Wiley Series in Computational and Quantitative Social Science – serie

This book explores social mechanisms that drive network change and link them to computationally sound models of changing structure to detect patterns. This text identifies the social processes generating these networks and how networks have evolved.Reviews: "this book is easy to read and entertaining, and much can be learned from it. Even if you know just abouteverything about large-scale and temporal networks, the book is a worthwhile read; you will learn a lot about SNA literature, patents, the US Supreme Court, and European soccer." (Social Networks)"a clear and accessible textbook, balancing symbolic maths, code, and visual explanations. The authors’ enthusiasm for the subject matter makes it enjoyable to read" (JASSS)

Computational Approaches to Studying the Co-evolution of Networks and Behaviour in Social Dilemmas shows students, researchers, and professionals how to use computation methods, rather than mathematical analysis, to answer research questions for an easier, more productive method of testing their models. Illustrations of general methodology are provided and explore how computer simulation is used to bridge the gap between formal theoretical models and empirical applications.

This book is organized in two parts: the first part introduces the reader to all the concepts, tools and references that are required to start conducting research in behavioral computational social science. The methodological reasons for integrating the two approaches are also presented from the individual and separated viewpoints of the two approaches.The second part of the book, presents all the advanced methodological and technical aspects that are relevant for the proposed integration. Several contributions which effectively merge the computational and the behavioral approaches are presented and discussed throughout

Political Science has traditionally employed empirical research and analytical resources to understand, explain and predict political phenomena. One of the long-standing criticisms against empirical modeling targets the static perspective provided by the model-invariant paradigm. In political science research, this issue has a particular relevance since political phenomena prove sophisticated degrees of context-dependency whose complexity could be hardly captured by traditional approaches. To cope with the complexity challenge, a new modeling paradigm was needed. This book is concerned with this challenge. Moreover, the book aims to reveal the power of computational modeling of political attitudes to reinforce the political methodology in facing two fundamental challenges: political culture modeling and polity modeling. The book argues that an artificial polity model as a powerful research instrument could hardly be effective without the political attitude and, by extension, the political culture computational and simulation modeling theory, experiments and practice.This book: Summarizes the state of the art in computational modeling of political attitudes, with illustrations and examples featured throughout.Explores the different approaches to computational modeling and how the complexity requirements of political science should determine the direction of research and evaluation methods.Addresses the newly emerging discipline of computational political science.Discusses modeling paradigms, agent-based modeling and simulation, and complexity-based modeling.Discusses model classes in the fundamental areas of voting behavior and decision-making, collective action, ideology and partisanship, emergence of social uprisings and civil conflict, international relations, allocation of public resources, polity and institutional function, operation, development and reform, political attitude formation and change in democratic societies.This book is ideal for students who need a conceptual and operational description of the political attitude computational modeling phases, goals and outcomes in order to understand how political attitudes could be computationally modeled and simulated. Researchers, Governmental and international policy experts will also benefit from this book.

Geo-mathematical modelling: models from complexity science  Sir Alan Wilson, Centre for Advanced Spatial Analysis, University College London  Mathematical and computer models for a complexity science tool kit  Geographical systems are characterised by locations, activities at locations, interactions between them and the infrastructures that carry these activities and flows. They can be described at a great variety of scales, from individuals and organisations to countries. Our understanding, often partial, of these entities, and in many cases this understanding is represented in theories and associated mathematical models.  In this book, the main examples are models that represent elements of the global system covering such topics as trade, migration, security and development aid together with examples at finer scales. This provides an effective toolkit that can not only be applied to global systems, but more widely in the modelling of complex systems. All complex systems involve nonlinearities involving path dependence and the possibility of phase changes and this makes the mathematical aspects particularly interesting. It is through these mechanisms that new structures can be seen to ‘emerge’, and hence the current notion of ‘emergent behaviour’. The range of models demonstrated include account-based models and biproportional fitting, structural dynamics, space-time statistical analysis, real-time response models, Lotka-Volterra models representing ‘war’, agent-based models, epidemiology and reaction-diffusion approaches, game theory, network models and finally, integrated models.  Geo-mathematical modelling: Presents mathematical models with spatial dimensions.Provides representations of path dependence and phase changes.Illustrates complexity science using models of trade, migration, security and development aid.Demonstrates how generic models from the complexity science tool kit can each be applied in a variety of situations  This book is for practitioners and researchers in applied mathematics, geography, economics, and interdisciplinary fields such as regional science and complexity science. It can also be used as the basis of a modelling course for postgraduate students.

