IDEAS home Printed from https://ideas.repec.org/a/jas/jasssj/2007-1-1.html
   My bibliography  Save this article

Dynamic Agent Compression

Author

Abstract

We introduce a new method for processing agents in agent-based models that significantly improves the efficiency of certain models. Dynamic Agent Compression allows agents to shift in and out of a compressed state based on their changing levels of heterogeneity. Sets of homogeneous agents are stored in compact bins, making the model more efficient in its use of memory and computational cycles. Modelers can use this increased efficiency to speed up the execution times, to conserve memory, or to scale up the complexity or number of agents in their simulations. We describe in detail an implementation of Dynamic Agent Compression that is lossless, i.e., no model detail is discarded during the compression process. We also contrast lossless compression to lossy compression, which promises greater efficiency gains yet may introduce artifacts in model behavior. The advantages outweigh the overhead of Dynamic Agent Compression in models where agents are unevenly heterogeneous — where a set of highly heterogeneous agents are intermixed with numerous other agents that fall into broad internally homogeneous categories. Dynamic Agent Compression is not appropriate in models with few, exclusively complex, agents.

Suggested Citation

  • Stephen Wendel & Catherine Dibble, 2007. "Dynamic Agent Compression," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(2), pages 1-9.
    • Handle: RePEc:jas:jasssj:2007-1-1
    as

    Download full text from publisher

    File URL: https://www.jasss.org/10/2/9/9.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dibble, Catherine, 2006. "Computational Laboratories for Spatial Agent-Based Models," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 31, pages 1511-1548, Elsevier.
    2. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Buda, Rodolphe, 2005. "Numerical Analysis in Econom(etr)ic Softwares: the Data-Memory Shortage Management," MPRA Paper 9145, University Library of Munich, Germany, revised 2007.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Joe Oppenheimer & Stephen Wendel & Norman Frohlich, 2011. "Paradox lost: Explaining and modeling seemingly random individual behavior in social dilemmas," Journal of Theoretical Politics, , vol. 23(2), pages 165-187, April.
    2. Citera, Emanuele & Gouri Suresh, Shyam & Setterfield, Mark, 2023. "The network origins of aggregate fluctuations: A demand-side approach," Structural Change and Economic Dynamics, Elsevier, vol. 64(C), pages 111-123.
    3. Popoyan, Lilit & Napoletano, Mauro & Roventini, Andrea, 2017. "Taming macroeconomic instability: Monetary and macro-prudential policy interactions in an agent-based model," Journal of Economic Behavior & Organization, Elsevier, vol. 134(C), pages 117-140.
    4. J. Silvestre, & T. Araújo & M. St. Aubyn, 2016. "Economic growth and individual satisfaction in an agent-based economy," Working Papers Department of Economics 2016/19, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.
    5. Deniz Erdemlioglu & Nikola Gradojevic, 2021. "Heterogeneous investment horizons, risk regimes, and realized jumps," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 617-643, January.
    6. Balint, T. & Lamperti, F. & Mandel, A. & Napoletano, M. & Roventini, A. & Sapio, A., 2017. "Complexity and the Economics of Climate Change: A Survey and a Look Forward," Ecological Economics, Elsevier, vol. 138(C), pages 252-265.
    7. Kubin, Ingrid & Zörner, Thomas O. & Gardini, Laura & Commendatore, Pasquale, 2019. "A credit cycle model with market sentiments," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 159-174.
    8. Eugenio Caverzasi & Antoine Godin, 2013. "Stock-flow Consistent Modeling through the Ages," Economics Working Paper Archive wp_745, Levy Economics Institute.
    9. Westerhoff Frank H., 2008. "The Use of Agent-Based Financial Market Models to Test the Effectiveness of Regulatory Policies," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 228(2-3), pages 195-227, April.
    10. Luca Riccetti & Alberto Russo & Mauro Gallegati, 2015. "An agent based decentralized matching macroeconomic model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 10(2), pages 305-332, October.
    11. Klaus Jaffe, 2015. "Agent based simulations visualize Adam Smith's invisible hand by solving Friedrich Hayek's Economic Calculus," Papers 1509.04264, arXiv.org, revised Nov 2015.
    12. Lovric, M. & Kaymak, U. & Spronk, J., 2008. "A Conceptual Model of Investor Behavior," ERIM Report Series Research in Management ERS-2008-030-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    13. repec:hal:spmain:info:hdl:2441/5bnglqth5987gaq6dhju3psjn3 is not listed on IDEAS
    14. Francesco Lamperti & Giovanni Dosi & Mauro Napoletano & Andrea Roventini & Alessandro Sapio, 2018. "And then he wasn't a she : Climate change and green transitions in an agent-based integrated assessment model," Working Papers hal-03443464, HAL.
    15. Dirk Helbing & Thomas U. Grund, 2013. "Editorial: Agent-Based Modeling And Techno-Social Systems," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 16(04n05), pages 1-3.
    16. Frank Westerhoff & Martin Hohnisch, 2010. "Consumer sentiment and countercyclical fiscal policies," International Review of Applied Economics, Taylor & Francis Journals, vol. 24(5), pages 609-618.
    17. Gennaro Zezza & Michalis Nikiforos, 2017. "Stock-flow Consistent Macroeconomic Models: A Survey," EcoMod2017 10762, EcoMod.
    18. Zhang, Hui & Cao, Libin & Zhang, Bing, 2017. "Emissions trading and technology adoption: An adaptive agent-based analysis of thermal power plants in China," Resources, Conservation & Recycling, Elsevier, vol. 121(C), pages 23-32.
    19. Cincotti, Silvano & Raberto, Marco & Teglio, Andrea, 2010. "Credit money and macroeconomic instability in the agent-based model and simulator Eurace," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 4, pages 1-32.
    20. Poledna, Sebastian & Thurner, Stefan & Farmer, J. Doyne & Geanakoplos, John, 2014. "Leverage-induced systemic risk under Basle II and other credit risk policies," Journal of Banking & Finance, Elsevier, vol. 42(C), pages 199-212.
    21. Waters, George A., 2009. "Chaos in the cobweb model with a new learning dynamic," Journal of Economic Dynamics and Control, Elsevier, vol. 33(6), pages 1201-1216, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jas:jasssj:2007-1-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Francesco Renzini (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.