基于主体的计算经济学

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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).

基于主体的计算经济学 Agent-based Computational Economics (ACE)是计算经济学的一个研究领域，将整个经济视作不同主体相互作用的动态系统，研究经济的发展过程。[4] 因此，它属于复杂适应系统的科学范式。在相应的基于主体模型中，“主体”指根据特定规则进行交互的计算实体，而非真实的人。其中交互的规则一种对个体行为和社会互动的建模。[5] 这些规则甚至可以通过人工智能方法（如Q学习 Q-learning及其他强化学习技术）来获得。[6]


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] 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.[6] 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.[7]


传统经济学中个体会理性地计算均衡状态地理论假设被舍弃，取而代之地是更为宽松、现实地假设——个体的理性有限，且其行为仅仅是对市场变化做出适应（而非计算均衡）。基于主体的计算经济学模型（ACE）将数值分析方法应用于复杂动力学问题，进行计算机模拟，这种方法可以应对传统方法，如数学方程不适用的问题。从建模者指定的初始条件开始，计算经济学模型会随着时间的推移而不断演化，模型中的主体不断地对其他参与者交互，包括从交互中学习。在这个角度来看，ACE 被认为是一种自下而上研究经济系统的方法。


ACE has a similarity to, and overlap with, game theory as an agent-based method for modeling social interactions.[8] 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.[9]

ACE 与博弈论时有相似之处和重叠部分，都可以看作以一种研究社会交往的基于主体的建模方法。但实践者也注意到了ACE与标准的博弈论方法的区别，例如，在ACE中，事件仅由初始条件驱动；ACE不在乎计算上是否易于处理；ACE中，主体有自动选择和学习的能力。

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."[10] The subject has been applied to research areas like asset pricing,[11] competition and collaboration,[12] transaction costs,[13] market structure and industrial organization and dynamics,[14] welfare economics,[15] and mechanism design,[16] information and uncertainty,[17] macroeconomics,[18] and Marxist economics.[19]

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，ACE方法也在不断发展，其终极科学目标是“用实际数据来检验理论发现——使得可靠的、有实际经验和数据支撑的理论能不断积累，而研究人员也能在可靠的前人理论基础上做进一步探索。”现在，ACE方法已应用于如资产定价、竞争与合作、交易成本 Transaction Cost、市场形式与产业组织 Industrial Organization与动态、福利经济学 Welfare Economics、机制设计 Mechanism Design、信息与不确定性、宏观经济学 Macroeconomics和马克思主义政治经济学 Marxist economics等研究领域。

Overview 概述

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.[7] Issues include those common to experimental economics in general[8] and development of a common framework for empirical validation and resolving open questions in agent-based modeling.[9]

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“主体（agent）”可以代表个体（如人）、社会群体（如公司）、生物实体（如生长中的庄稼）和/或物理系统（如运输系统），整个ACE模型就是由多个相互作用的主体组成的计算经济系统。建模者首先为ACE模型设置初始参数。然后，ACE模型就会自行演化，建模人员则观察系统随着时间的推移而演化的情况，不需要做进一步的干预。注意，系统活动应由主体间的相互作用驱动，而不需要外部强加平衡条件。目前来说，ACE面临的研究问题包括所有实验经济学 Experimental economics会遇到的普遍问题，以及开发用于验证模型正确性的的通用框架，和其他基于主体的建模中尚未解决的开放问题。


ACE is an officially designated special interest group (SIG) of the Society for Computational Economics.[10] Researchers at the Santa Fe Institute have contributed to the development of ACE.