A world model: economies, trade, migration, security and development aid.This bookprovides the analytical capability to understand and explore the dynamics of globalisation. It is anchored in economic input-output models of over 200 countries and their relationships through trade, migration, security and development aid. The tools of complexity science are brought to bear and mathematical and computer models are developed both for the elements and for an integrated whole. Models are developed at a variety of scales ranging from the global and international trade through a European model of inter-sub-regional migration to piracy in the Gulf and the London riots of 2011. The models embrace the changing technology of international shipping, the impacts of migration on economic development along with changing patterns of military expenditure and development aid. A unique contribution is the level of spatial disaggregation which presents each of 200+ countries and their mutual interdependencies – along with some finer scale analyses of cities and regions.  This is the first global model which offers this depth of detail with fully work-out models, these provide tools for policy making at national, European and global scales.Global dynamics: Presents in depth models of global dynamics.Provides a world economic model of 200+ countries and their interactions through trade, migration, security and development aid.Provides pointers to the deployment of analytical capability through modelling in policy development.Features a variety of models that constitute a formidable toolkit for analysis and policy development.Offers a demonstration of the practicalities of complexity science concepts.This book is for practitioners and policy analysts as well as those interested in mathematical model building and complexity science as well as advanced undergraduate and postgraduate level students.

Uniquely reflects an engineering view to social systems in a wide variety of contexts of applicationSocial Systems Engineering: The Design of Complexity brings together a wide variety of application approaches to social systems from an engineering viewpoint. The book defines a social system as any complex system formed by human beings. Focus is given to the importance of systems intervention design for specific and singular settings, the possibilities of engineering thinking and methods, the use of computational models in particular contexts, and the development of portfolios of solutions. Furthermore, this book considers both technical, human and social perspectives, which are crucial to solving complex problems.Social Systems Engineering: The Design of Complexity provides modelling examples to explore the design aspect of social systems. Various applications are explored in a variety of areas, such as urban systems, health care systems, socio-economic systems, and environmental systems. It covers important topics such as organizational design, modelling and intervention in socio-economic systems, participatory and/or community-based modelling, application of systems engineering tools to social problems, applications of computational behavioral modeling, computational modelling and management of complexity, and more.  Highlights an engineering view to social systems (as opposed to a “scientific” view) that stresses the importance of systems intervention design for specific and singular settingsDivulges works where the design, re-design, and transformation of social systems constitute the main aim, and where joint considerations of both technical and social perspectives are deemed important in solving social problemsFeatures an array of applied cases that illustrate the application of social systems engineering in different domainsSocial Systems Engineering: The Design of Complexity is an excellent text for academics and graduate students in engineering and social science—specifically, economists, political scientists, anthropologists, and management scientists with an interest in finding systematic ways to intervene and improve social systems.

