基于代理的计算经济学

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Agent-based computational economics (ACE) is the area of computational economics that studies economic processes, including whole economies, as dynamic systems of interacting agents. As such, it falls in the paradigm of complex adaptive systems.[1] In corresponding agent-based models, the "agents" are "computational objects modeled as interacting according to rules" over space and time, not real people. The rules are formulated to model behavior and social interactions based on incentives and information.[2] Such rules could also be the result of optimization, realized through use of AI methods (such as Q-learning and other reinforcement learning techniques).[3]

Agent-based computational economics (ACE) is the area of computational economics that studies economic processes, including whole economies, as dynamic systems of interacting agents. As such, it falls in the paradigm of complex adaptive systems. In corresponding agent-based models, the "agents" are "computational objects modeled as interacting according to rules" over space and time, not real people. The rules are formulated to model behavior and social interactions based on incentives and information. Such rules could also be the result of optimization, realized through use of AI methods (such as Q-learning and other reinforcement learning techniques).

基于代理的计算经济学研究是计算经济学的一个领域,研究经济过程,包括整个经济,作为动态系统的相互作用的代理。因此,它属于复杂适应系统的范式。在相应的基于 agent 的模型中,“ agent”是在空间和时间上“根据规则进行交互的计算对象” ,而不是真实的人。这些规则是基于激励和信息为行为和社会互动建模而制定的。这些规则也可能是优化的结果,通过人工智能方法(如 q 学习和其他强化学习技术)来实现。


The theoretical assumption of mathematical optimization by agents in equilibrium is replaced by the less restrictive postulate of agents with bounded rationality adapting to market forces.[4] ACE models apply numerical methods of analysis to computer-based simulations of complex dynamic problems for which more conventional methods, such as theorem formulation, may not find ready use.[5] Starting from initial conditions specified by the modeler, the computational economy evolves over time as its constituent agents repeatedly interact with each other, including learning from interactions. In these respects, ACE has been characterized as a bottom-up culture-dish approach to the study of economic systems.[6]

ACE has a similarity to, and overlap with, game theory as an agent-based method for modeling social interactions. But practitioners have also noted differences from standard methods, for example in ACE events modeled being driven solely by initial conditions, whether or not equilibria exist or are computationally tractable, and in the modeling facilitation of agent autonomy and learning.

ACE 与博弈论有相似之处,也有重叠之处,博弈论是一种基于 agent 的社会互动建模方法。但是实践者也注意到了标准方法的不同,例如在 ACE 事件模型中仅仅由初始条件驱动,不管均衡是否存在或者是否在计算上易于处理,以及在主体自主性和学习的建模方面。


ACE has a similarity to, and overlap with, game theory as an agent-based method for modeling social interactions.[7] But practitioners have also noted differences from standard methods, for example in ACE events modeled being driven solely by initial conditions, whether or not equilibria exist or are computationally tractable, and in the modeling facilitation of agent autonomy and learning.[8]


The method has benefited from continuing improvements in modeling techniques of computer science and increased computer capabilities. The ultimate scientific objective of the method is to "test theoretical findings against real-world data in ways that permit empirically supported theories to cumulate over time, with each researcher’s work building appropriately on the work that has gone before."[9] The subject has been applied to research areas like asset pricing,[10] competition and collaboration,[11] transaction costs,[12] market structure and industrial organization and dynamics,[13] welfare economics,[14] and mechanism design,[15] information and uncertainty,[16] macroeconomics,[17] and Marxist economics.[18][19]

ACE is an officially designated special interest group (SIG) of the Society for Computational Economics. Researchers at the Santa Fe Institute have contributed to the development of ACE.

