Search results for 'modeling strategies' (try it on Scholar)

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  1.  33
    Marcel Boumans (2012). Modeling Strategies for Measuring Phenomena In-and Outside the Laboratory. In Henk W. de Regt (ed.), Epsa Philosophy of Science: Amsterdam 2009. Springer 1--11.
    The Representational Theory of Measurement conceives measurement as establishing homomorphisms from empirical relational structures into numerical relation structures, called models. There are two different approaches to deal with the justification of a model: an axiomatic and an empirical approach. The axiomatic approach verifies whether a given relational structure satisfies certain axioms to secure homomorphic mapping. The empirical approach conceives models to function as measuring instruments by transferring observations of a phenomenon under investigation into quantitative facts about that phenomenon. These facts (...)
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  2. Marcel Weber (2014). Experimental Modeling in Biology: In Vivo Representation and Stand-Ins As Modeling Strategies. Philosophy of Science 81 (5):756-769.
    Experimental modeling in biology involves the use of living organisms (not necessarily so-called "model organisms") in order to model or simulate biological processes. I argue here that experimental modeling is a bona fide form of scientific modeling that plays an epistemic role that is distinct from that of ordinary biological experiments. What distinguishes them from ordinary experiments is that they use what I call "in vivo representations" where one kind of causal process is used to stand in (...)
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  3.  24
    Ehud Lamm (2013). Theoreticians as Professional Outsiders: The Modeling Strategies of John von Neumann and Norbert Wiener. In Oren Harman & Michael Dietrich (eds.), Outsider Scientists: Routes to Innovation in Biology. Chicago University Press
    Both von Neumann and Wiener were outsiders to biology. Both were inspired by biology and both proposed models and generalizations that proved inspirational for biologists. Around the same time in the 1940s von Neumann developed the notion of self reproducing automata and Wiener suggested an explication of teleology using the notion of negative feedback. These efforts were similar in spirit. Both von Neumann and Wiener used mathematical ideas to attack foundational issues in biology, and the concepts they articulated had lasting (...)
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  4.  1
    Sergio Sismondo (1997). Modelling Strategies: Creating Autonomy for Biology's Theory of Games. History and Philosophy of the Life Sciences 19 (2):147 - 161.
    John Maynard Smith is the person most responsible for the use of game theory in evolutionary biology, having introduced and developed its major concepts, and later surveyed its uses. In this paper I look at some rhetorical work done by Maynard Smith and his co-author G.R. Price to make game theory a standard and common modelling tool for the evolutionary study of behavior. The original presentation of the ideas — in a 1973 Nature article — is frequently cited but almost (...)
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  5.  17
    Tomasz Smoleń & Adam Chuderski (2010). Modeling Strategies in Stroop with a General Architecture of Executive Control. In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society 931--936.
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  6.  24
    Jonathan Opie (1998). Connectionist Modelling Strategies. Psycoloquy 9 (30).
    Green offers us two options: either connectionist models are literal models of brain activity or they are mere instruments, with little or no ontological significance. According to Green, only the first option renders connectionist models genuinely explanatory. I think there is a third possibility. Connectionist models are not literal models of brain activity, but neither are they mere instruments. They are abstract, IDEALISED models of the brain that are capable of providing genuine explanations of cognitive phenomena.
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  7.  2
    Dario Gregori, Lara Lusa, Rosalba Rosato & Luciano Silvestri (2008). Evaluating Effectiveness of Preoperative Testing Procedure: Some Notes on Modelling Strategies in Multi‐Centre Surveys. Journal of Evaluation in Clinical Practice 14 (1):11-18.
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  8.  8
    Gérard G. Emch (2007). Models and the Dynamics of Theory-Building in Physics. Part I—Modeling Strategies. Studies in History and Philosophy of Science Part B 38 (3):558-585.
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  9.  5
    Emch Gérard G. (2007). Models and the Dynamics of Theory-Building in Physics. Part I—Modeling Strategies. Studies in History and Philosophy of Science Part B.
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  10. Gérard G. Emch (2007). Models and the Dynamics of Theory-Building in Physics. Part I—Modeling Strategies. Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 38 (3):558-585.
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  11.  12
    D. M. Bailer-Jones (1999). Creative Strategies Employed in Modelling: A Case Study. [REVIEW] Foundations of Science 4 (4):375-388.