The only single-source guide to understanding, using, adapting, and designing state-of-the-art agent-based modelling of tax evasionA computational method for simulating the behavior of individuals or groups and their effects on an entire system, agent-based modeling has proven itself to be a powerful new tool for detecting tax fraud. While interdisciplinary groups and individuals working in the tax domain have published numerous articles in diverse peer-reviewed journals and have presented their findings at international conferences, until Agent-based Modelling of Tax Evasion there was no authoritative, single-source guide to state-of-the-art agent-based tax evasion modeling techniques and technologies.Featuring contributions from distinguished experts in the field from around the globe, Agent-Based Modelling of Tax Evasion provides in-depth coverage of an array of field tested agent-based tax evasion models. Models are presented in a unified format so as to enable readers to systematically work their way through the various modeling alternatives available to them. Three main components of each agent-based model are explored in accordance with the Overview, Design Concepts, and Details (ODD) protocol, each section of which contains several sub elements that help to illustrate the model clearly and that assist readers in replicating the modeling results described. Presents models in a unified and structured manner to provide a point of reference for readers interested in agent-based modelling of tax evasionExplores the theoretical aspects and diversity of agent-based modeling through the example of tax evasionProvides an overview of the characteristics of more than thirty agent-based tax evasion frameworksFunctions as a solid foundation for lectures and seminars on agent-based modelling of tax evasionThe only comprehensive treatment of agent-based tax evasion models and their applications, this book is an indispensable working resource for practitioners and tax evasion modelers both in the agent-based computational domain and using other methodologies. It is also an excellent pedagogical resource for teaching tax evasion modeling and/or agent-based modeling generally.

Provides an overview of the developments and advances in the field of network clustering and blockmodeling over the last 10 yearsThis book offers an integrated treatment of network clustering and blockmodeling, covering all of the newest approaches and methods that have been developed over the last decade. Presented in a comprehensive manner, it offers the foundations for understanding network structures and processes, and features a wide variety of new techniques addressing issues that occur during the partitioning of networks across multiple disciplines such as community detection, blockmodeling of valued networks, role assignment, and stochastic blockmodeling.Written by a team of international experts in the field, Advances in Network Clustering and Blockmodeling offers a plethora of diverse perspectives covering topics such as: bibliometric analyses of the network clustering literature; clustering approaches to networks; label propagation for clustering; and treating missing network data before partitioning. It also examines the partitioning of signed networks, multimode networks, and linked networks. A chapter on structured networks and coarsegrained descriptions is presented, along with another on scientific coauthorship networks. The book finishes with a section covering conclusions and directions for future work. In addition, the editors provide numerous tables, figures, case studies, examples, datasets, and more. Offers a clear and insightful look at the state of the art in network clustering and blockmodelingProvides an excellent mix of mathematical rigor and practical application in a comprehensive mannerPresents a suite of new methods, procedures, algorithms for partitioning networks, as well as new techniques for visualizing matrix arraysFeatures numerous examples throughout, enabling readers to gain a better understanding of research methods and to conduct their own research effectivelyWritten by leading contributors in the field of spatial networks analysisAdvances in Network Clustering and Blockmodeling is an ideal book for graduate and undergraduate students taking courses on network analysis or working with networks using real data. It will also benefit researchers and practitioners interested in network analysis.

Presented in a comprehensive manner, this book provides a comprehensive foundation in algebraic approaches for the analysis of different types of social networks such as multiple, signed, and affiliation networks. The study of such configurations corresponds to the structural analysis within the social sciences, and the methods applied for the analysis are in the areas of abstract algebra, combinatorics, and graph theory.Current research in social networks has moved toward the examination of more realistic but also more complex social relations by which agents or actors are connected in multiple ways. Addressing this trend, this book offers hands-on training of the algebraic procedures presented along with the computer package multiplex, written by the book’s author specifically to perform analyses of multiple social networks. An introductory section on both complex networks and for R will feature, however the subjects themselves correspond to advanced courses on social network analysis with the specialization on algebraic models and methods.

