of the book is that complexity of the environment in which we work and live offers new opportunities and that the best strategy for surviving and prospering under conditions of complexity is to develop adaptability to perpetually changing conditions. An effective method for designing adaptability into business processes using multi-agent technology is presented and illustrated by several extensive examples, including adaptive, real-time scheduling of taxis, see-going tankers, road transport, supply chains, railway trains, production processes and swarms of small space satellites. Additional case studies include adaptive servicing of the International Space Station; adaptive processing of design changes of large structures such as wings of the largest airliner in the world; dynamic data mining, knowledge discovery and distributed semantic processing. Finally, the book provides a foretaste of the next generation of complex issues, notably, The Internet of Things, Smart Cities, Digital Enterprises and Smart Logistics.

The essays and lectures collected in this book center around knowledge transfer from the complex-system sciences to applications in business, industry and society, as viewed from a broad perspective. The contributions aim to raise awareness across the spectrum to meet the increasing need to integrate lessons from complexity research into everyday planning, decision making, logistics or optimization procedures and forecasting. The writing has been largely kept non-technical.

Michael Marti presents a complexity management model that is based on the reasoning that product architecture determines to a considerable extent how external complexity is translated into physical products. The model demonstrates a procedure to optimize a product’s architecture and is applied to several industrial products.

"This book describes a holistic approach to monitoring, evaluating, and applying appropriate marketing strategies, and understanding the competition and its future implication on the business of a company"--Provided by publisher.

Agent-based modeling and simulation (ABMS), a way to simulate a large number of choices by individual actors, is one of the most exciting practical developments in business modeling since the invention of relational databases. It represents a new way to understand data and generate information that has never been available before--a way for businesses to view the future and to understand and anticipate the likely effects of their decisions on their markets and industries. It thus promises to have far-reaching effects on the way that businesses in many areas use computers to support practical decision-making.Managing Business Complexity is the first complete business-oriented agent-based modeling and simulation resource. It has three purposes: first, to teach readers how to think about ABMS, that is, about agents and their interactions; second, to teach readers how to explain the features and advantages of ABMS to other people and third, to teach readers how to actually implement ABMS by building agent-based simulations. It is intended to be a complete ABMS resource, accessible to readers who haven't had any previous experience in building agent-based simulations, or any other kinds of models, for that matter. It is also a collection of ABMS business applications resources, all assembled in one place for the first time. In short, Managing Business Complexity addresses who needs ABMS and why, where and when ABMS can be applied to the everyday business problems that surround us, and how specifically to build these powerful agent-based models.

This book delivers new IMD insights on an emerging challenge - how to deal with overwhelming complexity. Global organizations face a complex decision-making environment. On one side, diversity of cultures, customers, competitors and regulations creates complexity; on the other, competitive pressures cause expanding countries to extract more synergies across products and regions. In such a climate, a new way of thinking, acting and organizing is needed beyond the familiar ‘control’ mindset. Drawing together insights from across the expert faculty, Managing Complexity in the Global Organization presents IMD’s framework on how to understand complexity and its four key drivers (diversity; interdependence; ambiguity and flux), along with solutions on specific issues in a variety of functions, industries and markets. The focus is on providing practical solutions based on real-life examples.