IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v221y2010i5p738-760.html
   My bibliography  Save this article

HOMINIDS: An agent-based spatial simulation model to evaluate behavioral patterns of early Pleistocene hominids

Author

Listed:
  • Griffith, Cameron S.
  • Long, Byron L.
  • Sept, Jeanne M.

Abstract

The HOMINIDS ABM is a new Agent Based Model that simulates the actions of two species of proto-human agents defined by a few, simple parameters. These proto-human agents attempt to subsist by foraging and nesting on dynamic, spatially explicit landscapes. The landscapes are described with a number of parameters based on empirical field data collected in habitats in East Africa. The results of three separate scenarios with 1 year model runs repeated 30 times each, for a total of 90 simulations, are presented and discussed to illustrate both the capacity and flexibility of our ABM modeling environment. The simulations show that the model food preferences and anatomy ascribed to Australopithecus boisei resulted in different expressions of foraging behaviors and subsistence strategies in two distinct ecological settings, and that adding tubers to the diet significantly increases the chances of the hominid agents meeting their daily caloric requirements year-round. In addition, this paper provides links to the open-source implementation code, along with the user documentation, design document, java API, and all datasets required to replicate the simulations.

Suggested Citation

  • Griffith, Cameron S. & Long, Byron L. & Sept, Jeanne M., 2010. "HOMINIDS: An agent-based spatial simulation model to evaluate behavioral patterns of early Pleistocene hominids," Ecological Modelling, Elsevier, vol. 221(5), pages 738-760.
    • Handle: RePEc:eee:ecomod:v:221:y:2010:i:5:p:738-760
    DOI: 10.1016/j.ecolmodel.2009.11.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380009007558
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2009.11.009?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. J. Gareth Polhill & Dawn C. Parker & Daniel Brown & Volker Grimm, 2008. "Using the ODD Protocol for Describing Three Agent-Based Social Simulation Models of Land-Use Change," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(2), pages 1-3.
    2. J. Gareth Polhill & Bruce Edmonds, 2007. "Open Access for Social Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(3), pages 1-10.
    3. Uri Wilensky & William Rand, 2007. "Making Models Match: Replicating an Agent-Based Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(4), pages 1-2.
    4. Andre Costopoulos, 2001. "Evaluating the Impact of Increasing Memory on Agent Behaviour: Adaptive Patterns in an Agent Based Simulation of Subsistence," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 4(4), pages 1-7.
    5. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, December.
    Full references (including those not matched with items on IDEAS)

    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. Emiliano Alvarez & Volker Grimm, 2024. "The added value of using the ODD Protocol for agent-based modeling in Economics: go for it!," Working Papers 307, Red Nacional de Investigadores en Economía (RedNIE).
    2. Prenkert, Frans & Følgesvold, Atle, 2014. "Relationship strength and network form: An agent-based simulation of interaction in a business network," Australasian marketing journal, Elsevier, vol. 22(1), pages 15-27.
    3. Stuart Rossiter & Jason Noble & Keith R.W. Bell, 2010. "Social Simulations: Improving Interdisciplinary Understanding of Scientific Positioning and Validity," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 13(1), pages 1-10.
    4. Rand, William & Rust, Roland T., 2011. "Agent-based modeling in marketing: Guidelines for rigor," International Journal of Research in Marketing, Elsevier, vol. 28(3), pages 181-193.
    5. Frans Prenkert, 2012. "Business Network Simulation: Combining Research Cases and Agent-Based Models in a Robust Methodology," International Journal of Business Administration, International Journal of Business Administration, Sciedu Press, vol. 3(6), pages 82-92, November.
    6. Matus Halas, 2018. "Balancing Against Threats In Interactions Determined By Distance And Overall Gains," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(05), pages 1-22, August.
    7. José Manuel Galán & Luis R. Izquierdo & Segismundo S. Izquierdo & José Ignacio Santos & Ricardo del Olmo & Adolfo López-Paredes & Bruce Edmonds, 2009. "Errors and Artefacts in Agent-Based Modelling," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-1.
    8. Rubén Fuentes-Fernández & Samer Hassan & Juan Pavón & José M. Galán & Adolfo López-Paredes, 2012. "Metamodels for role-driven agent-based modelling," Computational and Mathematical Organization Theory, Springer, vol. 18(1), pages 91-112, March.
    9. Bruce Edmonds, 2010. "Bootstrapping Knowledge About Social Phenomena Using Simulation Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 13(1), pages 1-8.
    10. Luís de Sousa & Alberto Rodrigues da Silva, 2015. "Showcasing a Domain Specific Language for Spatial Simulation Scenarios with case studies," ERSA conference papers ersa15p1044, European Regional Science Association.
    11. Ross Richardson & Matteo G. Richiardi & Michael Wolfson, 2015. "We ran one billion agents. Scaling in simulation models," LABORatorio R. Revelli Working Papers Series 142, LABORatorio R. Revelli, Centre for Employment Studies.
    12. 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.
    13. Joshua M. Epstein, 2007. "Agent-Based Computational Models and Generative Social Science," Introductory Chapters, in: Generative Social Science Studies in Agent-Based Computational Modeling, Princeton University Press.
    14. Rich, Karl M. & Ross, R. Brent & Baker, A. Derek & Negassa, Asfaw, 2011. "Quantifying value chain analysis in the context of livestock systems in developing countries," Food Policy, Elsevier, vol. 36(2), pages 214-222, April.
    15. Laobing Zhang & Gabriele Landucci & Genserik Reniers & Nima Khakzad & Jianfeng Zhou, 2018. "DAMS: A Model to Assess Domino Effects by Using Agent‐Based Modeling and Simulation," Risk Analysis, John Wiley & Sons, vol. 38(8), pages 1585-1600, August.
    16. 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.
    17. Yanuar Nugroho & Gindo Tampubolon, 2008. "Network Dynamics in the Transition to Democracy: Mapping Global Networks of Contemporary Indonesian Civil Society," Sociological Research Online, , vol. 13(5), pages 144-160, September.
    18. Barr, Jason & Saraceno, Francesco, 2009. "Organization, learning and cooperation," Journal of Economic Behavior & Organization, Elsevier, vol. 70(1-2), pages 39-53, May.
    19. Sheri M. Markose, 2005. "Computability and Evolutionary Complexity: Markets as Complex Adaptive Systems (CAS)," Economic Journal, Royal Economic Society, vol. 115(504), pages 159-192, 06.
    20. Nannen, Volker & van den Bergh, Jeroen C. J. M. & Eiben, A. E., 2008. "Impact of Environmental Dynamics on Economic Evolution: Uncertainty, Risk Aversion, and Policy," MPRA Paper 13834, University Library of Munich, Germany.

    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:eee:ecomod:v:221:y:2010:i:5:p:738-760. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    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.