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标签:神经经济学
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Neuroeconomics
Neuroeconomics is a new highly promising approach to understanding the neurobiology of decision making and how it affects cognitive social interactions between humans and societies/economies. This book is the first edited reference to examine the science behind neuroeconomics, including how it influences human behavior and societal decision making from a behavioral economics point of view. Presenting a truly interdisciplinary approach, Neuroeconomics presents research from neuroscience, psychology, and behavioral economics, and includes chapters by all the major figures in the field, including two Economics nobel laureates. Carefully edited for a cohesive presentation of the material, the book is also a great textbook to be used in the many newly emerging graduate courses on Neuroeconomics in Neuroscience, Psychology, and Economics graduate schools. This groundbreaking work is sure to become the standard reference source for this growing area of research. * Editors and contributing authors represent the acknowledged experts and founders of the field of Neuroeconomics and include Nobel laureates Vernon Smith and Daniel Kahneman, making this the authoritative reference for the field * Presents an interdisciplinary view of the approaches, concepts, and results of the emerging field of neuroeconomics relevant for anyone interested in this area or research * Full color presentation throughout with carefully selected illustrations to highlight key concepts -
决策、不确定性和大脑
《决策、不确定性和大脑:神经经济学》内容简介:这是一部充满挑战性的著作。在书中,保罗·格莱姆齐指出,在对大脑和行为的研究中,经济学理论有可能取代经典的笛卡儿模型。勒内·笛卡儿(1596-1650)相信,所有的行为都可被归入简单行为与复杂行为两类。简单行为是指,在某些行为中,一个给定的感觉事件必然会引起一·项适当的运动反应。而复杂行为则意味着,在某些行为中刺激与反应的关系不可预测。所有这些行为都是笛卡儿所称为的心灵过程的产物,但现代科学家却可能把它称作认知或意志。 格莱姆齐认为,笛卡儿的二了己论是从一个错误的前提假设出发的,即在动物牛存的真实世界中,所有行为都可用反射予以描述。他宣称,一个富含数学形式的认知理论,可以解决任何环境中存在的最困难的问题;通过消除对反射理论的依赖,人们就可以消除对二元论的依赖。他进一步解释道,人们可基于微观经济理论,来将感觉和行动之间的神经过程用严格的数学形式描述出来。经济理论使得生理学家们能够确定动物可以选择的最优行为序列以及得出最优解的数学路径。格莱姆齐描绘r基于经济学的认知模型的概貌以及应该如何对其进行经验检验。沿着这一思路,他介绍了神经科学令人神往的发展历史。同时他还讨论了关于确定性、自由意志以及复杂行为的随机特征等问题。
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