This book considers the benefits of complexity, suggesting that economists should become a bit less certain in their policy conclusions. A broader range of models would include agent-based models, which use computational power to deal with specification of models that are far beyond analytic solution; and non-linear dynamic stochastic models, many of which are beyond analytic solution, but whose nature can be discovered by a combination of analytics and computer simulations.
Complexity Systems in the Social and Behavioral Sciences provides a sophisticated yet accessible account of complexity science or complex systems research. Phenomena in the behavioral, social, and hard sciences all exhibit certain important similarities consistent with complex systems. These include the concept of emergence, sensitivity to initial conditions, and interactions between agents in a system that yield unanticipated, nonlinear outcomes. The topics discussed range from the implications for artificial intelligence and computing to questions about how to model complex systems through agent-based modeling, to complex phenomena exhibited in international relations, and in organizational behavior. This volume will be an invaluable addition for both the general reader and the specialist, offering new insights into this fascinating area of research.
The transition from a catching-up style economy to an innovation-driven economy poses a major challenge for China. This book examines the major issues at stake, outlines developments in crucial business fields and industries, and discusses the roles of top-down politics and bottom-up entrepreneurship. It focuses in particular on the institutional foundations of innovation, arguing that successful innovation relies on the favourable interplay of business, politics, and society, and that comprehensive institutional and organizational changes will be required in China in order for innovation to succeed. Overall, the book assesses how far China will be able to depart from the Western paradigm of successful innovation regimes and create its own innovation system with Chinese characteristics.
The book on complex systems, sustainability, and innovation explores a broad set of ideas and presents some of the state-of-the-art research in this field concisely in six chapters. In a complex system, it is difficult to know exactly how the individual components contribute to an observed behavior and the extent of each component's contributions. It is the interactions of the individual components that determine the emergent functionalities. This makes it difficult to understand and predict the behavior of complex systems and hence the effects of any innovations in this field. This necessitates for the emergence of a new age of innovations with the main focus on user orientation and sustainability. This book explores some of the complex systems and their dependence on the environment to provide a long-term perspective, aiding innovations and supporting a sustainable society. The intended audience of this book will mainly consist of researchers, research students, and practitioners in the field of complex systems and sustainability.
This book aims to answer two questions that are fundamental to the study of agent-based economic models: what is agent-based computational economics and why do we need agent-based economic modelling of economy? This book provides a review of the development of agent-based computational economics (ACE) from a perspective on how artificial economic agents are designed under the influences of complex sciences, experimental economics, artificial intelligence, evolutionary biology, psychology, anthropology and neuroscience. This book begins with a historical review of ACE by tracing its origins. From a modelling viewpoint, ACE brings truly decentralized procedures into market analysis, from a single market to the whole economy. This book also reviews how experimental economics and artificial intelligence have shaped the development of ACE. For the former, the book discusses how ACE models can be used to analyse the economic consequences of cognitive capacity, personality and cultural inheritance. For the latter, the book covers the various tools used to construct artificial adaptive agents, including reinforcement learning, fuzzy decision rules, neural networks, and evolutionary computation. This book will be of interest to graduate students researching computational economics, experimental economics, behavioural economics, and research methodology.
Finance, Econometrics and System Dynamics presents an overview of the concepts and tools for analyzing complex systems in a wide range of fields. The text integrates complexity with deterministic equations and concepts from real world examples, and appeals to a broad audience.
In economics agents are assumed to choose on the basis of rational calculations aimed at the maximization of their pleasure or profit. Formally, agents are said to manifest transitive and consistent preferences in attempting to maximize their utility in the presence of several constraints. They operate according to the choice imperative: given a set of alternatives, choose the best. This imperative works well in a static and simplistic framework, but it may fail or vary when 'the best' is changing continuously. This approach has been questioned by a descriptive approach that springing from the complexity theory tries to give a scientific basis to the way in which individuals really choose, showing that those models of human nature is routinely falsified by experiments since people are neither selfish nor rational. Thus inductive rules of thumb are usually implemented in order to make decisions in the presence of incomplete and heterogeneous information sets.
Most research about financial stability and sustainable growth focuses on the financial sector and macroeconomics and neglects the real sector, microeconomics and psychology issues. Real-sector and financial-sectors linkages are increasing and are a foundation of economic/social/environmental/urban sustainability, given financial crises, noise, internet, “transition economics”, disintermediation, demographics and inequality around the world. Within complex systems theory framework, this book analyses some multi-sided mechanisms and risk-perception that can have symbiotic relationships with financial stability, systemic risk and/or sustainable growth. Within the context of Regret Minimization, MN-Transferable Utility and WTAL, new theories-of-the-firm are developed that consider sustainable growth, price stability, globalization, financial stability and birth-to-death evolutions of firms. This book introduces new behaviour theories pertaining to real estate and intangibles, which can affect the evolutions of risk-taking and risk perception within organizations and investment entities. The chapters address elements of the dilemma of often divergent risk perceptions of, and risk-taking by corporate executives, regulators and investment managers.
This book explores a most central phenomenon in our contemporary businesses and organization, the growing complexity in business. Economic growth and growth of complexity always have been inseparable, but the last decennia the growth of complexity appears to outrun our growth of knowledge and understanding. For success and continuity, the modern firm in the developing complexity of its markets and societal contexts must have the capacity to master and exploit a commensurate level of complexity in its internal organization. This book is the first of its kind to help the reader to understand the different types of complexity and the different concepts and tools to deal with each of them in business administration, strategy, and organization. This book offers the reader a fresh perspective on conventional concepts and tools in the field of business administration and bridges these to new concepts as are being used to exploit new complexities. In the process, the reader becomes familiar with the rich cybernetic concept of information, as a basis for the information-based organization and to master big data. With that complex decision-making is clarified and a fresh understanding of the core function of the organization, coordination, is offered for those who want to solve the issue of self-coordination. The book provides working examples but even more the strongest tool to master and to reduce complexity: a deeper and broader understanding of what is going on beneath the surface of what we experience daily. This book builds on Herbert Simon’s hypothesis of simplicity: ‘to use the simplicity of process to deal with the complexity of state.’
Computable Foundations for Economics is a unified collection of essays, some of which are published here for the first time and all of which have been updated for this book, on an approach to economic theory from the point of view of algorithmic mathematics. By algorithmic mathematics the author means computability theory and constructive mathematics. This is in contrast to orthodox mathematical economics and game theory, which are formalised with the mathematics of real analysis, underpinned by what is called the ZFC formalism, i.e., set theory with the axiom of choice. This reliance on ordinary real analysis and the ZFC system makes economic theory in its current mathematical mode completely non-algorithmic, which means it is numerically meaningless. The book provides a systematic attempt to dissect and expose the non-algorithmic content of orthodox mathematical economics and game theory and suggests a reformalization on the basis of a strictly rigorous algorithmic mathematics. This removes the current schizophrenia in mathematical economics and game theory, where theory is entirely divorced from algorithmic applicability – for experimental and computational exercises. The chapters demonstrate the uncomputability and non-constructivity of core areas of general equilibrium theory, game theory and recursive macroeconomics. The book also provides a fresh look at the kind of behavioural economics that lies behind Herbert Simon’s work, and resurrects a role for the noble classical traditions of induction and verification, viewed and formalised, now, algorithmically. It will therefore be of particular interest to postgraduate students and researchers in algorithmic economics, game theory and classical behavioural economics.