ACE 是美国计算经济学协会的一个官方指定的特殊利益集团。圣菲研究所的研究人员为 ACE 的发展做出了贡献。


Overview

The "agents" in ACE models can represent individuals (e.g. people), social groupings (e.g. firms), biological entities (e.g. growing crops), and/or physical systems (e.g. transport systems). The ACE modeler provides the initial configuration of a computational economic system comprising multiple interacting agents. The modeler then steps back to observe the development of the system over time without further intervention. In particular, system events should be driven by agent interactions without external imposition of equilibrium conditions.[20] Issues include those common to experimental economics in general[21] and development of a common framework for empirical validation and resolving open questions in agent-based modeling.[22]

One area where ACE methodology has frequently been applied is asset pricing. W. Brian Arthur, Eric Baum, William Brock, Cars Hommes, and Blake LeBaron, among others, have developed computational models in which many agents choose from a set of possible forecasting strategies in order to predict stock prices, which affects their asset demands and thus affects stock prices. These models assume that agents are more likely to choose forecasting strategies which have recently been successful. The success of any strategy will depend on market conditions and also on the set of strategies that are currently being used. These models frequently find that large booms and busts in asset prices may occur as agents switch across forecasting strategies. More recently, Brock, Hommes, and Wagener (2009) have used a model of this type to argue that the introduction of new hedging instruments may destabilize the market, and some papers have suggested that ACE might be a useful methodology for understanding the recent financial crisis.

ACE 方法经常应用的一个领域是资产定价。布莱恩 · 阿瑟,埃里克 · 鲍姆,威廉 · 布洛克,汽车人,布莱克 · 勒巴伦等人已经开发出计算模型,其中许多代理人选择从一组可能的预测策略,以预测股票价格,这影响到他们的资产需求,从而影响股票价格。这些模型假设代理人更有可能选择最近成功的预测策略。任何战略的成功将取决于市场条件,也取决于目前正在使用的一套战略。这些模型经常发现,随着经纪人转换预测策略,资产价格可能会出现大起大落。最近,Brock、 Hommes 和 Wagener (2009)使用了这类模型,认为引入新的对冲工具可能会破坏市场稳定,一些论文提出,ACE 可能是理解最近金融危机的有用方法。


ACE is an officially designated special interest group (SIG) of the Society for Computational Economics.[23] Researchers at the Santa Fe Institute have contributed to the development of ACE.


Example: finance

One area where ACE methodology has frequently been applied is asset pricing. W. Brian Arthur, Eric Baum, William Brock, Cars Hommes, and Blake LeBaron, among others, have developed computational models in which many agents choose from a set of possible forecasting strategies in order to predict stock prices, which affects their asset demands and thus affects stock prices. These models assume that agents are more likely to choose forecasting strategies which have recently been successful. The success of any strategy will depend on market conditions and also on the set of strategies that are currently being used. These models frequently find that large booms and busts in asset prices may occur as agents switch across forecasting strategies.[10][24][25] More recently, Brock, Hommes, and Wagener (2009) have used a model of this type to argue that the introduction of new hedging instruments may destabilize the market,[26] and some papers have suggested that ACE might be a useful methodology for understanding the recent financial crisis.[27][28][29]


See also

Category:Computational economics

类别: 计算经济学


Category:Monte Carlo methods in finance

类别: 金融中的蒙特卡罗方法

References

Category:Computational fields of study

类别: 研究的计算领域


This page was moved from wikipedia:en:Agent-based computational economics. Its edit history can be viewed at 基于代理的计算经济学/edithistory

