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Найти книгу:      Навигация           Вход на сайт  Регистрация Логин: Пароль (Забыли?):  Чужой компьютер     Реклама             Название: Probability Models for Economic Decisions, 2nd edition
Автор: Roger B. Myerson, Eduardo Zambrano
Издательство: The MIT Press
Год: 2019
Страниц: 568
Язык: английский
Формат: epub
Размер: 116.1 MB

An introduction to the use of probability models for analyzing risk and economic decisions, using spreadsheets to represent and simulate uncertainty. This textbook offers an introduction to the use of probability models for analyzing risks and economic decisions. It takes a learn-by-doing approach, teaching the student to use spreadsheets to represent and simulate uncertainty and to analyze the effect of such uncertainty on an economic decision. Students in applied business and economics can more easily grasp difficult analytical methods with Excel spreadsheets.

All the analytical work in this book is done in Microsoft Excel spreadsheets, because the spreadsheet medium allows students to handle much more complex problems than they could handle with traditional ways of representing mathematical formulas. As a result of this emphasis on spreadsheet modeling, many readers may also learn sophisticated spreadsheet skills from this book.

The most important advantage of teaching spreadsheets is that they make multivariable models much easier for students to visually understand. Most students lose the big picture when they are asked to think about more than two variables that are represented by letters like “x” and “y” on a blackboard, but the same students can visually understand a spreadsheet model with many variables that are represented as cells on a spreadsheet page. Working in spreadsheets brings down the barrier that prevents most students from becoming sophisticated quantitative modelers. Throughout this book, we have tried to show how the methods of probability analysis can be applied to examples of a realistic complexity.

The book covers the basic ideas of probability, how to simulate random variables, and how to compute conditional probabilities via Monte Carlo simulation. The first four chapters use a large collection of probability distributions to simulate a range of problems involving worker efficiency, market entry, oil exploration, repeated investment, and subjective belief elicitation. The book then covers correlation and multivariate normal random variables; conditional expectation; optimization of decision variables, with discussions of the strategic value of information, decision trees, game theory, and adverse selection; risk sharing and finance; dynamic models of growth; dynamic models of arrivals; and model risk.

New material in this second edition includes two new chapters on additional dynamic models and model risk; new sections in every chapter; many new end-of-chapter exercises; and coverage of such topics as simulation model workflow, models of probabilistic electoral forecasting, and real options. The book comes equipped with Simtools, an open-source, free software used througout the book, which allows students to conduct Monte Carlo simulations seamlessly in Excel.

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