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  1. The Epistemic Importance of Technology in Computer Simulation and Machine Learning.Michael Resch & Andreas Kaminski - 2019 - Minds and Machines 29 (1):1-9.
    Scientificity is essentially methodology. The use of information technology as methodological instruments in science has been increasing for decades, this raises the question: Does this transform science? This question is the subject of the Special Issue in Minds and Machines “The epistemological significance of methods in computer simulation and machine learning”. We show that there is a technological change in this area that has three methodological and epistemic consequences: methodological opacity, reproducibility issues, and altered forms of justification.
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  • Massive Simulation of Complex Behaviour.Max Urchs - 2016 - Studies in Logic, Grammar and Rhetoric 48 (1):71-84.
    The promise of Newtonian science to create a universal precise explanation of all phenomena seems to be out-dated. “Cutting through complexity” may kill potential solutions. The complexity of real phenomena should be accepted and at best tamed by appropriate techniques. Complexity, a recent megatrend in the sciences, may effectuate another scientific revolution.
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  • (1 other version)Exploring Minds: Modes of Modelling and Simulation in Artificial Intelligence.Hajo Greif - 2021 - Perspectives on Science 29 (4):409-435.
    -/- The aim of this paper is to grasp the relevant distinctions between various ways in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. The proposed taxonomy cuts across (...)
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  • (1 other version)Exploring Minds: Modes of Modeling and Simulation in Artificial Intelligence.Hajo Greif - 2021 - Perspectives on Science 29 (4):409-435.
    The aim of this paper is to grasp the relevant distinctions between various ways in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. The proposed taxonomy cuts across the (...)
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