“Agent-based computational economics”的意思、由来-开放百科全书

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.^[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]

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]

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

References

1. ^• W. Brian Arthur, 1994. "[https://ocw.tudelft.nl/wp-content/uploads/ElFarolArtur1994.pdf Inductive Reasoning and Bounded Rationality]," American Economic Review, 84(2), pp. 406-411 {{Webarchive|url=https://web.archive.org/web/20130521145936/http://www-personal.umich.edu/~samoore/bit885f2011/arthur-inductive.pdf |date=21 May 2013 }}.
   • Leigh Tesfatsion, 2003. "Agent-based Computational Economics: Modeling Economies as Complex Adaptive Systems," Information Sciences, 149(4), pp. 262-268 {{webarchive|url=https://web.archive.org/web/20120426000037/http://copper.math.buffalo.edu/urgewiki/uploads/Literature/Tesfatsion2002.pdf |date=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 {{Webarchive|url=https://web.archive.org/web/20090904194934/http://www.cs.ualberta.ca/~sutton/book/ebook/the-book.html |date=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 {{Webarchive|url=https://web.archive.org/web/20110105015853/http://www.santafe.edu/media/workingpapers/91-05-025.pdf |date=5 January 2011 }} p. 366.
   • Thomas C. Schelling (1978 [2006]). Micromotives and Macrobehavior, Norton. Description, [https://books.google.com/books?id=DenWKRgqzWMC&printsec=find&pg=PA1=#v=onepage&q&f=false preview].
   •Thomas J. Sargent, 1994. Bounded Rationality in Macroeconomics, Oxford. Description and chapter-preview 1st-page [https://www.questia.com/library/book/bounded-rationality-in-macroeconomics-thomas-j-sargent-by-thomas-j-sargent.jsp links.]
5. ^• Kenneth L. Judd, 2006. "Computationally Intensive Analyses in Economics," Handbook of Computational Economics, v. 2, ch. 17, Introduction, p. 883. [Pp. [https://books.google.com/books?id=6ITfRkNmKQcC&pg=PA881 881-] 893. Pre-pub [http://www2.econ.iastate.edu/tesfatsi/Judd.finalrev.pdf PDF].
   • _____, 1998. Numerical Methods in Economics, MIT Press. Links to description {{webarchive|url=https://web.archive.org/web/20120211061602/http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=3257 |date=11 February 2012 }} and [https://books.google.com/books?id=9Wxk_z9HskAC&pg=PR7 chapter previews].
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 {{webarchive|url=https://web.archive.org/web/20130514143904/http://www.econ.brown.edu/fac/Peter_Howitt/SummerSchool/Agent.pdf |date=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.
7. ^• 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.75-79.
   • Alvin E. Roth (2002). "The Economist as Engineer: Game Theory, Experimentation, and Computation as Tools for Design Economics," Econometrica, 70(4), pp. [https://web.archive.org/web/20040414102216/http://kuznets.fas.harvard.edu/~aroth/papers/engineer.pdf 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. [https://books.google.com/books?id=6ITfRkNmKQcC&pg=PA881 881-] 893. Pre-pub PDF.
   • Leigh Tesfatsion and Kenneth L. Judd, ed. (2006). Handbook of Computational Economics, v. 2. Description {{Webarchive|url=https://web.archive.org/web/20120306100156/http://www.elsevier.com/wps/find/bookdescription.cws_home/660847/description#description |date=6 March 2012 }} & and chapter-previewlinks.
10. ^¹B. 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.
11. ^Robert Axelrod (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration, Princeton. Description, contents, and [https://books.google.com/books?id=J0dgRGMdjmQC&printsec=find&pg=PR11#v=onepage&q&f=false preview].
12. ^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.
13. ^• Roberto Leombruni and Matteo Richiardi, ed. (2004), Industry and Labor Dynamics: The Agent-Based Computational Economics Approach. World Scientific Publishing {{ISBN|981-256-100-5}}. Description and chapter-preview [https://books.google.com/books?id=P5O7A5D55nQC&printsec=fond&pg=PR5#v=onepage&q&f=false links].
   • Joshua M. Epstein (2006). "Growing Adaptive Organizations: An Agent-Based Computational Approach," in Generative Social Science: Studies in Agent-Based Computational Modeling, pp. [https://books.google.com/books?id=543OS3qdxBYC&pg=PA309 309-] 344. Description and abstract.
14. ^Robert Axtell (2005). "The Complexity of Exchange," Economic Journal, 115(504, Features), pp. F193-F210.
15. ^• 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 {{Webarchive|url=https://web.archive.org/web/20120505140924/http://www.cup.cam.ac.uk/asia/catalogue/catalogue.asp?isbn=9780521872829 |date=5 May 2012 }}.
16. ^Tuomas W. Sandholm and Victor R. Lesser (2001). "Leveled Commitment Contracts and Strategic Breach," Games and Economic Behavior, 35(1-2), pp. 212-270.
17. ^• 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. [https://www.jstor.org/pss/29730026 236]-240. Pre-pub PDF.
   • Thomas J. Sargent (1994). Bounded Rationality in Macroeconomics, Oxford. Description and chapter-preview 1st-page [https://www.questia.com/library/book/bounded-rationality-in-macroeconomics-thomas-j-sargent-by-thomas-j-sargent.jsp links].
   • M. Oeffner (2009). 'Agent-based Keynesian Macroeconomics'. PhD thesis, Faculty of Economics, University of Würzburg.
18. ^A. F. Cottrell, P. Cockshott, G. J. Michaelson, I. P. Wright, V. Yakovenko(2009), Classical Econophysics. Routledge, {{ISBN|978-0-415-47848-9}}.
19. ^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.
20. ^Summary of methods: Department of Economics, Politics and Public Administration, Aalborg University, Denmark website.
21. ^Vernon L. Smith, 2008. "experimental economics," The New Palgrave Dictionary of Economics, 2nd Edition. Abstract.
22. ^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.
23. ^Society for Computational Economics website.
24. ^W. Brock and C. Hommes (1997), 'A rational route to randomness.' Econometrica 65 (5), pp. 1059-1095.
25. ^C. Hommes (2008), 'Interacting agents in finance,' in The New Palgrave Dictionary of Economics.
26. ^{{cite journal |first=W. |last=Brock |first2=C. |last2=Hommes |first3=F. |last3=Wagener |year=2009 |title=More hedging instruments may destabilize markets |journal=Journal of Economic Dynamics and Control |volume=33 |issue=11 |pages=1912–1928 |doi=10.1016/j.jedc.2009.05.004 }}
27. ^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.
28. ^J.D. Farmer, D. Foley (2009), 'The economy needs agent-based modelling.' Nature, Vol. 460, No. 7256. (5 August 2009), pp. 685-686.
29. ^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