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1. |
Record Nr. |
UNINA9910456969803321 |
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Autore |
Alpaslan Can M (Can Murat) |
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Titolo |
Swans, swine, and swindlers [[electronic resource] ] : coping with the growing threat of mega-crises and mega-messes / / Can M. Alpaslan and Ian I. Mitroff |
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Pubbl/distr/stampa |
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Stanford, California, : Stanford Business Books, An Imprint of Stanford University Press, 2011 |
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ISBN |
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Descrizione fisica |
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1 online resource (232 p.) |
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Collana |
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High reliability and crisis management |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Crisis management |
Conflict management |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Contents; Preface; Part I: Uncovering Assumptions; 1. A Crisis Is Not What We Have Been Led to Believe; Every Crisis Is an Existential Crisis of Meaning; 2. What Is a Mess? The Fundamental Differences Between Exercises, Problems, and Messes; 3. All Crises Are Messes; 4. When Good Organizations Do Unwise, Immature, and Bad Things; 5. It's the Culture; Part II: Managing Assumptions; 6. Overcoming Mega-Denial; 7. Beyond Fear-Based Crisis Management; Part III: Applications; 8. The Art and Science of Messy Inquiry |
9. Trust, Transparency, and Reliability: What Can the HROs Teach the Financial Sector?Afterword; Notes; Index |
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Sommario/riassunto |
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Swans, Swine, and Swindlers addresses a core, contemporary question: What steps can we take to better anticipate and manage mega-crises, such as Haiti, Katrina, and 9/11?This book explores the concept of ""messes."" A mess is a web of complex and dynamically interacting, ill-defined, and/or wicked problems; their solutions; and our conscious and unconscious assumptions, beliefs, emotions, and values. The roots of messes can be classified as Swans (the inability to surface and test false assumptions and mistaken beliefs), Swine (the inability to confront and m |
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2. |
Record Nr. |
UNISA996418252503316 |
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Autore |
Dayal Vikram |
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Titolo |
Quantitative Economics with R [[electronic resource] ] : A Data Science Approach / / by Vikram Dayal |
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Pubbl/distr/stampa |
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Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (XV, 326 p. 300 illus., 89 illus. in color.) |
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Disciplina |
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Soggetti |
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Game theory |
Economic theory |
Statistics |
Computer simulation |
Sociology—Research |
R (Computer program language) |
Game Theory, Economics, Social and Behav. Sciences |
Economic Theory/Quantitative Economics/Mathematical Methods |
Statistics for Business, Management, Economics, Finance, Insurance |
Simulation and Modeling |
Research Methodology |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Ch 1 Introduction -- Ch 2 R and RStudio -- Ch 3 Getting data into R -- Ch 4 Wrangling and graphing data -- Ch 5 Functions -- Ch 6 Matrices -- Ch 7 Probability and statistical inference -- Ch 8 Causal inference -- Ch 9 Solow model and basic facts of growth -- Ch 10 Causal inference for growth -- Ch 11 Graphing and simulating basic time series -- Ch 12 Simple examples: forecasting and causal inference -- Ch 13 Generalized additive models -- Ch 14 Tree models. |
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Sommario/riassunto |
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This book provides a contemporary treatment of quantitative economics, with a focus on data science. The book introduces the reader to R and RStudio, and uses expert Hadley Wickham’s tidyverse package for different parts of the data analysis workflow. After a gentle introduction to R code, the reader’s R skills are gradually honed, with |
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the help of “your turn” exercises. At the heart of data science is data, and the book equips the reader to import and wrangle data, (including network data). Very early on, the reader will begin using the popular ggplot2 package for visualizing data, even making basic maps. The use of R in understanding functions, simulating difference equations, and carrying out matrix operations is also covered. The book uses Monte Carlo simulation to understand probability and statistical inference, and the bootstrapis introduced. Causal inference is illuminated using simulation, data graphs, and R code for applications with real economic examples, covering experiments, matching, regression discontinuity, difference-in-difference, and instrumental variables. The interplay of growth related data and models is presented, before the book introduces the reader to time series data analysis with graphs, simulation, and examples. Lastly, two computationally intensive methods—generalized additive models and random forests (an important and versatile machine learning method)—are introduced intuitively with applications. The book will be of great interest to economists—students, teachers, and researchers alike—who want to learn R. It will help economics students gain an intuitive appreciation of appliedeconomics and enjoy engaging with the material actively, while also equipping them with key data science skills. |
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