What is a model? How do you construct one? What are some common models in the social sciences? How can models be applied in new situations? What makes a model good? Focusing on answers to these and related questions, this multidisciplinary introduction to model building in the social sciences formulates interesting problems that involve students in creative model building and the process of invention. The book describes models of individual choice, exchange, adaptation, and diffusion. Throughout, student participation in analytical thinking is encouraged. Originally published in 1975 by HarperCollins Publishers.

The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fields, including bioinformatics, communication theory, statistical physics, combinatorial optimization, signal and image processing, information retrieval and statistical machine learning. Many problems that arise in specific instances-including the key problems of computing marginals and modes of probability distributions-are best studied in the general setting. Working with exponential family representations, and exploiting the conjugate duality between the cumulant function and the entropy for exponential families, Graphical Models, Exponential Families and Variational Inference develops general variational representations of the problems of computing likelihoods, marginal probabilities and most probable configurations. It describes how a wide variety of algorithms- among them sum-product, cluster variational methods, expectation-propagation, mean field methods, and max-product-can all be understood in terms of exact or approximate forms of these variational representations. The variational approach provides a complementary alternative to Markov chain Monte Carlo as a general source of approximation methods for inference in large-scale statistical models.

This book explores the intersection between cognitive sciences and social sciences. In particular, it explores the intersection between individual cognitive modeling and modeling of multi-agent interaction (social stimulation). The two contributing fields - individual cognitive modeling (especially cognitive architectures) and modeling of multi-agent interaction (including social simulation and, to some extent, multi-agent systems) - have seen phenomenal growth in recent years. However, the interaction of these two fields has not been sufficiently developed. We believe that the interaction of the two may be more significant than either alone. They bring with them enormous intellectual capitals. These intellectual capitals can be profitably leveraged in creating true synergy between the two fields, leading to more in-depth studies and better understanding of both individual cognition and sociocultural processes. It is possible that an integrative field of study in cognitive and social sciences is emerging and we are laying the foundation for it.