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A Credible-World Account of Biological Models

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Abstract

In a broad brush, biological models are often constructed in two general types: (1) as a concrete model; (2) as an abstract model. A concrete model is a material model such as model organisms, while an abstract model is a mathematical or computational model consists of equations or algorithms. Though there are types of biological models that cannot be strictly categorized as either concrete or abstract, they are falling somewhere in between this spectrum. In view of the fact that biological phenomena are sui generis and distinct from physical phenomena in many respects, using the concepts in standard modeling fails to account for the differences between biological sciences and physical sciences. Drawing from the resources of a credible-world account of models in economic modeling, I propose to reinterpret abstract models and concrete models in the light of the credible world account of models.

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Notes

  1. See Backhouse (2002), Morgan (2002) and Hausman (1992).

  2. For example, in a double-slit experiment, the target world is only the observed interference pattern, irrelevant things such as the cloud outside of the lab is not the constituent of the target world in the context of this experiment. Similarly, the target world of a mouse cancer model only consists of human cancer patients—we do not count, for example, diabetic patients as individuals in the target world of a mouse cancer model.

  3. I do not deny that there are exceptions to the general view that economic models are theoretical credible models. One of the examples is the Phillips Machine, which is a concrete mechanical monetary model set up to demonstrate the mechanisms of monetary flow. Economic parameters in the Phillips Machine are manipulable by pumping colored water through pipes. However, like what Sugden submits, most of the economic models are imaginary constructions of the economists.

  4. Cartwright avers that the problem of inducing from the model world to the real world is “the paucity of economic principles with serious empirical content” (2009, 57). She thinks that model assumptions are unrealistic in the wrong way (2009).

  5. Some economists are pessimistic about the intersection between the model world and the real world, and come to the conclusion that the model world does not reflect the real situations in the actual economics. In a letter to Science, Leontief wrote “page after page of professional economic journals are filled with mathematical formulas leading the reader from sets of more or less plausible but entirely arbitrary assumptions to precisely stated but irrelevant theoretical conclusions… without being able to advance, in any perceptible way, a systematic understanding of the structure and the operations of a real economic system.” (Leontief 1982, 104, cited in Lawson 1997, 4).

  6. The standard account of counterfactual is that a counterfactual is true iff the consequent is true at all relevant possible worlds where the antecedent is true. However, it has been recognized that the counterfactual is always false in the standard account, for the consequent is not true at all relevant antecedent worlds (see Schulz 2014; Hawthorne 2005). I do not take side on the various semantic accounts of counterfactual theory. Rather, I am inclined to use the term without a strict propositional connotation, as proposed by Grüne-Yanoff (2009a).

  7. I thank an anonymous reviewer for bringing to my attention about this point.

  8. Some examples: (1) a Markovian model to determine demographic characteristics, such as the family size and the extinction probability, of female populations in different countries (Hautphenne and Latouche 2012); (2) a mathematical formalism to predict protein interaction and function due to the experimental obstacles and challenges (Saini and Hou 2013; Lei et al. 2013); (3) studies of evolutionary events over hundreds of thousands of years. (Cartwright et al. 2011).

  9. I thank an anonymous reviewer for motivating me to clarify the credibility of a model world and its relation to predictive force and causal isolation.

  10. In terms of the disease similarity between the animal model and human atherosclerosis, pig is a much better model compared to mouse because the plasma levels and atherosclerotic lesions are close to what has been observed in human atherosclerosis when fed with cholesterol. The reason pig is not widely used in the animal studies is due to the cost factor and difficulty in handling (see Jawień et al. 2004, 504).

  11. The main dissimilarity between ApoE deficient mouse models and human atherosclerosis is that plaque rupture is not observed in the former, while it is always observed in human (Jawień et al. 2004). However, duplication of a precise mechanism of plaque rupture using animal models (not just limited to mouse models) is always complicated and problematic (Gross 2009, 309).

  12. The C57BL/6 strain of mice is the exception, but their vascular lesions differ from the human lesions in terms of the histological nature and location (Jawień et al. 2004). This strain is mainly used in the non-genetic context of atherosclerosis studies (Daugherty and Rateri 2006).

  13. See http://www.drugbank.ca/drugs/DB01039 for detail (Wishart et al. 2006).

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  1. Multimedia University, Jalan Multimedia, 63000, Cyberjaya, Selangor, Malaysia

    Sim-Hui Tee

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Tee, SH. A Credible-World Account of Biological Models. Axiomathes 28, 309–324 (2018). https://doi.org/10.1007/s10516-017-9365-z

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