A comprehensive textbook on the tools of mathematical sociology and their applications Mathematical models and computer simulations of complex social systems have become everyday tools in sociology. Yet until now, students had no up-to-date textbook from which to learn these techniques. Introduction to Mathematical Sociology fills this gap, providing undergraduates with a comprehensive, self-contained primer on the mathematical tools and applications that sociologists use to understand social behavior. Phillip Bonacich and Philip Lu cover all the essential mathematics, including linear algebra, graph theory, set theory, game theory, and probability. They show how to apply these mathematical tools to demography; patterns of power, influence, and friendship in social networks; Markov chains; the evolution and stability of cooperation in human groups; chaotic and complex systems; and more. Introduction to Mathematical Sociology also features numerous exercises throughout, and is accompanied by easy-to-use Mathematica-based computer simulations that students can use to examine the effects of changing parameters on model behavior. Provides an up-to-date and self-contained introduction to mathematical sociology Explains essential mathematical tools and their applications Includes numerous exercises throughout Features easy-to-use computer simulations to help students master concepts

Until the 1960s, maths was studied as an academic subject in a desire to have more mathematicians. The current trend, however, has moved away from viewing maths as a purely intellectual endeavour and towards developing a more mathematically competent workforce and citizenry. This trend has seen a large increase in the number of maths schemes being produced by the major educational publishers, which attempt to make maths easier and more approachable by using language instead of symbols. So why do so many children still fail at maths? The author contends that to understand this, teachers need to analyze and evaluate the maths textbooks they are currently using. The author shows the reader how to systematically analyze and evaluate these textbooks. This interrogation of classroom resources, should have important implications for teaching strategies and for textbook design and use.

First Published in 1984. Routledge is an imprint of Taylor & Francis, an informa company.

This book offers a unique analysis of how ideas about science and technology in the public and scientific imaginations (in particular about maths, logic, the gene, the brain, god, and robots) perpetuate the false reality that values and politics are separate from scientific knowledge and its applications. These ideas are reinforced by cultural myths about free will and individualism. Restivo makes a compelling case for a synchronistic approach in the study of these notoriously 'hard' cases, arguing that their significance reaches far beyond the realms of science and technology, and that their sociological and political ramifications are of paramount importance in our global society. This innovative work deals with perennial problems in the social sciences, philosophy, and the history of science and religion, and will be of special interest to professionals in these fields, as well as scholars of science and technology studies.

Quantitative Sociology: International Perspective on Mathematical and Statistical Modeling presents diverse mathematical modeling procedures involving different strategies for understanding sociology. This book is organized into three parts encompassing 22 chapters that also describe meta-mathematical models suggesting general ways of conceptualizing or expressing phenomena in mathematical or logical languages. Part I deals with the diachronic process analysis, causation of conditional probabilities, and graph-theoretical formulations. Part II highlights the different fields of applied statistics, including experimental designs, survey sampling and panel designs, multivariate analysis, econometrics, multiple classification analysis, and other approaches to data analysis and measurement. This part also treats the elimination of distortions or artifacts of various kinds, such as sampling errors or biases stemming from faulty designs, measurement errors, or incorrectly specified equations. Part III explores other mathematical models for a deductive or semideductive system containing axioms, definitions, and theorems that may then be examined both in terms of internal consistency using mathematical reasoning and their ability to explain real-world phenomena. This book is of value to sociologists, applied and statistical mathematicians, and researchers.