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过程。因此，它属于复杂适应系统范式 Paradigm。在相应的智能体基模型 Agent-based Model(ABM)中，“主体”指时空中“根据规则建模的交互计算对象”，而非真实的人。这些规则是为了建立基于激励和信息的行为和社会互动模型而制定的。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 Q-learning及其他强化学习技术）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] 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]


   • _____, 1998. Numerical Methods in Economics, MIT Press. Links to description and chapter previews.</ref> 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.


   • _____, 1998.经济学数值方法，麻省理工学院出版社。链接到[ http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=3257说明]和[ https://books.google.com/books?id=9wxk_z9hskac&pg=pr7章预览]。 

经济均衡 Economic Equilibrium中主体最优化 Mathematical Optimization的理论假设被限制较少的适应市场力量的有限理性 Bounded Rationality主体假设所取代。ACE 模型将数值分析方法应用于复杂动力学问题的计算机模拟，对其更传统的方法，如定理公式可能无法方便地使用。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，其组成主体不断地相互作用，包括从交互中学习。在这些方面，ACE 被认为是研究经济体系 Economic System的一种自下而上的文化方法。


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]

75-79 .
   • Alvin E. Roth (2002). "The Economist as Engineer: Game Theory, Experimentation, and Computation as Tools for Design Economics," Econometrica, 70(4), pp. 1341–1378.</ref> 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.

75-79 .
   • Alvin E. Roth (2002).《作为工程师的经济学家: 博弈论、实验和计算作为设计经济学的工具》 ，《经济学》 ，70(4) ，页。[ https://web.archive.org/web/20040414102216/http://kuznets.fas.harvard.edu/~aroth/papers/engineer.pdf 1341-1378].

ACE 与博弈论（作为基于主体的社会互动建模方法时）有相似之处和重叠部分。但实践者也注意到了与标准方法的区别，例如，在仅由初始条件驱动的ACE事件建模中，均衡是否存在或者在计算上是否易于处理，以及在建模过程中促进主体自主性和学习方面。


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] 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.[20][21]

(2009), Classical Econophysics. Routledge, .</ref>

(2009) ，古典经济物理学。路特利奇，参考文献

该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。其最终科学目标是“用实际数据来检验理论发现——以允许经验支持的理论随着时间的推移而累积的方式（每位研究者的工作恰当建立在之前工作的基础之上）。”其现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.[22] Issues include those common to experimental economics in general[23] and development of a common framework for empirical validation and resolving open questions in agent-based modeling.[24]

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”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）。ACE 模型提供了由多个相互作用的主体组成的计算经济系统的初始配置。然后，建模人员返回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。注意，系统活动应由主体间的相互作用驱动，而不需要外部强加的平衡条件。研究问题包括实验经济学 Experimental economics通用的问题，以及开发用于经验验证的通用框架和解决基于主体的建模中尚未解决的问题。


ACE is an officially designated special interest group (SIG) of the Society for Computational Economics.[25] 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 是美国计算经济学学会的一个官方指定的特殊利益集团 (SIG) 。圣菲研究所 Santa Fe Institute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][26][27] 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,[28] and some papers have suggested that ACE might be a useful methodology for understanding the recent financial crisis.[29][30][31]

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以预测股票价格，这影响了他们的资产需求，进而影响股票价格。这些模型假设主体更有可能选择最近成功的预测策略。任何策略的成功取决于市场条件，也取决于目前正在使用的一组策略。这些模型经常表明，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


