Agent-based computational economics ( ACE ) is the area of computational economics that studies economic processes, including all economies , as dynamic systems of interacting agents . As such, it falls in the paradigm of complex adaptive systems .  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, made using AI methods (such as Q-learning and other reinforcement learning techniques). 
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 methodsof analysis to computer-based simulations of complex dynamic problems. Starting from initial conditions specified by the modeler, the computational economy evolves over time as its constituting agents interacting 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 . 
ACE has a similarity to, and overlap with, game theory as an agent-based method for social interaction modeling.  The purpose of this study is to determine whether it is a condition that is appropriate for the first time, whether it is not equilibria or computationally tractable, and that it is a question of the facilitation of agent autonomy and learning. 
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 the findings of empirically supported theories to 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 cooperation ,  transaction costs ,  market structure and industrial organization and dynamics, welfare economics ,  and mechanism design ,  information and uncertainty , macroeconomics ,  and Marxist economics .  
The ” agents ” in ACE models can represent individuals (eg people), social groupings (eg firms), biological entities (eg growing crops), and / or physical systems (eg transport systems). The ACE modeler provides the initial configuration of a computational economic system involving multiple interacting agents. The modeler then steps back to the development of the system. 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 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.
One area where ACE has been applied. W. Brian Arthur , Eric Baum, William Brock , Men’s Cars, and Blake LeBaron, among others, have developed computational models in which thus affects stock prices. These models assume that they are more likely to be successful. The success of any strategy will depend on market conditions and is currently being used. These models often find that broad booms and mechanisms can occur as agents switch across forecasting strategies.   More recently, Brock, Men, and Wagener (2009) have used this model to argue that the introduction of new hedging instruments may destabilize the market,  and some papers have suggested that ACE might be a useful for the recent financial crisis .   
- Agent-based social simulation
- Artificial economics
- Computational economics
- Macroeconomic model
- Multi-agent system
- Statistical finance
- Jump up^ •W. Brian Arthur, 1994. “Inductive Reasoning and Bounded Rationality,”American Economic Review, 84 (2), pp. 406-411.
•Leigh Tesfatsion, 2003. “Agent-based Computational Economics: Modeling Economies as Complex Adaptive Systems,”Information Science, 149 (4), pp. 262-268 Archived26 April 2012 at theWayback Machine..
- Jump up^ Scott E. Page (2008). “agent-based models,” The New Palgrave Dictionary of Economics , 2nd Edition. Abstract.
- Jump up^ Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, The MIT Press, Cambridge, MA, 1998
- Jump up^ •John H. Hollandand John H. Miller (1991). “Artificial Adaptive Agents in Economic Theory,”American Economic Review, 81 (2), pp. 365-370p. 366.
•Thomas C. Schelling(1978 ). Micromotives and Macrobehavior, Norton. Description,preview.
•Thomas J. Sargent, 1994.Bounded Rationality in Macroeconomics, Oxford. Descriptionand chapter-preview 1st-pagelinks.
- Jump up^ • Kenneth L. Judd, 2006. “Computationally Intensive Analyzes in Economics,”Handbook of Computational Economics, v. 2, ch. 17, Introduction, p. 883. [Pp. 881-893. Pre-pubPDF.
• _____, 1998.Numerical Methods in Economics, MIT Press. Links todescription Archived11 February 2012 at theWayback Machine. andchapter previews.
- Jump up^ • Leigh Tesfatsion (2002). “Agent-Based Computational Economics: Growing Economies from the Bottom Up,”Artificial Life, 8 (1), pp.55-82. Abstractand pre-pubPDF Archived14 May 2013 at theWayback Machine..
• _____ (1997). “How Economists Can Get Alife,” in WB Arthur, S. Durlauf, and D. Lane, eds.,The Economy as an Evolving Complex System, II, pp. 533-564. Addison-Wesley. Pre-pubPDF.
- Jump up^ •Joseph 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. 1341-1378.
- Jump up^ 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. Abstractand pre-pubPDF.
- Jump up^ • 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.Abstractand pre-pubPDF.
