MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02172cam a22002057i 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
180427t20182018nyua b 001 0 eng |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
0465094627 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781541618411 |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
001.4226 |
| Item number |
P2M6 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Page, Scott E |
| 9 (RLIN) |
341647 |
| 245 14 - TITLE STATEMENT |
|---|
| Title |
The model thinker: what you need to know to make data work for you |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
New York |
| Name of publisher, distributor, etc. |
Basic Books |
| Date of publication, distribution, etc. |
2018 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xiii, 427 p. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
"We confront no end of complex problems: why is inequality on the rise? Why are more and more Americans clinically obese? Does a racially diverse team make better decisions? How can we predict the outcomes of elections? At the same time, we find ourselves awash in data, be it on the opioid crisis, college admissions, genetic correlates of disease, financial transactions, or athletic performance. To confront such complexity and put that data to use, we need models: we can use linear regression to predict sales growth, or a power-law distribution to explain city sizes and book sales. Although each model offers insight, any single model will be wrong--just ask the physicist who, trying to understand barnyard animals, imagined a spherical cow. We must be able to do better. The question is simply how. In [this book], Scott E. Page gives us the answer: many-model thinking. By applying multiple diverse frameworks, we can achieve greater insights--indeed, using many models enables us to scale a hierarchy encompassing data, information, knowledge, and ultimately wisdom. Underpinning this, Page presents twenty-five broad classes of models--including models of growth, random walks, entropy, Markov chains, and many more--in a user-friendly and highly readable format, while teaching us how and when to apply them. Whether you work in science, business, government, or even literary studies, you confront complex problems, and you have more data than ever before. The Model Thinker will show how models can make that data work for you."-- |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Information visualization |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Social systems |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Social sciences - Mathematical models |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Complexity (Philosophy) |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Book |
