Models and science

The ancient Greeks crafted extraordinary models that continue to resonate. For instance, Ptolemy’s geocentric model, with Earth at the core and planets tracing intricate epicycles, elegantly accounted for celestial motion, much like the ancient ambition behind the Tower of Babel sought to bridge humanity and the heavens through a grand, unifying structure. Ptolemy’s framework dominated astronomy for over a millennium until Copernicus upended it with a heliocentric vision.

Modern science diverges from these ancient endeavors—whether the Greeks’ celestial models or the Babel builders’ monumental aspirations—through its insistence on testing ideas against observable reality. The theory of plate tectonics, once dismissed by the scientific establishment, won acceptance not through persuasive arguments but through compelling evidence: seafloor spreading, earthquake distributions, and paleomagnetic records. The Royal Society’s motto, Nullius in verba (“Take nobody’s word for it”), encapsulates this ethos—no authority, however revered, escapes scrutiny. This defines science: a steadfast commitment to an external, tangible reality that exists beyond human invention.

Richard Feynman captured this principle succinctly: “It doesn’t matter how beautiful your theory is, it doesn’t matter how smart you are. If it doesn’t agree with experiment, it’s wrong.” Models, no matter how elegant, are mere human constructs, subordinate to the unyielding truth of reality. Science demands humility—the rejection of hubris, whether in the form of a towering edifice or a cherished theory, and an embrace of relentless questioning, empirical rigor, and the courage to discard flawed ideas when evidence dictates.

Daniel Lemire, "Models and science," in Daniel Lemire's blog, May 23, 2025, https://lemire.me/blog/2025/05/23/models-and-science/.
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Daniel Lemire

A computer science professor at the University of Quebec (TELUQ).

One thought on “Models and science”

  1. As a layman, I’ve liked the last quote I read somewhere.

    It’s in the 2nd edition (2007) too.
    https://www.google.com.au/books/edition/Response_Surfaces_Mixtures_and_Ridge_Ana/pTb9PgbQAvYC?hl=en&gbpv=1&dq=All%20models%20are&pg=PA414

    https://en.wikiquote.org/wiki/George_E._P._Box#Empirical_Model-Building_and_Response_Surfaces_(1987)

    Empirical Model-Building and Response Surfaces (1987)

    Box, G. E. P., and Draper, N. R., (1987), Empirical Model Building and Response Surfaces, Wiley . ISBN 9780471810339 OCLC 13218699

    A mechanistic model has the following advantages:
    1. It contributes to our scientific understanding of the phenomenon under study.
    2. It usually provides a better basis for extrapolation (at least to conditions worthy of further experimental investigation if not through the entire range of all input variables).
    3. It tends to be parsimonious (i.e, frugal) in the use of parameters and to provide better estimates of the response
    p. 13-14 as cited in: Andrew Odlyzko (2010) Social Networks and Mathematical Models Electronic Commerce Research and Applications 9(1): 26-28 (2010)

    Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful.
    p. 74

    Essentially, all models are wrong, but some are useful.
    p. 424,

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