    This paper examines creative strategies employed inscientific modelling. It is argued that being creativepresents not a discrete event, but rather an ongoingeffort consisting of many individual `creative acts''.These take place over extended periods of time andcan be carried out by different people, working ondifferent aspects of the same project. The example ofextended extragalactic radio sources shows that, inorder to model a complicated phenomenon in itsentirety, the modelling task is split up into smallerproblems that result in several sub-models. This is (...)
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  12.  25
    Anya Plutynski (2006). Strategies of Model Building in Population Genetics. Philosophy of Science 73 (5):755-764.
    In 1966, Richard Levins argued that there are different strategies in model building in population biology. In this paper, I reply to Orzack and Sober’s (1993) critiques of Levins, and argue that his views on modeling strategies apply also in the context of evolutionary genetics. In particular, I argue that there are different ways in which models are used to ask and answer questions about the dynamics of evolutionary change, prospectively and retrospectively, in classical versus molecular evolutionary (...)
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  13. Swantje Bargmann, Magnus Ekh, Kenneth Runesson & Bob Svendsen (2010). Modeling of Polycrystals with Gradient Crystal Plasticity: A Comparison of Strategies. Philosophical Magazine 90 (10):1263-1288.
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  14. Heinz Mandl, Cornelia Gräsel & Frank Fischer (2000). Problem-Oriented Learning: Facilitating the Use of Domain-Specific and Control Strategies Through Modeling by an Expert. In Walter J. Perrig & Alexander Grob (eds.), Control of Human Behavior, Mental Processes, and Consciousness: Essays in Honor of the 60th Birthday of August Flammer. Erlbaum 165--182.
     
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  15. Nicolas Fillion & Robert M. Corless (2014). On the Epistemological Analysis of Modeling and Computational Error in the Mathematical Sciences. Synthese 191 (7):1451-1467.
    Interest in the computational aspects of modeling has been steadily growing in philosophy of science. This paper aims to advance the discussion by articulating the way in which modeling and computational errors are related and by explaining the significance of error management strategies for the rational reconstruction of scientific practice. To this end, we first characterize the role and nature of modeling error in relation to a recipe for model construction known as Euler’s recipe. We then (...)
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  16. Rasmus Grønfeldt Winther, Michael J. Wade & Christopher C. Dimond (2013). Pluralism in Evolutionary Controversies: Styles and Averaging Strategies in Hierarchical Selection Theories. Biology and Philosophy 28 (6):957-979.
    Two controversies exist regarding the appropriate characterization of hierarchical and adaptive evolution in natural populations. In biology, there is the Wright-Fisher controversy over the relative roles of random genetic drift, natural selection, population structure, and interdemic selection in adaptive evolution begun by Sewall Wright and Ronald Aylmer Fisher. There is also the Units of Selection debate, spanning both the biological and the philosophical literature and including the impassioned group-selection debate. Why do these two discourses exist separately, and interact relatively little? (...)
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  17.  29
    John Stewart & Olivier Gapenne (2004). Reciprocal Modelling of Active Perception of 2-D Forms in a Simple Tactile-Vision Substitution System. Minds and Machines 14 (3):309-330.
    The strategies of action employed by a human subject in order to perceive simple 2-D forms on the basis of tactile sensory feedback have been modelled by an explicit computer algorithm. The modelling process has been constrained and informed by the capacity of human subjects both to consciously describe their own strategies, and to apply explicit strategies; thus, the strategies effectively employed by the human subject have been influenced by the modelling process itself. On this basis, (...)
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  18.  76
    Rosanna Keefe (2012). Modelling Vagueness: What Can We Ignore? Philosophical Studies 161 (3):453-470.
    A theory of vagueness gives a model of vague language and of reasoning within the language. Among the models that have been offered are Degree Theorists’ numerical models that assign values between 0 and 1 to sentences, rather than simply modelling sentences as true or false. In this paper, I ask whether we can benefit from employing a rich, well-understood numerical framework, while ignoring those aspects of it that impute a level of mathematical precision that is not present in the (...)
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  19.  6
    William M. Goodwin (2015). Global Climate Modeling as Applied Science. Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 46 (2):339-350.
    In this paper I argue that the appropriate analogy for “understanding what makes simulation results reliable” in global climate modeling is not with scientific experimentation or measurement, but—at least in the case of the use of global climate models for policy development—with the applications of science in applied design problems. The prospects for using this analogy to argue for the quantitative reliability of GCMs are assessed and compared with other potential strategies.
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  20.  47
    William M. Goodwin (2015). Global Climate Modeling as Applied Science. Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 46 (2):339-350.
