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  1. A Mid-Level Approach to Modeling Scientific Communities.Audrey Harnagel - 2019 - Studies in History and Philosophy of Science Part A 76:49-59.
    This paper provides an account of mid-level models, which calibrate highly theoretical agent-based models of scientific communities by incorporating empirical information from real-world systems. As a result, these models more closely correspond with real-world communities, and are better suited for informing policy decisions than extant how-possibly models. I provide an exemplar of a mid-level model of science funding allocation that incorporates bibliometric data from scientific publications and data generated from empirical studies of peer review into an epistemic landscape model. The (...)
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  • In Defense of Meta-Analysis.Bennett Holman - 2019 - Synthese 196 (8):3189-3211.
    Arguments that medical decision making should rely on a variety of evidence often begin from the claim that meta-analysis has been shown to be problematic. In this paper, I first examine Stegenga’s argument that meta-analysis requires multiple decisions and thus fails to provide an objective ground for medical decision making. Next, I examine three arguments from social epistemologists that contend that meta-analyses are systematically biased in ways not appreciated by standard epistemology. In most cases I show that critiques of meta-analysis (...)
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  • Computer Modeling in Philosophy of Religion.F. LeRon Shults - 2019 - Open Philosophy 2 (1):108-125.
    How might philosophy of religion be impacted by developments in computational modeling and social simulation? After briefly describing some of the content and context biases that have shaped traditional philosophy of religion, this article provides examples of computational models that illustrate the explanatory power of conceptually clear and empirically validated causal architectures informed by the bio-cultural sciences. It also outlines some of the material implications of these developments for broader metaphysical and metaethical discussions in philosophy. Computer modeling and simulation can (...)
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  • Signals and Spite in Fluctuating Populations.Patrick Forber & Rory Smead - 2019 - Open Philosophy 2 (1):137-146.
    Spite is behavior that harms others at a cost to the actor. The presence of spite in human and animal populations presents an evolutionary puzzle. Recent work has suggested small populations and pre-play signaling can have a significant effect on the evolution of spite. Here, we use computational methods to explore these factors in fluctuating populations that may go extinct. We find that the presence of spite can make a population significantly more likely to go extinct, but that this does (...)
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  • Diversity and Democracy: Agent-Based Modeling in Political Philosophy.Bennett Holman, William Berger, Daniel J. Singer, Patrick Grim & Aaron Bramson - 2018 - Historical Social Research 43:259-284.
    Agent-based models have played a prominent role in recent debates about the merits of democracy. In particular, the formal model of Lu Hong and Scott Page and the associated “diversity trumps ability” result has typically been seen to support the epistemic virtues of democracy over epistocracy (i.e., governance by experts). In this paper we first identify the modeling choices embodied in the original formal model and then critique the application of the Hong-Page results to philosophical debates on the relative merits (...)
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  • Epistemological Framework for Computer Simulations in Building Science Research: Insights From Theory and Practice.Amos Kalua & James Jones - 2020 - Philosophies 5 (30):30-0.
    Computer simulations are widely used within the area of building science research. Building science research deals with the physical phenomena that affect buildings, including heat and mass transfer, lighting and acoustic transmission. This wide usage of computer simulations, however, is characterized by a divergence in thought on the composition of an epistemological framework that may provide guidance for their deployment in research. This paper undertakes a fundamental review of the epistemology of computer simulations within the context of the philosophy of (...)
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  • Modeling the Social Organization of Science: Chasing Complexity Through Simulations.Carlo Martini & Manuela Fernández Pinto - 2016 - European Journal for Philosophy of Science 7 (2):221-238.
    At least since Kuhn’s Structure, philosophers have studied the influence of social factors in science’s pursuit of truth and knowledge. More recently, formal models and computer simulations have allowed philosophers of science and social epistemologists to dig deeper into the detailed dynamics of scientific research and experimentation, and to develop very seemingly realistic models of the social organization of science. These models purport to be predictive of the optimal allocations of factors, such as diversity of methods used in science, size (...)
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  • How to Infer Explanations From Computer Simulations.Florian J. Boge - 2020 - Studies in History and Philosophy of Science Part A 82:25-33.
    Computer simulations are involved in numerous branches of modern science, and science would not be the same without them. Yet the question of how they can explain real-world processes remains an issue of considerable debate. In this context, a range of authors have highlighted the inferences back to the world that computer simulations allow us to draw. I will first characterize the precise relation between computer and target of a simulation that allows us to draw such inferences. I then argue (...)
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  • Why is There No Successful Whole Brain Simulation (Yet)?Klaus M. Stiefel & Daniel S. Brooks - 2019 - Biological Theory 14 (2):122-130.
    With the advent of powerful parallel computers, efforts have commenced to simulate complete mammalian brains. However, so far none of these efforts has produced outcomes close to explaining even the behavioral complexities of animals. In this article, we suggest four challenges that ground this shortcoming. First, we discuss the connection between hypothesis testing and simulations. Typically, efforts to simulate complete mammalian brains lack a clear hypothesis. Second, we treat complications related to a lack of parameter constraints for large-scale simulations. To (...)
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