•Kenneth L. Judd(2006). “Computationally Intensive Analyzes in Economics,”Handbook of Computational Economics, v. 2, ch. 17, pp. 881-893. Pre-pubPDF.
• Leigh Tesfatsion and Kenneth L. Judd, ed. (2006). Handbook of Computational Economics, v. 2.Description& and chapter-previewlinks.
- ^ Jump up to:a b 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.
- Jump up^ Robert Axelrod(1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration, Princeton. Description,contents, andpreview.
- Jump up^ Tomas B. Klosa andBart Nooteboom, 2001. “Agent-based Computational Transaction Cost Economics,”Journal of Economic Dynamics and Control25 (3-4), pp. 503-52. Abstract.
- Jump up^ • Leombruni Roberto and Matteo Richiardi, ed. (2004),Industry and Labor Dynamics: The Agent-Based Computational Economics Approach. World Scientific PublishingISBN 981-256-100-5. Descriptionand chapter-previewlinks.
•Joshua M. Epstein(2006). “Growing Adaptive Organizations: An Agent-Based Computational Approach,” inGenerative Social Science: Studies in Agent-Based Computational Modeling, pp. 309-344.Descriptionandabstract.
- Jump up^ Robert Axtell(2005). “The Complexity of Exchange,”Economic Journal, 115 (504, Features), pp. F193-F210.
- Jump up^ •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 Nisanand 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.
- Jump up^ Tuomas W. Sandholm and Victor R. Lesser (2001). “Leveled Commitment Contracts and Strategic Breach,”Games and Economic Behavior, 35 (1-2), pp. 212-270.
- Jump up^ •David Colander,Peter Howitt, Alan Kirman,Axel Leijonhufvud, andPerry Mehrling, 2008. “Beyond DSGE Models: Toward an empirically Based Macroeconomics,”American Economic Review, 98 (2), pp. 236-240. Pre-pubPDF.
•Thomas J. Sargent(1994). Bounded Rationality in Macroeconomics, Oxford. Descriptionand chapter-preview 1st-pagelinks.
• M. Oeffner (2009). ‘Agent-based Keynesian Macroeconomics’. PhD thesis, Faculty of Economics, University of Würzburg.
- Jump up^ AF Cottrell, P. Cockshott, GJ Michaelson, IP Wright, V.Yakovenko (2009),Classical Econophysics. Routledge,ISBN 978-0-415-47848-9.
- Jump up^ 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. Abstractand pre-pubPDF.
- Jump up^ Summary of methods:Department of Economics, Politics and Public Administration, Aalborg University, Denmarkwebsite.
- Jump up^ Vernon L. Smith, 2008. “experimental economics,”The New Palgrave Dictionary of Economics, 2nd Edition. Abstract.
- Jump up^ Giorgio Fagiolo, Alessio Moneta, and Paul Windrum, 2007. “A Critical Guide to Empirical Validation of Agent-Based Models in Economics: Methods, Procedures, and Open Problems,”Computational Economics, 30, pp. 195-226.
- Jump up^ Society for Computational Economicswebsite.
- Jump up^ W. Brock and C. Men (1997), ‘A rational route to randomness.’ Econometrica65 (5), pp. 1059-1095.
- Jump up^ C. Men (2008), ‘Interacting agents in finance,’ inThe New Palgrave Dictionary of Economics.
- Jump up^ Brock, W .; Men, C .; Wagener, F. (2009). “More hedging instruments may destabilize markets”. Journal of Economic Dynamics and Control . 33 (11): 1912-1928. doi : 10.1016 / j.jedc.2009.05.004 .
- Jump up^ Mr. Buchanan (2009), ‘Meltdown modeling. Could a computer-based agent prevent another financial crisis? . ‘ Nature, Vol. 460, No. 7256. (August 5, 2009), pp. 680-682.
- Jump up^ JD Farmer, D. Foley (2009), ‘The economy needs agent-based modeling.’ Nature, Vol. 460, No. 7256. (August 5, 2009), pp. 685-686.
- Jump up^ M. Holcombe, S. Coakley, M.Kiran, S. Chin, C. Greenough, D.Worth, S.Cincotti, M.Raberto, A.Teglio, C. Deissenberg, S.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