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Sparsity: A Tractable Approach to Bounded Rationality, Applied to Basic Consumer Theory, Equilibrium Theory, and Dynamic Programming

Sparsity: A Tractable Approach to Bounded Rationality, Applied to Basic Consumer Theory, Equilibrium Theory, and Dynamic Programming
稀疏性:一种易于处理的有限理性方法,应用于基本消费者理论、均衡理论和动态规划
批准号:
1325181
负责人:
Xavier Gabaix
金额:
$27.73万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2013
资助国家:
美国
项目状态:
已结题
起止时间:
2013-09-01 至 2016-08-31

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中文摘要
翻译
该奖项资助经济理论研究,开发考虑有限理性的经济决策模型。 PI将用“稀疏最大化”的替代假设取代标准经济学中通常假设的最大化行为。 这导致了一个易于计算的模型,同时反映了控制有限注意力的基本心理力量。 然后,PI继续在各种微观经济模型中使用这个起始假设。 这包括推导需求曲线,确定简单交换经济的结果,并分析动态选择,如整个生命周期的消费。具体来说,PI提出了一个稀疏的最大运算,它推广了经济学中使用的传统最大运算。 该代理建立一个简化的模型的世界是稀疏的,只考虑一阶重要性的变量。 她程式化的心智模型和由此产生的选择都来自约束优化。 该框架产生了许多经济学支柱的行为版本,包括消费者需求和竞争均衡的基本理论。 spare dynamic programming是传统动态规划的行为版本,spare dynamic programming将sparse max扩展到动态环境,该框架允许探索一些具体问题,例如对利率等变量的忽视,近视,投资组合选择的惯性,以及对罕见事件的忽视。 该框架为我们提供了一种新的方法来评估基础经济学的哪些部分对完美最大化假设是稳健的。该项目通过开发一种新方法来帮助政策制定者更好地预测他们的行动对经济的影响,从而为更广泛的社会目标做出贡献。
英文摘要
This award funds research in economic theory that develops a model of economic decisions that takes into account bounded rationality. The PI will replace the usual maximizing behavior assumed in standard economics with an alternative assumption of "sparse maximizing". This results in a model that is tractable to compute while reflecting basic psychological forces governing limited attention. The PI then goes on to use this starting assumption in a variety of microeconomic models. This includes deriving demand curves, determining the outcome of simple exchange economies, and analyzing dynamic choices such as consumption over the life cycle.Specifically, the PI proposes a sparse max operation which generalizes the traditional max operator used in economics. The agent builds a simplified model of the world which is sparse, considering only the variables of first-order importance. Her stylized mental model and her resulting choices both derive from constrained optimization. The framework yields a behavioral version of many pillars of economics, including the basic theory of consumer demand and competitive equilibrium. The sparse max extends to dynamic contexts via spare dynamic programming, a behavioral version of traditional dynamic programming.The framework allows for the exploration of a number of concrete issues, such as inattention to some variables like the interest rate, myopia, inertia in portfolio choice, and inattention to rare events. The framework gives us a new way to assess which parts of basic economics are robust to the assumption of perfect maximization.The project contributes to broader societal goals by developing a new approach that may help policymakers better predict the effects of their actions on the economy.
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