    In this paper I argue that the appropriate analogy for “understanding what makes simulation results reliable” in global climate modeling is not with scientific experimentation or measurement, but—at least in the case of the use of global climate models for policy development—with the applications of science in applied design problems. The prospects for using this analogy to argue for the quantitative reliability of GCMs are assessed and compared with other potential strategies.
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  21.  8
    Roberto Poli (2016). Belief Systems and the Modeling Relation. Foundations of Science 21 (1):195-206.
    The paper presents the most general aspects of scientific modeling and shows that social systems naturally include different belief systems. Belief systems differ in a variety of respects, most notably in the selection of suitable qualities to encode and the internal structure of the observables. The following results emerge from the analysis: conflict is explained by showing that different models encode different qualities, which implies that they model different realities; explicitly connecting models to the realities that they encode makes (...)
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  22.  7
    Ming Shan, Albert P. C. Chan, Yun Le & Yi Hu (2015). Investigating the Effectiveness of Response Strategies for Vulnerabilities to Corruption in the Chinese Public Construction Sector. Science and Engineering Ethics 21 (3):683-705.
    Response strategy is a key for preventing widespread corruption vulnerabilities in the public construction sector. Although several studies have been devoted to this area, the effectiveness of response strategies has seldom been evaluated in China. This study aims to fill this gap by investigating the effectiveness of response strategies for corruption vulnerabilities through a survey in the Chinese public construction sector. Survey data obtained from selected experts involved in the Chinese public construction sector were analyzed by factor analysis (...)
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  23.  47
    Anya Plutynski (2006). Strategies of Model Building in Population Genetics. Philosophy of Science 73 (5):755-764.
    In 1966, Richard Levins argued that there are different strategies in model building in population biology. In this paper, I reply to Orzack and Sober's (1993) critiques of Levins and argue that his views on modeling strategies apply also in the context of evolutionary genetics. In particular, I argue that there are different ways in which models are used to ask and answer questions about the dynamics of evolutionary change, prospectively and retrospectively, in classical versus molecular evolutionary (...)
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  24.  42
    James Justus (2005). Qualitative Scientific Modeling and Loop Analysis. Philosophy of Science 72 (5):1272-1286.
    Loop analysis is a method of qualitative modeling anticipated by Sewall Wright and systematically developed by Richard Levins. In Levins’ (1966) distinctions between modeling strategies, loop analysis sacrifices precision for generality and realism. Besides criticizing the clarity of these distinctions, Orzack and Sober (1993) argued qualitative modeling is conceptually and methodologically problematic. Loop analysis of the stability of ecological communities shows this criticism is unjustified. It presupposes an overly narrow view of qualitative modeling and underestimates (...)
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  25.  10
    Jeffrey C. Schank (2001). Dimensions of Modelling: Generality and Integrativeness. Behavioral and Brain Sciences 24 (6):1075-1076.
    Webb has articulated a clear, multi-dimensional framework for discussing simulation models and modelling strategies. This framework will likely co-evolve with modelling. As such, it will be important to continue to clarify these dimensions and perhaps add to them. I discuss the dimension of generality and suggest that a dimension of integrativeness may also be needed.
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  26.  8
    Julia Bursten, Multiscale Modeling in Nanoscience: Beyond Hierarchical Relations.
    Winsberg's "handshaking" account of inter-model relations is a well-known theory of multiscale modeling in physical systems. Winsberg argues that relations among the component models in a multiscale modeling system are not related mereologically, but rather by empirically determined algorithms. I argue that while the handshaking account does demonstrate the existence of non-mereological relationships among component models, Winsberg does not attend to the different ways in which handshaking algorithms are developed. By overlooking the distinct strategies employed in different (...)
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  27.  31
    Maureen O'Malley, Ingo Brigandt, Alan C. Love, John W. Crawford, Jack A. Gilbert, Rob Knight, Sandra D. Mitchell & Forest Rohwer (2014). Multilevel Research Strategies and Biological Systems. Philosophy of Science 81 (5):811-828.
    Multilevel research strategies characterize contemporary molecular inquiry into biological systems. We outline conceptual, methodological, and explanatory dimensions of these multilevel strategies in microbial ecology, systems biology, protein research, and developmental biology. This review of emerging lines of inquiry in these fields suggests that multilevel research in molecular life sciences has significant implications for philosophical understandings of explanation, modeling, and representation.
