Thinking Like a Wolf, a Sheep, or a Firefly: Learning Biology Through Constructing and Testing Computational Theories
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
Learn more about PhilPapers
Cognition & Instruction 24 (2):171-209 (2006)
Biological phenomena can be investigated at multiple levels, from the molecular to the cellular to the organismic to the ecological. In typical biology instruction, these levels have been segregated. Yet, it is by examining the connections between such levels that many phenomena in biology, and complex systems in general, are best explained. We describe a computation-based approach that enables students to investigate the connections between different biological levels. Using agent-based, embodied modeling tools, students model the microrules underlying a biological phenomenon and observe the resultant aggregate dynamics. We describe 2 cases in which this approach was used. In both cases, students framed hypotheses, constructed multiagent models that incorporate these hypotheses, and tested these by running their models and observing the outcomes. Contrasting these cases against traditionally used, classical equation-based approaches, we argue that the embodied modeling approach connects more directly to students’ experience, enables extended investigations as well as deeper understanding, and enables “advanced” topics to be productively introduced into the high school curriculum.
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
Michael Weisberg & Kenneth Reisman (2008). The Robust Volterra Principle. Philosophy of Science 75 (1):106-131.
Michelene T. H. Chi, Rod D. Roscoe, James D. Slotta, Marguerite Roy & Catherine C. Chase (2012). Misconceived Causal Explanations for Emergent Processes. Cognitive Science 36 (1):1-61.
Sanjay Chandrasekharan & Nancy J. Nersessian (2015). Building Cognition: The Construction of Computational Representations for Scientific Discovery. Cognitive Science 39 (8):1727-1763.
Similar books and articles
Richard M. Burian (1997). Comments on Complexity and Experimentation in Biology. Philosophy of Science 64 (4):291.
Nagarjuna G. (forthcoming). Towards a Model of Life and Cognition. In B. V. Srikantan (ed.), Foundations of Science. Center for Studies in Civilizations
Barbara L. Horan (1988). Theoretical Models, Biological Complexity and the Semantic View of Theories. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1988:265 - 277.
Gordana Dodig-Crnkovic (2011). Significance of Models of Computation, From Turing Model to Natural Computation. Minds and Machines 21 (2):301-322.
Rob Hengeveld (2002). Methodology Going Astray in Population Biology. Acta Biotheoretica 50 (2):77-93.
Arciszewski Michal, Reducing the Dauer Larva: Molecular Models of Biological Phenomena in Caenorhabditis Elegans Research.
Ron Sun, Andrew Coward & Michael J. Zenzen (2005). On Levels of Cognitive Modeling. Philosophical Psychology 18 (5):613-637.
Philippe De Backer, Danny De Waele & Linda Van Speybroeck (2010). Ins and Outs of Systems Biology Vis-À-Vis Molecular Biology: Continuation or Clear Cut? Acta Biotheoretica 58 (1):15-49.
Slobodan Perovic & Paul-Antoine Miquel (2011). On Gene's Action and Reciprocal Causation. Foundations of Science 16 (1):31-46.
Added to index2010-02-23
Total downloads46 ( #91,659 of 1,907,059 )
Recent downloads (6 months)1 ( #468,221 of 1,907,059 )
How can I increase my downloads?