000			03275cam a2200301Ii 4500
001			on1028523969
003			OCoLC
007			ta
008			180311s2018 nyua b 001 0 eng
010			_a 2018942802
020			_a9780465094622 (hardback)
020			_a0465094627 (hardback)
035			_a(OCoLC)1028523969
050			_aQA76.9.I52 _bP34 2018
100	1		_aPage, Scott E.
245	1	4	_aThe model thinker : _bwhat you need to know to make data work for you / _cScott E. Page.
250			_a1st ed.
260			_aNew York : _bBasic Books, _c2018.
300			_axiii, 427 p. : _bill.
504			_aIncludes bibliographical references (pages 383-409) and index.
505	0		_aThe many-model thinker -- Why model? -- The science of many models -- Modeling human actors -- Normal distributions : the bell curve -- Power-law distributions : long tails -- Linear models -- Concavity and convexity -- Models of value and power -- Network models -- Broadcast, diffusion, and contagion -- Entropy : modeling uncertainty -- Random walks -- Path dependence -- Local interaction models -- Lyapunov functions and equilibria -- Markov models -- Systems dynamics models -- Threshold models with feedbacks -- Spatial and hedonic choice -- Game theory models times three -- Models of cooperation -- Collective action problems -- Mechanism design -- Signaling models -- Models of learning -- Multi-armed bandit problems -- Rugged-landscape models -- Opioids, inequality, and humility.
520			_a"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."--Publisher's description.
650		4	_aInformation visualization.
650		4	_aSocial systems _xMathematical models.
650		4	_aSocial sciences _xMathematical models.
650		4	_aComplexity (Philosophy)
942			_2lcc _cBK
999			_c2588 _d2588