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  28.  13
    Tarja Knuuttila & Andrea Loettgers (forthcoming). Modelling as Indirect Representation? The Lotka–Volterra Model Revisited. British Journal for the Philosophy of Science:axv055.
    Is there something specific about modelling that distinguishes it from many other theoretical endeavours? We consider Michael Weisberg’s thesis that modelling is a form of indirect representation through a close examination of the historical roots of the Lotka–Volterra model. While Weisberg discusses only Volterra’s work, we also study Lotka’s very different design of the Lotka–Volterra model. We will argue that while there are elements of indirect representation in both Volterra’s and Lotka’s modelling approaches, they are largely due to two other (...)
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  29.  12
    Matthew Mayhew & Patricia King (2008). How Curricular Content and Pedagogical Strategies Affect Moral Reasoning Development in College Students. Journal of Moral Education 37 (1):17-40.
    College instructors use a variety of approaches to teach students to reason more effectively about issues with a moral dimension and achieve mixed results. This pre?post study of 423 undergraduate students examined the effects of morally explicit and implicit curricular content and of selected pedagogical strategies on moral reasoning development. Using causal modelling to control for a range of student background variables as well as Time 1 scores, 52% of the variance in moral reasoning scores was explained; we found (...)
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  30.  28
    Collin Rice & Joshua Smart (2011). Interdisciplinary Modeling: A Case Study of Evolutionary Economics. Biology and Philosophy 26 (5):655-675.
    Biologists and economists use models to study complex systems. This similarity between these disciplines has led to an interesting development: the borrowing of various components of model-based theorizing between the two domains. A major recent example of this strategy is economists’ utilization of the resources of evolutionary biology in order to construct models of economic systems. This general strategy has come to be called evolutionary economics and has been a source of much debate among economists. Although philosophers have developed literatures (...)
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  31.  75
    Axel Gelfert (2013). Strategies of Model-Building in Condensed Matter Physics: Trade-Offs as a Demarcation Criterion Between Physics and Biology? Synthese 190 (2):253-272.
    This paper contrasts and compares strategies of model-building in condensed matter physics and biology, with respect to their alleged unequal susceptibility to trade-offs between different theoretical desiderata. It challenges the view, often expressed in the philosophical literature on trade-offs in population biology, that the existence of systematic trade-offs is a feature that is specific to biological models, since unlike physics, biology studies evolved systems that exhibit considerable natural variability. By contrast, I argue that the development of ever more sophisticated (...)
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  32.  10
    Vitaly Pronskikh, Computer Modeling and Simulation: Towards Epistemic Distinction Between Verification and Validation.
    Verification and validation of computer codes and models used in simulation are two aspects of the scientific practice of high importance and have recently been discussed by philosophers of science. While verification is predominantly associated with the correctness of the way a model is represented by a computer code or algorithm, validation more often refers to model’s relation to the real world and its intended use. It has been argued that because complex simulations are generally not transparent to a practitioner, (...)
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  33. Daniel G. Goldstein & Gerd Gigerenzer (2002). Models of Ecological Rationality: The Recognition Heuristic. Psychological Review 109 (1):75-90.
    [Correction Notice: An erratum for this article was reported in Vol 109 of Psychological Review. Due to circumstances that were beyond the control of the authors, the studies reported in "Models of Ecological Rationality: The Recognition Heuristic," by Daniel G. Goldstein and Gerd Gigerenzer overlap with studies reported in "The Recognition Heuristic: How Ignorance Makes Us Smart," by the same authors and with studies reported in "Inference From Ignorance: The Recognition Heuristic". In addition, Figure 3 in the Psychological Review article (...)
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  34.  26
    Richard C. Schugart (2010). Mathematical Modeling in Wound Healing, Bone Regeneration and Tissue Engineering. Acta Biotheoretica 58 (4):355-367.
    The processes of wound healing and bone regeneration and problems in tissue engineering have been an active area for mathematical modeling in the last decade. Here we review a selection of recent models which aim at deriving strategies for improved healing. In wound healing, the models have particularly focused on the inflammatory response in order to improve the healing of chronic wound. For bone regeneration, the mathematical models have been applied to design optimal and new treatment strategies (...)
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  35.  32
    Werner Ehm (2005). Meta-Analysis O Mind-Matter Experiments: A Statistical Modeling Perspective. Mind and Matter 3 (1):85-132.