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       • Leigh Tesfatsion, 2003. "Agent-based Computational Economics: Modeling Economies as Complex Adaptive Systems," Information Sciences, 149(4), pp. 262-268 -{zh-cn:互联网档案馆; zh-tw:網際網路檔案館; zh-hk:互聯網檔案館;}-存檔,存档日期26 April 2012..
  2. Scott E. Page (2008). "agent-based models," The New Palgrave Dictionary of Economics, 2nd Edition. Abstract.
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       • The theoretical assumption of mathematical optimization by agents in equilibrium is replaced by the less restrictive postulate of agents with bounded rationality adapting to market forces. ACE models apply numerical methods of analysis to computer-based simulations of complex dynamic problems for which more conventional methods, such as theorem formulation, may not find ready use. Starting from initial conditions specified by the modeler, the computational economy evolves over time as its constituent agents repeatedly interact with each other, including learning from interactions. In these respects, ACE has been characterized as a bottom-up culture-dish approach to the study of economic systems. 代理人均衡最优化的理论假设被代理人适应市场力量的限制较少的假设所取代。ACE 模型将数值分析方法应用于基于计算机的复杂动力学问题的模拟,对于这些问题,更传统的方法,如定理公式,可能找不到现成的用途。从建模者指定的初始条件开始,计算经济随着时间的推移而发展,因为它的组成代理不断地相互交互,包括从交互中学习。在这些方面,ACE 被认为是研究经济体系的一种自下而上的文化方法。 Thomas J. Sargent, 1994. Bounded Rationality in Macroeconomics, Oxford. Description and chapter-preview 1st-page links.
  5. • Kenneth L. Judd, 2006. "Computationally Intensive Analyses in Economics," Handbook of Computational Economics, v. 2, ch. 17, Introduction, p. 883. [Pp. 881- 893. Pre-pub PDF.
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  6. • Leigh Tesfatsion (2002). "Agent-Based Computational Economics: Growing Economies from the Bottom Up," Artificial Life, 8(1), pp.55-82. Abstract and pre-pub PDF -{zh-cn:互联网档案馆; zh-tw:網際網路檔案館; zh-hk:互聯網檔案館;}-存檔,存档日期14 May 2013..
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       • Yoav Shoham (2008). "Computer Science and Game Theory," Communications of the ACM, 51(8), pp. The method has benefited from continuing improvements in modeling techniques of computer science and increased computer capabilities. The ultimate scientific objective of the method is to "test theoretical findings against real-world data in ways that permit empirically supported theories to cumulate over time, with each researcher’s work building appropriately on the work that has gone before." The subject has been applied to research areas like asset pricing, competition and collaboration, transaction costs, market structure and industrial organization and dynamics, welfare economics, and mechanism design, information and uncertainty, macroeconomics, and Marxist economics. 这种方法得益于计算机科学建模技术的不断改进和计算机能力的提高。这种方法的最终科学目标是“用现实世界的数据来检验理论发现,其方式允许经验支持的理论随着时间的推移而累积,每个研究人员的工作都是在以前的工作基础上适当地建立起来的。”这个课题已经应用于研究领域,如资产定价、竞争与合作、交易成本、市场结构与产业组织与动态、福利经济学、机制设计、信息与不确定性、宏观经济学和马克思主义政治经济学。 75-79 -{zh-cn:互联网档案馆; zh-tw:網際網路檔案館; zh-hk:互聯網檔案館;}-存檔,存档日期26 April 2012..
       • Alvin E. Roth (2002). "The Economist as Engineer: Game Theory, Experimentation, and Computation as Tools for Design Economics," Econometrica, 70(4), pp. 1341–1378.
  8. Tesfatsion, Leigh (2006), "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," ch. 16, Handbook of Computational Economics, v. 2, part 2, ACE study of economic system. Abstract and pre-pub PDF.
  9. • Leigh Tesfatsion (2006). "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," ch. 16, Handbook of Computational Economics, v. 2, [pp. 831-880] sect. 5. Abstract and pre-pub PDF.
       • Kenneth L. Judd (2006). "Computationally Intensive Analyses in Economics," Handbook of Computational Economics, v. 2, ch. 17, pp. 881- 893. Pre-pub PDF.
       • Leigh Tesfatsion and Kenneth L. Judd, ed. (2006). Handbook of Computational Economics, v. 2. Description -{zh-cn:互联网档案馆; zh-tw:網際網路檔案館; zh-hk:互聯網檔案館;}-存檔,存档日期6 March 2012. & and chapter-preview The "agents" in ACE models can represent individuals (e.g. people), social groupings (e.g. firms), biological entities (e.g. growing crops), and/or physical systems (e.g. transport systems). The ACE modeler provides the initial configuration of a computational economic system comprising multiple interacting agents. The modeler then steps back to observe the development of the system over time without further intervention. In particular, system events should be driven by agent interactions without external imposition of equilibrium conditions. Issues include those common to experimental economics in general and development of a common framework for empirical validation and resolving open questions in agent-based modeling. ACE 模型中的“代理人”可以代表个体(例如:。)、社会群体(例如:。公司)、生物实体(例如:。作物生长)及/或物理系统(例如:。运输系统)。ACE 模型提供了由多个相互作用的代理组成的计算经济系统的初始配置。然后,建模人员回过头来观察系统随着时间的推移而发展的情况,而不需要进一步的干预。特别是,系统事件应该由主体间的相互作用驱动,而不受外部平衡条件的影响。这些问题包括实验经济学通用的问题,以及开发一个用于经验验证的通用框架和解决基于主体的建模中的开放性问题。 links.
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