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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/7635

    Título
    Social Simulation Models as Refuting Machines
    Autor
    Mauhe, Nicolas
    Izquierdo Millán, Luis RodrigoAutoridad UBU Orcid
    Izquierdo, Segismundo S.
    Publicado en
    Journal of Artificial Societies and Social Simulation. 2023, V. 26, n. 2
    Editorial
    SimSoc Consortium
    Fecha de publicación
    2023-03
    ISSN
    1460-7425
    DOI
    10.18564/jasss.5076
    Resumo
    This paper discusses a prominent way in which social simulations can contribute (and have contributed) to the advance of science; namely, by refuting some of our incorrect beliefs about how the real world works. More precisely, social simulations can produce counter-examples that reveal something is wrong in a prevailing scientific assumption. Indeed, here we argue that this is a role that many well-known social simulation models have played, and it may be one of the main reasons why such well-known models have become so popular. To test this hypothesis, here we examine several popular models in the social simulation literature and we find that all these models are most naturally interpreted as providers of compelling and reproducible (computer-generated) evidence that refuted some assumption or belief in a prevailing theory. By refuting prevailing theories, these models have greatly advanced science and, in some cases, have even opened a new field of research.
    Palabras clave
    Social Simulation
    Computer Simulation
    Refutation
    Modelling
    Counter-Example
    Markov Chain
    Materia
    Matemáticas
    Mathematics
    Informática
    Computer science
    Sociología
    Sociology
    URI
    http://hdl.handle.net/10259/7635
    Versión del editor
    https://doi.org/10.18564/jasss.5076
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