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. ↑ • John H. Holland and John H. Miller (1991). "Artificial Adaptive Agents in Economic Theory," American Economic Review, 81(2), pp. 365-370 -{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5 January 2011. p. 366.
       • Thomas C. Schelling (1978 [2006]). Micromotives and Macrobehavior, Norton. 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;}-的存檔，存档日期2 November 2017., preview.
       • 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.   • Thomas C. Schelling (1978 [2006]). Micromotives and Macrobehavior, Norton. Description , preview.
       • •托马斯 · c · 谢林(Thomas c. Schelling，1978[2006])。微动机和宏行为，诺顿。[ http://books.wwnorton.com/books/978-0-393-32946-9/ 描述] ，[ https://books.google.com/books?id=denwkrgqzwmc&printsec=find&pg=pa1=#v=onepage&q&f=false 预览] . br / • 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. Thomas J. Sargent, 1994. Bounded Rationality in Macroeconomics, Oxford. Description and chapter-preview 1st-page links.
  6. ↑ • 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. 托马斯·萨金特，1994年。牛津大学宏观经济学有限理性。[ http://www.oup.com/us/catalog/general/subject/economics/macroeconomictheory/?view=usa&ci=9780198288695说明]和第一章预览页面[ https://www.questia.com/library/book/bounded-rationality-in-macroeconomics-thomas-j-sargent-by-thomas-j-sargent.jsp 链接]。] / ref 。 文献• Kenneth l. Judd，2006。《经济学中的计算密集分析》 ，《计算经济学手册，第2卷，第5页。17，Introduction，p. 883.[ Pp.Https://books.google.com/books?id=6itfrknmkqcc&pg=pa881881-893.预订酒吧[ http://www2.econ.iastate.edu/tesfatsi/judd.finalrev.PDF PDF ]。
       • _____, 1998. Numerical Methods in Economics, MIT Press. Links to 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;}-的存檔，存档日期11 February 2012. and chapter previews.
  7. ↑ • 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;}-的存檔，存档日期14 May 2013..
       • _____ (1997). "How Economists Can Get Alife," in W. B. Arthur, S. Durlauf, and D. Lane, eds., The Economy as an Evolving Complex System, II, pp. 533-564. Addison-Wesley. Pre-pub PDF.
  8. ↑ • Joseph Y. Halpern (2008). "computer science and game theory," The New Palgrave Dictionary of Economics, 2nd Edition. Abstract.
       • Yoav Shoham (2008). "Computer Science and Game Theory," Communications of the ACM, 51(8), pp. ACE has a similarity to, and overlap with, game theory as an agent-based method for modeling social interactions.   • Yoav Shoham (2008). "Computer Science and Game Theory," Communications of the ACM, 51(8), pp. • Yoav Shoham (2008)。《计算机科学与博弈论》 ，美国计算机学会通讯，51(8)页。 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;}-的存檔，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.
  9. ↑ 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.
  10. ↑ • 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;}-的存檔，存档日期6 March 2012. & and chapter-preview 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."   • 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 & and chapter-preview • Kenneth l. Judd (2006)。《经济学中的计算密集分析》 ，计算经济学手册，第2卷，第5页。17页。Https://books.google.com/books?id=6itfrknmkqcc&pg=pa881881-893.Pre-pub PDF.
       • Leigh Tesfatsion and Kenneth L. Judd, ed.(2006).计算经济学手册第2节。[ http://www.elsevier.com/wps/find/bookdescription.cws_home/660847/Description#Description 说明]和章节预览 links.
  11. ↑ 11.0 11.1 B. Arthur, J. Holland, B. LeBaron, R. Palmer, P. Taylor (1997), 'Asset pricing under endogenous expectations in an artificial stock market,' in The Economy as an Evolving Complex System II, B. Arthur, S. Durlauf, and D. Lane, eds., Addison Wesley.
  12. ↑ Robert Axelrod (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration, Princeton. Description, contents, and preview.
  13. ↑ Tomas B. Klosa and Bart Nooteboom, 2001. "Agent-based Computational Transaction Cost Economics," Journal of Economic Dynamics and Control 25(3–4), pp. 503–52. Abstract.
  14. ↑ • Roberto Leombruni and Matteo Richiardi, ed. (2004), Industry and Labor Dynamics: The Agent-Based Computational Economics Approach. World Scientific Publishing
    . 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;}-的存檔，存档日期27 July 2010. and chapter-preview links.
       • Joshua M. Epstein (2006). "Growing Adaptive Organizations: An Agent-Based Computational Approach," in Generative Social Science: Studies in Agent-Based Computational Modeling, pp. 309- 344. 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;}-的存檔，存档日期26 January 2012. and abstract.
  15. ↑ Robert Axtell (2005). "The Complexity of Exchange," Economic Journal, 115(504, Features), pp. F193-F210.
  16. ↑ • The New Palgrave Dictionary of Economics (2008), 2nd Edition:
         Roger B. Myerson "mechanism design." Abstract.
         _____. "revelation principle." Abstract.
         Tuomas Sandholm. "computing in mechanism design." Abstract.
       • Noam Nisan and Amir Ronen (2001). "Algorithmic Mechanism Design," Games and Economic Behavior, 35(1-2), pp. 166–196.
       • Noam Nisan et al., ed. (2007). Algorithmic Game Theory, Cambridge University Press. 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;}-的存檔，存档日期5 May 2012..
  17. ↑ Tuomas W. Sandholm and Victor R. Lesser (2001). "Leveled Commitment Contracts and Strategic Breach," Games and Economic Behavior, 35(1-2), pp. 212-270.
  18. ↑ • David Colander, Peter Howitt, Alan Kirman, Axel Leijonhufvud, and Perry Mehrling, 2008. "Beyond DSGE Models: Toward an Empirically Based Macroeconomics," American Economic Review, 98(2), pp. 236-240. Pre-pub PDF.
       • Thomas J. Sargent (1994). Bounded Rationality in Macroeconomics, Oxford. Description and chapter-preview 1st-page links.
       • M. Oeffner (2009). 'Agent-based Keynesian Macroeconomics'. PhD thesis, Faculty of Economics, University of Würzburg.
  19. ↑ A. F. Cottrell, P. Cockshott, G. J. Michaelson, I. P. Wright, V. Yakovenko links.
  20. ↑ A. F. Cottrell, P. Cockshott, G. J. Michaelson, I. P. Wright, V. Yakovenko [ http://www.sciencedirect.com/science?_ob=publicationurl&_hubeid=1-s2.0-s1574002105x02003&_cid=273377&_pubtype=hs&_auth=y&_acct=c000228598&_version=1&_urlversion=0&_userid=10&md5=e4757b4f65755ed6340a11fee9615200连结。参考文献 参考文献 a。科特雷尔，p. Cockshott，g. j. Michaelson，i. p. Wright，v. Yakovenko (2009), Classical Econophysics. Routledge,
    .
  21. ↑ Leigh Tesfatsion (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.
  22. ↑ 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.
  23. ↑ Vernon L. Smith, 2008. "experimental economics," The New Palgrave Dictionary of Economics, 2nd Edition. Abstract.
  24. ↑ 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.
  25. ↑ Society for Computational Economics website.
  26. ↑ W. Brock and C. Hommes (1997), 'A rational route to randomness.' Econometrica 65 (5), pp. 1059-1095.
  27. ↑ C. Hommes (2008), 'Interacting agents in finance,' in The New Palgrave Dictionary of Economics.
  28. ↑ 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.
  29. ↑ 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.
  30. ↑ J.D. Farmer, D. Foley (2009), 'The economy needs agent-based modelling.' Nature, Vol. 460, No. 7256. (5 August 2009), pp. 685-686.
  31. ↑ 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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类别: 金融中的蒙特卡罗方法

Category:Computational fields of study

类别: 研究的计算领域


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