    Are there relationships between consciousness and the material world? Empirical evidence for such a connection was reported in several meta-analyses of mind-matter experiments designed to address this question. In this paper we consider such meta-analyses from a statistical modeling perspective, emphasizing strategies to validate the models and the associated statistical procedures. In particular, we explicitly model increased data variability and selection mechanisms, which permits us to estimate 'selection profiles ' and to reassess the experimental effect in view of (...)
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  36.  1
    William Goodwin, Global Climate Modeling as Applied Science.
    In this paper I argue that the appropriate analogy for “understanding what makes simulation results reliable” in Global Climate Modeling is not with scientific experimentation or measurement, but—at least in the case of the use of global climate models for policy development—with the applications of science in engineering design problems. The prospects for using this analogy to argue for the quantitative reliability of GCMs are assessed and compared with other potential strategies.
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  37.  23
    Richard A. Depue & Jeannine V. Morrone-Strupinsky (2005). Modeling Human Behavioral Traits and Clarifying the Construct of Affiliation and its Disorders. Behavioral and Brain Sciences 28 (3):371-378.
    Commentary on our target article centers around six main topics: (1) strategies in modeling the neurobehavioral foundation of human behavioral traits; (2) clarification of the construct of affiliation; (3) developmental aspects of affiliative bonding; (4) modeling disorders of affiliative reward; (5) serotonin and affiliative behavior; and (6) neural considerations. After an initial important research update in section R1, our Response is organized around these topics in the following six sections, R2 to R7.
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  38.  19
    John S. Fenlon, Malcolm J. Faddy, Menia Toussidou & Michael E. Courcy Williamdes (forthcoming). Egg Distributions of Insect Parasitoids: Modelling and Analysis of Temporal Data with Host Density Dependence. Acta Biotheoretica.
    A simple numerical procedure is presented for the problem of estimating the parameters of models for the distribution of eggs oviposited in a host. The modelling is extended to incorporate both host density and time dependence to produce a remarkably parsimonious structure with only seven parameters to describe a data set of over 3,000 observations. This is further refined using a mixed model to accommodate several large outliers. Both models show that the level of superparasitism declines with increasing host density, (...)
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  39.  13
    Johanna M. Harte & Pieter Koele (2001). Modelling and Describing Human Judgement Processes: The Multiattribute Evaluation Case. Thinking and Reasoning 7 (1):29 – 49.
    In this article we describe research methods that are used for the study of individual multiattribute evaluation processes. First we explain that a multiattribute evaluation problem involves the evaluation of a set of alternatives, described by their values on a number of alternatives. We discuss a number of evaluation strategies that may be applied to arrive at a conclusion about the attractiveness or suitability of the alternatives, and next introduce two main research paradigms in this area, structural modelling and (...)
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  40. C. Anthony Hunt, Ryan C. Kennedy, Sean H. J. Kim & Glen E. P. Ropella (2013). Agent-Based Modeling: A Systematic Assessment of Use Cases and Requirements for Enhancing Pharmaceutical Research and Development Productivity. Wiley Interdisciplinary Reviews 5 (4):461-480.
    A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What (...)
     
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  41. Giovanni Pezzulo & Haris Dindo (2013). Intentional Strategies That Make Co-Actors More Predictable: The Case of Signaling. Behavioral and Brain Sciences 36 (4):371-372.
    Pickering & Garrod (P&G) explain dialogue dynamics in terms of forward modeling and prediction-by-simulation mechanisms. Their theory dissolves a strict segregation between production and comprehension processes, and it links dialogue to action-based theories of joint action. We propose that the theory can also incorporate intentional strategies that increase communicative success: for example, signaling strategies that help remaining predictable and forming common ground.
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  42.  2
    Chiara Lisciandra (forthcoming). Robustness Analysis and Tractability in Modeling. European Journal for Philosophy of Science:1-17.
    In the philosophy of science and epistemology literature, robustness analysis has become an umbrella term that refers to a variety of strategies. One of the main purposes of this paper is to argue that different strategies rely on different criteria for justifications. More specifically, I will claim that: i) robustness analysis differs from de-idealization even though the two concepts have often been conflated in the literature; ii) the comparison of different model frameworks requires different justifications than the comparison (...)
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  43. Margaret Morrison (2009). Models, Measurement and Computer Simulation: The Changing Face of Experimentation. Philosophical Studies 143 (1):33 - 57.
