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  1. Leyes, mecanismos y modelos en biología: el caso de la genética mendeliana.Mario Casanueva - 2018 - Scientiae Studia 15 (2):343.
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  • Modeling complexity: cognitive constraints and computational model-building in integrative systems biology.Miles MacLeod & Nancy J. Nersessian - 2018 - History and Philosophy of the Life Sciences 40 (1):17.
    Modern integrative systems biology defines itself by the complexity of the problems it takes on through computational modeling and simulation. However in integrative systems biology computers do not solve problems alone. Problem solving depends as ever on human cognitive resources. Current philosophical accounts hint at their importance, but it remains to be understood what roles human cognition plays in computational modeling. In this paper we focus on practices through which modelers in systems biology use computational simulation and other tools to (...)
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  • Diagrammatic Reasoning.William Bechtel - unknown
    Diagrams figure prominently in human reasoning, especially in science. Cognitive science research has provided important insights into the inferences afforded by diagrams and revealed differences in the reasoning made possible by physically instantiated diagrams and merely imagined ones. In scientific practice, diagrams figures prominently both in the way scientists reason about data and in how they conceptualize explanatory mechanisms. To identify patterns in data, scientists often graph it. While some graph formats, such as line graphs, are used widely, scientists often (...)
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  • Constraint‐Based Reasoning for Search and Explanation: Strategies for Understanding Variation and Patterns in Biology.Sara Green & Nicholaos Jones - 2016 - Dialectica 70 (3):343-374.
    Life scientists increasingly rely upon abstraction-based modeling and reasoning strategies for understanding biological phenomena. We introduce the notion of constraint-based reasoning as a fruitful tool for conceptualizing some of these developments. One important role of mathematical abstractions is to impose formal constraints on a search space for possible hypotheses and thereby guide the search for plausible causal models. Formal constraints are, however, not only tools for biological explanations but can be explanatory by virtue of clarifying general dependency-relations and patterning between (...)
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  • Constructing Diagrams to Understand Phenomena and Mechanisms.Benjamin Sheredos & William Bechtel - manuscript
    Biologists often hypothesize mechanisms to explai phenomena. Our interest is how their understanding of the phenomena and mechanisms develops as they construct diagrams to communicate their claims. We present two case studies in which scientists integrate various data to create a single diagram to communicate their major conclusions in a research publication. In both cases, the history of revisions suggests that scientists' initial drafts encode biases and oversights that are only gradually overcome through prolonged, reflective re-design. To account for this, (...)
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  • Why Do Biologists Use so Many Diagrams?Benjamin Sheredos, Daniel Burnston, Adele Abrahamsen & William Bechtel - 2013 - Philosophy of Science 80 (5):931-944.
    Diagrams have distinctive characteristics that make them an effective medium for communicating research findings, but they are even more impressive as tools for scientific reasoning. Focusing on circadian rhythm research in biology to explore these roles, we examine diagrammatic formats that have been devised to identify and illuminate circadian phenomena and to develop and modify mechanistic explanations of these phenomena.
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  • Bowtie Structures, Pathway Diagrams, and Topological Explanation.Nicholaos Jones - 2014 - Erkenntnis 79 (5):1135-1155.
    While mechanistic explanation and, to a lesser extent, nomological explanation are well-explored topics in the philosophy of biology, topological explanation is not. Nor is the role of diagrams in topological explanations. These explanations do not appeal to the operation of mechanisms or laws, and extant accounts of the role of diagrams in biological science explain neither why scientists might prefer diagrammatic representations of topological information to sentential equivalents nor how such representations might facilitate important processes of explanatory reasoning unavailable to (...)
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  • Using Computational Models to Discover and Understand Mechanisms.William Bechtel - 2016 - Studies in History and Philosophy of Science Part A 56:113-121.
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  • Sketching Biological Phenomena and Mechanisms.Sheredos Benjamin & Bechtel William - 2017 - Topics in Cognitive Science 9 (4):970-985.
    In many fields of biology, both the phenomena to be explained and the mechanisms proposed to explain them are commonly presented in diagrams. Our interest is in how scientists construct such diagrams. Researchers begin with evidence, typically developed experimentally and presented in data graphs. To arrive at a robust diagram of the phenomenon or the mechanism, they must integrate a variety of data to construct a single, coherent representation. This process often begins as the researchers create a first sketch, and (...)
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