385 research outputs found

    Mainstream economics and the Austrian school: toward reunification

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    In this paper, I compare the methodology of the Austrian school to two alternative methodologies from the economic mainstream: the ‘orthodox’ and revealed preference methodologies. I argue that Austrian school theorists should stop describing themselves as ‘extreme apriorists’ (or writing suggestively to that effect), and should start giving greater acknowledgement to the importance of empirical work within their research program. The motivation for this dialectical shift is threefold: the approach is more faithful to their actual practices, it better illustrates the underlying similarities between the mainstream and Austrian research paradigms, and it provides a philosophical foundation that is much more plausible in itself

    The right way to play a game

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    Is there a right or wrong way to play a game? Many think not. Some have argued that, when we insist that players obey the rules of a game, we give too much weight to the author’s intent. Others have argued that such obedience to the rules violates the true purpose of games, which is fostering free and creative play. Both of these responses, I argue, misunderstand the nature of games and their rules. The rules do not tell us how to interpret a game; they merely tell us what the game is. And the point of the rules is not always to foster free and creative play. The point can be, instead, to communicate a sculpted form of activity. And in games, as with any form of communication, we need some shared norms to ground communicative stability. Games have what has been called a “prescriptive ontology.” A game is something more than simply a piece of material. It is some material as approached in a certain specified way. These prescriptions help to fix a common object of attention. Games share this prescriptive ontology with more traditional kinds of works. Novels are more than just a set of words on a page; they are those words read in a certain order. Games are more than just some software or cardboard bits; they are those bits interacted with according to certain rules. Part of a game’s essential nature is the prescriptions for how we are to play it. What’s more, we investigate the prescriptive ontology of games, we will uncover at least distinct prescriptive categories of games. Party games prescribe that we encounter the game once; heavy strategy games prescribe we encounter the game many times; and community evolution games prescribe that we encounter the game while embedded in an ongoing community of play

    Information and Design: Book Symposium on Luciano Floridi’s The Logic of Information

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    Purpose – To review and discuss Luciano Floridi’s 2019 book The Logic of Information: A Theory of Philosophy as Conceptual Design, the latest instalment in his philosophy of information (PI) tetralogy, particularly with respect to its implications for library and information studies (LIS). Design/methodology/approach – Nine scholars with research interests in philosophy and LIS read and responded to the book, raising critical and heuristic questions in the spirit of scholarly dialogue. Floridi responded to these questions. Findings – Floridi’s PI, including this latest publication, is of interest to LIS scholars, and much insight can be gained by exploring this connection. It seems also that LIS has the potential to contribute to PI’s further development in some respects. Research implications – Floridi’s PI work is technical philosophy for which many LIS scholars do not have the training or patience to engage with, yet doing so is rewarding. This suggests a role for translational work between philosophy and LIS. Originality/value – The book symposium format, not yet seen in LIS, provides forum for sustained, multifaceted and generative dialogue around ideas

    Acquisition of Chess Knowledge in AlphaZero

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    What is learned by sophisticated neural network agents such as AlphaZero? This question is of both scientific and practical interest. If the representations of strong neural networks bear no resemblance to human concepts, our ability to understand faithful explanations of their decisions will be restricted, ultimately limiting what we can achieve with neural network interpretability. In this work we provide evidence that human knowledge is acquired by the AlphaZero neural network as it trains on the game of chess. By probing for a broad range of human chess concepts we show when and where these concepts are represented in the AlphaZero network. We also provide a behavioural analysis focusing on opening play, including qualitative analysis from chess Grandmaster Vladimir Kramnik. Finally, we carry out a preliminary investigation looking at the low-level details of AlphaZero's representations, and make the resulting behavioural and representational analyses available online.Comment: 69 pages, 44 figure

    Metaphor and Metanoia: Linguistic Transfer and Cognitive Transformation in British and Irish Modernism