Agent-based Models and Causal InferenceScholars of causal inference have given little credence to the possibility that ABMs could be an important tool in warranting causal claims. Manzo’s book makes a convincing case that this is a mistake. The book starts by describing the impressive progress that ABMs have made as a credible methodology in the last several decades. It then goes on to compare the inferential threats to ABMs versus the traditional methods of RCTs, regression, and instrumental variables showing that they have a common vulnerability of being based on untestable assumptions. The book concludes by looking at four examples where an analysis based on ABMs complements and augments the evidence for specific causal claims provided by other methods. Manzo has done a most convincing job of showing that ABMs can be an important resource in any researcher’s tool kit.—Christopher Winship, Diker-Tishman Professor of Sociology, Harvard University, USAAgent-based Models and Causal Inference is a first-rate contribution to the debate on, and practice of, causal claims. With exemplary rigor, systematic precision and pedagogic clarity, this book contrasts the assumptions about causality that undergird agent-based models, experimental methods, and statistically based observational methods, discusses the challenges these methods face as far as inferences go, and, in light of this discussion, elaborates the case for combining these methods’ respective strengths: a remarkable achievement.—Ivan Ermakoff, Professor of Sociology, University of Wisconsin-Madison, USAAgent-based models are a uniquely powerful tool for understanding how patterns in society may arise in often surprising and counter-intuitive ways. This book offers a strong and deeply reflected argument for how ABM’s can do much more: add to actual empirical explanation. The work is of great value to all social scientists interested in learning how computational modelling can help unraveling the complexity of the real social world.—Andreas Flache, Professor of Sociology at the University of Groningen, NetherlandsAgent-based Models and Causal Inference is an important and much-needed contribution to sociology and computational social science. The book provides a rigorous new contribution to current understandings of the foundation of causal inference and justification in the social sciences. It provides a powerful and cogent alternative to standard statistical causal-modeling approaches to causation. Especially valuable is Manzo’s careful analysis of the conditions under which an agent-based simulation is relevant to causal inference. The book represents an exceptional contribution to sociology, the philosophy of social science, and the epistemology of simulations and models.—Daniel Little, Professor of philosophy, University of Michigan, USAAgent-based Models and Causal Inference delivers an insightful investigation into the conditions under which different quantitative methods can legitimately hold to be able to establish causal claims. The book compares agent-based computational methods with randomized experiments, instrumental variables, and various types of causal graphs.Organized in two parts, Agent-based Models and Causal Inference connects the literature from various fields, including causality, social mechanisms, statistical and experimental methods for causal inference, and agent-based computation models to help show that causality means different things within different methods for causal analysis, and that persuasive causal claims can only be built at the intersection of these various methods.Readers will also benefit from the inclusion of: A thorough comparison between agent-based computation models to randomized experiments, instrumental variables, and several types of causal graphsA compelling argument that observational and experimental methods are not qualitatively superior to simulation-based methods in their ability to establish causal claimsPractical discussions of how statistical, experimental and computational methods can be combined to produce reliable causal inferencesPerfect for academic social scientists and scholars in the fields of computational social science, philosophy, statistics, experimental design, and ecology, Agent-based Models and Causal Inference will also earn a place in the libraries of PhD students seeking a one-stop reference on the issue of causal inference in agent-based computational models.

Demonstrates the power of the theoretical framework of analytical sociology inexplaining a large array of social phenomenaAnalytical Sociology: Actions and Networks presents the most advanced theoretical discussion of analytical sociology, along with a unique set of examples on mechanism-based sociology. Leading scholars apply the theoretical principles of analytical sociologyto understand how puzzling social and historical phenomena including crime, lynching,witch-hunts, tax behaviours, Web-based social movement and communication,restaurant reputation, job search and careers, social network homophily and instability, cooperation and trust are brought about by complex, multi-layered social mechanisms.  The analyses presented in this book rely on a wide range of methods which include qualitative observations, advanced statistical techniques, complex network tools, refined simulation methods and creative experimental protocols.This book ultimately demonstrates that sociology, like any other science, is at its bestwhen it dissects the mechanisms at work by means of rigorous model building and testing.         Analytical Sociology: • Provides the most complete and up-to-date theoretical treatment of analytical sociology.• Looks at a wide range of complex social phenomena within a single and unitary theoretical framework.• Explores a variety of advanced methods to build and test theoretical models.• Examines how both computational modelling and experiments can be usedto study the complex relation between norms, networks and social actions.• Brings together research from leading global experts in the field in order topresent a unique set of examples on mechanism-based sociology. Advanced graduate students and researchers working in sociology, methodology of social sciences, statistics, social networks analysis and computer simulation will benefit from this book.