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 同时也是是美国计算经济学学会的一个官方特殊兴趣团体 (SIG) 。圣菲研究所的研究人员为 ACE 的发展做出了贡献。


Example: finance 举例：金融

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.[11][12][13] 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,[14] and some papers have suggested that ACE might be a useful methodology for understanding the recent financial crisis.[15][16][17]

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 方法已广泛应用的领域之一是资产定价。布莱恩·亚瑟 W. Brian Arthur，埃里克·鲍姆 Eric Baum，威廉·布洛克 William Brock，Cars Hommes，布莱克·勒巴朗 Blake LeBaron等人已开发出一个计算模型，其中许多主体从一组可能的预测策略中选择一种或几种以预测股票价格，他们自己的预测结果影响了他们的自己的资产需求，进而影响整体股票价格。这些模型假设每个主体更有可能选择最近前几轮中成功的预测策略。任何策略的成功取决于市场条件，也取决于目前正在被主体们使用的预测策略。这些模型的实验结果经常表明，随着主体改换预测策略，资产价格可能会出现大起大落。最近，布洛克、Hommes 和瓦格纳 Wagener （2009）使用了这类模型来论证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。


See also 参见

  • ACEGES 基于主体的全球能源系统的计算经济学


References 参考资料

  1. ↑ • W. Brian Arthur, 1994. "Inductive Reasoning and Bounded Rationality," American Economic Review, 84(2), pp. 406-411 -{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;}-的存檔，存档日期21 May 2013..
       • 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;}-的存檔，存档日期26 April 2012..
  2. ↑ Scott E. Page (2008). "agent-based models," The New Palgrave Dictionary of Economics, 2nd Edition. Abstract.
  3. ↑ Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, The MIT Press, Cambridge, MA, 1998 [1] -{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;}-的存檔，存档日期4 September 2009.
  4. ↑ • W. Brian Arthur, 1994. "Inductive Reasoning and Bounded Rationality," American Economic Review, 84(2), pp. 406-411 -{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;}-的存檔，存档日期21 May 2013..
       • 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;}-的存檔，存档日期26 April 2012..
  5. ↑ Scott E. Page (2008). "agent-based models," The New Palgrave Dictionary of Economics, 2nd Edition. Abstract.
  6. ↑ Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, The MIT Press, Cambridge, MA, 1998 [2] -{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;}-的存檔，存档日期4 September 2009.
  7. ↑ Summary of methods -{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;}-的存檔，存档日期26 May 2007.: Department of Economics, Politics and Public Administration, Aalborg University, Denmark website.
  8. ↑ Vernon L. Smith, 2008. "experimental economics," The New Palgrave Dictionary of Economics, 2nd Edition. Abstract.
  9. ↑ Giorgio Fagiolo, Alessio Moneta, and Paul Windrum, 2007. "A Critical Guide to Empirical Validation of Agent-Based Models in Economics: Methodologies, Procedures, and Open Problems," Computational Economics, 30, pp. 195–226.
  10. ↑ Society for Computational Economics website.
  11. ↑ 引用错误：无效<ref>标签；未给name属性为arthuretal的引用提供文字
  12. ↑ W. Brock and C. Hommes (1997), 'A rational route to randomness.' Econometrica 65 (5), pp. 1059-1095.
  13. ↑ C. Hommes (2008), 'Interacting agents in finance,' in The New Palgrave Dictionary of Economics.
  14. ↑ Brock, W.; Hommes, C.; Wagener, F. (2009). "More hedging instruments may destabilize markets" (PDF). Journal of Economic Dynamics and Control. 33 (11): 1912–1928. doi:10.1016/j.jedc.2009.05.004.
  15. ↑ M. Buchanan (2009), 'Meltdown modelling. Could agent-based computer models prevent another financial crisis?.' Nature, Vol. 460, No. 7256. (5 August 2009), pp. 680-682.
  16. ↑ J.D. Farmer, D. Foley (2009), 'The economy needs agent-based modelling.' Nature, Vol. 460, No. 7256. (5 August 2009), pp. 685-686.
  17. ↑ M. Holcombe, S. Coakley, M.Kiran, S. Chin, C. Greenough, D.Worth, S.Cincotti, M.Raberto, A. Teglio, C. Deissenberg, S. van der Hoog, H. Dawid, S. Gemkow, P. Harting, M. Neugart. Large-scale Modeling of Economic Systems, Complex Systems, 22(2), 175-191, 2013

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