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    This dissertation contributes to the critical expansions that Douglas Mao and Rebecca L. Walkowitz identify as New Modernist Studies. This expansion is temporal, spatial, and vertical. I engage with the effects Modernist texts have “above” the page: lived experience. I examine the structural similarity of linguistic metaphor and the mind as considered by cognitive scientists. Identifying the human mind as linguistic and language as an artifact of the human mind, my research extrapolates upon what I call the “psycho-ecology” of reading, a self-representational knot between text and mind that constitutes lived experience. Far from being an abstraction, psycho-ecology is concrete: atypical textual engagement is equated with a transformation in perception. The prologue traces a lineage between Modernism, phenomenology, and the cognitive sciences. The first chapter considers the relationship between two narrative levels in Oscar Wilde’s novel The Picture of Dorian Gray (1890). The second chapter considers temporal experimentation in Virginia Woolf’s novel To the Lighthouse (1927) in relation to Martin Heidegger’s formulation of being as that which discloses our experience with language as temporal and finite. The third chapter examines the “sentimental information” of James Joyce’s Finnegans Wake (1939) from a phenomenological approach to information theory. The final chapter analyzes Samuel Beckett’s Endgame (1957) as a zero-player game that discloses the limits of agency in psycho-ecology. The dissertation follows a trajectory beginning with the intimacy a reader has with alphanumeric text towards the increasing experience of illiteracy when encountering new languages such as digital code

    Artificial intelligence and musical creativity : computing Beethoven's tenth

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    Thesis (S.M. in Science Writing)--Massachusetts Institute of Technology, Dept. of Humanities, Program in Writing and Humanistic Studies, 2003.Includes bibliographical references (p. 47-48).by Matthew T. Hutson.S.M.in Science Writin

    Competing fantasies of humans and machines: Symbolic convergences in artificial intelligence events coverage

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    This research analyzes coverage of major artificial intelligence events representing the thematic concept of "man versus machine." Rooted in grounded theory and rhetorical criticism, this research applies symbolic convergence theory and fantasy theme analysis to reporting from The New York Times, The Wall Street Journal and The Washington Post immediately surrounding three cultural and scientific milestones in the development of artificial intelligence technology: IBM Deep Blue's 1997 defeat of chess grandmaster Garry Kasparov; IBM Watson's 2011 defeat of Jeopardy! champions Ken Jennings and Brad Rutter; and Google DeepMind AlphaGo's 2016 defeat of Lee Sedol. This research analyzes how symbolic realities are dramatized in the context of these events such that the competitions themselves represent ideological battles between humanism or technological superiority. This research also demonstrates subtle variations in how fantasy themes and rhetorical visions manifest in coverage from each outlet, amounting to what is effectively a competition for shared consciousness between these two competing ideological constructs

    Efficient instance and hypothesis space revision in Meta-Interpretive Learning

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    Inductive Logic Programming (ILP) is a form of Machine Learning. The goal of ILP is to induce hypotheses, as logic programs, that generalise training examples. ILP is characterised by a high expressivity, generalisation ability and interpretability. Meta-Interpretive Learning (MIL) is a state-of-the-art sub-field of ILP. However, current MIL approaches have limited efficiency: the sample and learning complexity respectively are polynomial and exponential in the number of clauses. My thesis is that improvements over the sample and learning complexity can be achieved in MIL through instance and hypothesis space revision. Specifically, we investigate 1) methods that revise the instance space, 2) methods that revise the hypothesis space and 3) methods that revise both the instance and the hypothesis spaces for achieving more efficient MIL. First, we introduce a method for building training sets with active learning in Bayesian MIL. Instances are selected maximising the entropy. We demonstrate this method can reduce the sample complexity and supports efficient learning of agent strategies. Second, we introduce a new method for revising the MIL hypothesis space with predicate invention. Our method generates predicates bottom-up from the background knowledge related to the training examples. We demonstrate this method is complete and can reduce the learning and sample complexity. Finally, we introduce a new MIL system called MIGO for learning optimal two-player game strategies. MIGO learns from playing: its training sets are built from the sequence of actions it chooses. Moreover, MIGO revises its hypothesis space with Dependent Learning: it first solves simpler tasks and can reuse any learned solution for solving more complex tasks. We demonstrate MIGO significantly outperforms both classical and deep reinforcement learning. The methods presented in this thesis open exciting perspectives for efficiently learning theories with MIL in a wide range of applications including robotics, modelling of agent strategies and game playing.Open Acces
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