Abstract
Animals are paradigms of complex systems. Therefore, models must be used to fully understand their emergent individual, group, and social behavior. Models can be physical, symbolic, mathematical, or computational, but they are always simpler than the animal systems they represent. Thus, models always have limitations. Viewed as tools for understanding, some models are better fit for investigating particular systems than others. Indeed, using models in science is much like using tools to build things. The use of tools is typically coordinated for the best success. For example, to drill a precise hole in a sheet of metal, several tools are needed: a ruler, pencil, punch, hammer, and drill. Holes can be made without some or all of these tools, but there will be a cost in precision and accuracy. To use models effectively in science, we need to understand their specific functions and limitations. Understanding these properties of models as tools is essential for their coordinated and effective use.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bish, R., Joshi, S., Schank, J., & Wexler, J. 2007, Mathematical modeling and computer simulation of a robotic rat pup. Mathematical and Computer Modeling, 54, 981–1000.
Bonabeau, E. 2002, Agent-based modeling: Methods and techniques for simulating human systems, Proceedings of the National Academy of Sciences, 99, 7280–7287.
Bryson, J. J., Ando, Y., & Lehmann, H. 2007, Agent-based modelling as scientific method: a case study analysing primate social behaviour. Phil. Trans. R. Soc. B., 362, 1685–1698.
Clarac, F., Brocard, F., & Vinay, L. 2004, The maturation of locomotor networks. Progress in Brain Research, 143, 57–66.
Frankel, G. S. & Gunn, D. L. 1961, The Orientation of Animals. New York: Dover.
Joshi, S., Schank, J. Giannini, J, Hargreaves, L. & Bish, R. 2004, Development of autonomous robotics technology for the study of rat pups. In: Proceedings of the IEEE Conference on robotics and Automation, (pp. 2860–2864).
Klein, J. 2002, Breve: a 3D simulation environment for the simulation of decentralized systems and artificial life. In R.K. Standish, MA. Bedau, & HA. Abbass (Eds.), Proceedings of Artificial Life VIII, the 8th International Conference on the Simulation and Synthesis of Living Systems (pp. 329–335). Cambridge: MIT Press.
Koehnle, T. J. & Schank, J. C. 2003, Power tools needed for the dynamical toolbox. Adaptive Behavior, 11, 291–295.
Levins, R. 1966, The strategy of model building in population biology. American Scientist. 54, 421–431.
Lloyd, S. 2000, Ultimate physical limits to computation. Nature, 406, 1047–1054.
May, C. J. 2007, Modeling the behavior of infant Norway rats (Rattus norvegicus). Dissertation Thesis, University of California, Davis, USA.
May, C. J., Schank, J. C., Joshi, S., Tran, J., Taylor, R. J., & Scott, I. 2006, Rat pups and random robots generate similar self-organized and intentional behavior. Complexity, 12, 1, 53–66.
Schank, J. C. 2001, Dimensions of modelling: Generality and integrativeness. Behavioral and Brain Sciences, 24, 1075–1076.
Schank, J. C. 2008, The development of locomotor kinematics in neonatal rats: an agent-based modeling analysis in group and individual contexts. Journal of Theoretical Biology, (in press).
Schank, J. C. & Alberts, J. R. 1997, Self-organized huddles of rat pups modeled by simple rules of individual behavior. Journal of Theoretical Biology, 189, 11–25.
Schank, J. C. & Alberts, J. R. 2000, The developmental emergence of coupled activity as cooperative aggregation in rat pups. Proceedings of the Royal Society of London: Biological Sciences, 267, 2307–2315.
Schank, J. C., & Koehnle, T. J. 2007, Modeling complex biobehavioral systems. In Laubichler, M. D. & Muller, G. B. (Eds.) Modeling Biology: Structures, Behaviors, Evolution, (pp. 219–244), MIT Press: Cambridge, MA.
Schank, J. C., May, C. J., Tran, J. T., & Joshi, S. S. 2004, A biorobotic investigation of Norway rat pups (Rattus norvegicus) in an arena. Adaptive Behavior, 12, 161–173.
Webb, B. 2001, Can robots make good models of biological behaviour? Behavioral and Brain Sciences, 24, 1033–1050.
Wimsatt, W. C. 1987, False models as means to truer theories, in Nitecki, M. and Hoffman, A. (Eds.), Neutral Models in Biology, (pp. 23–55), New York: Oxford University Press.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Schank, J., Joshi, S., May, C., Tran, J.T., Bish, R. (2011). A Multi-Modeling Approach to the Study of Animal Behavior. In: Minai, A.A., Braha, D., Bar-Yam, Y. (eds) Unifying Themes in Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17635-7_37
Download citation
DOI: https://doi.org/10.1007/978-3-642-17635-7_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17634-0
Online ISBN: 978-3-642-17635-7
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)