"Will delight and inform anyone who enjoys rigorous thinking and the unexpected conclusions it delivers." -- Jamie Whyte, author of Crimes Against Logic "This ingenious gallop through the mind of Steven Landsburg enables you to think like an economist without incurring a Keynesian headache or a huge student loan. You may even end up 'smarter than Google.' But you will not readily put down this exhilarating adventure in ideas." -- George Gilder, author of Knowledge and Power and Life After Google Can you outsmart an economist? Steven Landsburg, acclaimed author and professor of economics, dares you to try. In this whip-smart, entertaining, and entirely unconventional economics primer, he brings together over one hundred puzzles and brain teasers that illustrate the subject's key concepts and pitfalls. From warm-up exercises to get your brain working, to logic and probability problems, to puzzles covering more complex topics like inferences, strategy, and irrationality, Can You Outsmart an Economist? will show you how to do just that by expanding the way you think about decision making and problem solving. Let the games begin "Entertaining as well as edifying. Read it, expand your mind, and have fun "-- N. Gregory Mankiw, Robert M. Beren Professor of Economics, Harvard University

The Handbook of Mathematical Economics aims to provide a definitive source, reference, and teaching supplement for the field of mathematical economics. It surveys, as of the late 1970's the state of the art of mathematical economics. This is a constantly developing field and all authors were invited to review and to appraise the current status and recent developments in their presentations. In addition to its use as a reference, it is intended that this Handbook will assist researchers and students working in one branch of mathematical economics to become acquainted with other branches of this field. Volume 2 elaborates on Mathematical Approaches to Microeconomic Theory , including consumer, producer, oligopoly, and duality theory, as well as Mathematical Approaches to Competitive Equilibrium including such aspects of competitive equilibrium as existence, stability, uncertainty, the computation of equilibrium prices, and the core of an economy. For more information on the Handbooks in Economics series, please see our home page on http://www.elsevier.nl/locate/hes

This is the first volume of the Handbook of Game Theory with Economic Applications, to be followed by two additional volumes. Game Theory has developed greatly in the last decade, and today it is an essential tool in much of economic theory. The three volumes will cover the fundamental theoretical aspects, a wide range of applications to economics, several chapters on applications to political science, and individual chapters on relations with other disciplines. The topics covered in the present volume include chess-playing computers, an introduction to the non-cooperative theory, repeated games, bargaining theory, auctions, location, entry deterrence, patents, the cooperative theory and its applications, and the relation between Game Theory and ethics. For more information on the Handbooks in Economics series, please see our home page on http://www.elsevier.nl/locate/hes

Gini's mean difference (GMD) was first introduced by Corrado Gini in 1912 as an alternative measure of variability. GMD and the parameters which are derived from it (such as the Gini coefficient or the concentration ratio) have been in use in the area of income distribution for almost a century. In practice, the use of GMD as a measure of variability is justified whenever the investigator is not ready to impose, without questioning, the convenient world of normality. This makes the GMD of critical importance in the complex research of statisticians, economists, econometricians, and policy makers. This book focuses on imitating analyses that are based on variance by replacing variance with the GMD and its variants. In this way, the text showcases how almost everything that can be done with the variance as a measure of variability, can be replicated by using Gini. Beyond this, there are marked benefits to utilizing Gini as opposed to other methods. One of the advantages of using Gini methodology is that it provides a unified system that enables the user to learn about various aspects of the underlying distribution. It also provides a systematic method and a unified terminology. Using Gini methodology can reduce the risk of imposing assumptions that are not supported by the data on the model. With these benefits in mind the text uses the covariance-based approach, though applications to other approaches are mentioned as well.