A formal model in the social sciences builds explanations when it structures the reasoning underlying a theoretical argument, opens venues for controlled experimentation, and can lead to hypotheses. Yet more importantly, models evaluate theory, build theory, and enhance conjectures. Formal Modeling in Social Science addresses the varied helpful roles of formal models and goes further to take up more fundamental considerations of epistemology and methodology. The authors integrate the exposition of the epistemology and the methodology of modeling and argue that these two reinforce each other. They illustrate the process of designing an original model suited to the puzzle at hand, using multiple methods in diverse substantive areas of inquiry. The authors also emphasize the crucial, though underappreciated, role of a narrative in the progression from theory to model. Transparency of assumptions and steps in a model means that any analyst will reach equivalent predictions whenever she replicates the argument. Hence, models enable theoretical replication, essential in the accumulation of knowledge. Formal Modeling in Social Science speaks to scholars in different career stages and disciplines and with varying expertise in modeling.

Model building in the social sciences can increasingly rely on well elaborated formal theories. At the same time inexpensive large computational capacities are now available. Both make computer-based model building and simulation possible in social science, whose central aim is in particular an understanding of social dynamics. Such social dynamics refer to public opinion formation, partner choice, strategy decisions in social dilemma situations and much more. In the context of such modelling approaches, novel problems in philosophy of science arise which must be analysed - the main aim of this book. Interest in social simulation has recently been growing rapidly world- wide, mainly as a result of the increasing availability of powerful personal computers. The field has also been greatly influenced by developments in cellular automata theory (from mathematics) and in distributed artificial intelligence which provided tools readily applicable to social simulation. This book presents a number of modelling and simulation approaches and their relations to problems in philosophy of science. It addresses sociologists and other social scientists interested in formal modelling, mathematical sociology, and computer simulation as well as computer scientists interested in social science applications, and philosophers of social science.

An accessible treatment of important formal models of domestic politics, fully updated and now including a chapter on nondemocracy.

This essay was written following discussions with friends and colleagues from different disciplines about the use of mathematics in the social sciences. It sets out some of the advantages of mathematical modeling (MM) in the social sciences, and defends it against some of the common criticisms that I have encountered. The main argument is that theories of the social world necessarily involve abstractions and trade-offs between the detail of the analysis and its tractability. Mathematics is a way to make these trade-offs in the clearest possible way, and this clarity makes discussion and empirical testing easier. Not all MM is based on such Popperian arguments. Often, models have a more artistic aspiration, of providing a metaphor or caricature of some feature of reality in order to understand it better. Finally, like any research methodology, mathematical theorizing does have its limits and associated problems, which I will also briefly discuss.

Offers an overview of mathematical modeling concentrating on game theory, statistics and computational modeling.

In this introduction to computational modelling the authors provide a concise description of computational methods, including dynamic simulation, knowledge-based models and machine learning, as a single broad class of research tools.

The use of case studies to build and test theories in political science and the other social sciences has increased in recent years. Many scholars have argued that the social sciences rely too heavily on quantitative research and formal models and have attempted to develop and refine rigorous methods for using case studies. This text presents a comprehensive analysis of research methods using case studies and examines the place of case studies in social science methodology. It argues that case studies, statistical methods, and formal models are complementary rather than competitive. The book explains how to design case study research that will produce results useful to policymakers and emphasizes the importance of developing policy-relevant theories. It offers three major contributions to case study methodology: an emphasis on the importance of within-case analysis, a detailed discussion of process tracing, and development of the concept of typological theories. Case Studies and Theory Development in the Social Sciences will be particularly useful to graduate students and scholars in social science methodology and the philosophy of science, as well as to those designing new research projects, and will contribute greatly to the broader debate about scientific methods.

Causal models are formal theories stating the relationships between precisely defined variables, and have become an indispensable tool of the social scientist. This collection of articles is a course book on the causal modeling approach to theory construction and data analysis. H. M. Blalock, Jr. summarizes the then-current developments in causal model utilization in sociology, political science, economics, and other disciplines. This book provides a comprehensive multidisciplinary picture of the work on causal models. It seeks to address the problem of measurement in the social sciences and to link theory and research through the development of causal models. Organized into five sections (Simple Recursive Models, Path Analysis, Simultaneous Equations Techniques, The Causal Approach to Measurement Error, and Other Complications), this volume contains twenty-seven articles (eight of which were specially commissioned). Each section begins with an introduction explaining the concepts to be covered in the section and links them to the larger subject. It provides a general overview of the theory and application of causal modeling. Blalock argues for the development of theoretical models that can be operationalized and provide verifiable predictions. Many of the discussions of this subject that occur in other literature are too technical for most social scientists and other scholars who lack a strong background in mathematics. This book attempts to integrate a few of the less technical papers written by econometricians such as Koopmans, Wold, Strotz, and Fisher with discussions of causal approaches in the social and biological sciences. This classic text by Blalock is a valuable source of material for those interested in the issue of measurement in the social sciences and the construction of mathematical models.

Click ′Additional Materials′ to read the foreword by Jerald Hage As straightforward as its title, How to Build Social Science Theories sidesteps the well-traveled road of theoretical examination by demonstrating how new theories originate and how they are elaborated. Essential reading for students of social science research, this book traces theories from their most rudimentary building blocks (terminology and definitions) through multivariable theoretical statements, models, the role of creativity in theory building, and how theories are used and evaluated. Authors Pamela J. Shoemaker, James William Tankard, Jr., and Dominic L. Lasorsa intend to improve research in many areas of the social sciences by making research more theory-based and theory-oriented. The book begins with a discussion of concepts and their theoretical and operational definitions. It then proceeds to theoretical statements, including hypotheses, assumptions, and propositions. Theoretical statements need theoretical linkages and operational linkages; this discussion begins with bivariate relationships, as well as three-variable, four-variable, and further multivariate relationships. The authors also devote chapters to the creative component of theory-building and how to evaluate theories. How to Build Social Science Theories is a sophisticated yet readable analysis presented by internationally known experts in social science methodology. It is designed primarily as a core text for graduate and advanced undergraduate courses in communication theory. It will also be a perfect addition to any course dealing with theory and research methodology across the social sciences. Additionally, professional researchers will find it an indispensable guide to the genesis, dissemination, and evaluation of social science theories.