Books in the Wiley Series in Computational and Quantitative Social Science series

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.

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.

This book illustrates how analytical sociology is progressively refining its theoretical framework and how powerful this framework is in explaining a large array of social phenomena.

Geo-mathematical modelling: models from complexity scienceSir Alan Wilson, Centre for Advanced Spatial Analysis, University College LondonMathematical and computer models for a complexity science tool kitGeographical 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 situationsThis 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.

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 graphs* A compelling argument that observational and experimental methods are not qualitatively superior to simulation-based methods in their ability to establish causal claims* Practical 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.

A comprehensive, sweeping work by an acclaimed team of authors at the forefront of this hot topic, Understanding Large Temporal Networks and Spatial Networks explores the different approaches to studying large temporal and spatial networks and links them to computationally sound models of changing structure to detect patterns.

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.

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.

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 blockmodeling* Provides an excellent mix of mathematical rigor and practical application in a comprehensive manner* Presents a suite of new methods, procedures, algorithms for partitioning networks, as well as new techniques for visualizing matrix arrays* Features numerous examples throughout, enabling readers to gain a better understanding of research methods and to conduct their own research effectively* Written 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.

This book introduces readers to new ways of thinking about how to look at social statistics, particularly those about people in places.