    The paper presents an argument for treating certain types of computer simulation as having the same epistemic status as experimental measurement. While this may seem a rather counterintuitive view it becomes less so when one looks carefully at the role that models play in experimental activity, particularly measurement. I begin by discussing how models function as “measuring instruments” and go on to examine the ways in which simulation can be said to constitute an experimental activity. By focussing on the connections (...)
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  44.  54
    Richard Levins (2006). Strategies of Abstraction. Biology and Philosophy 21 (5):741-755.
    Abstraction is seen as an active process which both enlightens and obscures. Abstractions are not true or false but relatively enlightening or obscuring according to the problem under study; different abstractions may grasp different aspects of a problem. Abstractions may be useless if they can answer questions only about themselves. A theoretical enterprise explores reality through acluster of abstractions that use different perspectives, temporal and horizontal scales, and assumes different givens.
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  45.  7
    Ann-Sophie Barwich (2014). Fiction in Science? Exploring the Reality of Theoretical Entities. In Javier Cumpa, Greg Jesson & Guido Bonino (eds.), Defending Realism: Ontological and Epistemological Investigations. De Gruyter 291-310.
    This paper revisits the concept of fiction employed in recent debates about the reality of theoretical entities in the philosophy of science. From an anti-realist perspective the dependence of evidence for some scientific entities on mediated forms of observation and modelling strategies reflects a degree of construction that is argued to closely resemble fiction. As a realist’s response to this debate, this paper provides an analysis of fictional entities in comparison to real ones. I argue that the distinction between (...)
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  46. William Bechtel & Adele A. Abrahamsen (2013). Thinking Dynamically About Biological Mechanisms: Networks of Coupled Oscillators. [REVIEW] Foundations of Science 18 (4):707-723.
    Explaining the complex dynamics exhibited in many biological mechanisms requires extending the recent philosophical treatment of mechanisms that emphasizes sequences of operations. To understand how nonsequentially organized mechanisms will behave, scientists often advance what we call dynamic mechanistic explanations. These begin with a decomposition of the mechanism into component parts and operations, using a variety of laboratory-based strategies. Crucially, the mechanism is then recomposed by means of computational models in which variables or terms in differential equations correspond to properties (...)
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  47.  5
    Ben Meijering, Niels A. Taatgen, Hedderik van Rijn & Rineke Verbrugge (2014). Modeling Inference of Mental States: As Simple as Possible, as Complex as Necessary. Interaction Studies 15 (3):455-477.
    Behavior oftentimes allows for many possible interpretations in terms of mental states, such as goals, beliefs, desires, and intentions. Reasoning about the relation between behavior and mental states is therefore considered to be an effortful process. We argue that people use simple strategies to deal with high cognitive demands of mental state inference. To test this hypothesis, we developed a computational cognitive model, which was able to simulate previous empirical findings: In two-player games, people apply simple strategies at (...)
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  48.  23
    Chiara Lisciandra, Robustness Analysis and Tractability in Modeling.
    In the philosophy of science and epistemology literature, robustness analysis has become an umbrella term that refers to a variety of strategies. One of the main purposes of this paper is to argue that different strategies rely on different criteria for justifications. More specifically, I will claim that: i) robustness analysis differs from de-idealization even though the two concepts have often been conflated in the literature; ii) the comparison of different model frameworks requires different justifications than the comparison (...)
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  49.  6
    Serge Robert & Janie Brisson (2016). The Klein Group, Squares of Opposition and the Explanation of Fallacies in Reasoning. Logica Universalis 10 (2-3):377-392.
    During the last decades, the psychology of reasoning has identified experimentally many fallacies committed by spontaneous reasoners. Given these experimental results, some theories have been developed about this phenomenon, mainly algorithmic theories. This paper develops instead a computational modelling of these current fallacies which appear as simplifications in the treatment of information that do not respect the formal rules of classical propositional logic. These fallacies are explained as crushes in the Klein group structure and so, in squares of opposition. These (...)
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    Wendell Wallach, Colin Allen & Iva Smit (2007). Machine Morality: Bottom-Up and Top-Down Approaches for Modelling Human Moral Faculties. [REVIEW] AI and Society 22 (4):565-582.
    The implementation of moral decision making abilities in artificial intelligence (AI) is a natural and necessary extension to the social mechanisms of autonomous software agents and robots. Engineers exploring design strategies for systems sensitive to moral considerations in their choices and actions will need to determine what role ethical theory should play in defining control architectures for such systems. The architectures for morally intelligent agents fall within two broad approaches: the top-down imposition of ethical theories, and the bottom-up building (...)
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