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Catherine Greene
London School of Economics
  1.  17
    Nomadic Concepts, Variable Choice, and the Social Sciences.Catherine Greene - 2020 - Philosophy of the Social Sciences 50 (1):3-22.
    The observation that concepts used by social scientists are often problematic is not new; they have been described as Ballung concepts, cluster concepts, essentially contested, and reflexive; however, the need to work with these concepts remains. This article addresses the problem of variable choice in the social sciences by exploring and extending Woodward’s recommendations. This article demonstrates why Woodward’s criteria are difficult to apply in the social sciences and proposes an alternative, but complementary, framework for assessing variables.
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  2.  4
    Information in Financial Markets.Catherine Greene - 2019 - In Mark Addis, Fernand Gobet & Peter Sozou (eds.), Scientific Discovery in the Social Sciences. Springer Verlag.
    The concept of ‘information’ is central to our understanding of financial markets, both in theory and in practice. Analysing information is not only a critical part of the activities of many financial practitioners, but also plays a central role in the Efficient Market Hypothesis (EMH). The central claim of this paper is that different data can count as information in fi-nancial markets and that particular investors do not consider all of the available data. This suggests that firstly, saying the price (...)
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  3.  26
    Mind the Gap: Virtue Ethics and the Financial Crisis.Catherine Greene - 2018 - Midwest Studies in Philosophy 42 (1):174-190.
    The financial crisis has led to calls for increased regulation of the financial sector. In many respects this is uncontroversial because increased regulation should promote the behaviours we want to see, while limiting the behaviours we do not. This article takes issue with the idea that regulation, and guidelines, promote ethical behaviour in the way that we want them to. Firstly, judgement is often required to implement guidelines and regulations, which allows room for unethical behaviour. Secondly, we want financial professionals (...)
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  4.  10
    AI and the Social Sciences: Why All Variables Are Not Created Equal.Catherine Greene - 2022 - Res Publica 1:1-17.
    This article argues that it is far from trivial to convert social science concepts into accurate categories on which algorithms work best. The literature raises this concern in a general way; for example, Deeks notes that legal concepts, such as proportionality, cannot be easily converted into code noting that ‘The meaning and application of these concepts is hotly debated, even among lawyers who share common vocabularies and experiences’ (Deeks in Va Law Rev 104, pp. 1529–1593, 2018). The example discussed here (...)
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  5.  28
    Historical Counterfactuals, Transition Periods, and the Constraints on Imagination.Catherine Greene - 2021 - Hopos: The Journal of the International Society for the History of Philosophy of Science 11 (1):305-323.
    Counterfactual analysis is an interesting feature of thought experiments, because it requires the imagination of alternative states of the world (see also publications by Fearon, Lebow and Stein, Reiss, and Tetlock and Belkin, who suggest the same). In historical analysis, the use of imagination is often the focus of criticisms of such counterfactual analysis. In this article, I consider three strategies for constraining imagination: making limited counterfactual changes, limiting counterfactual changes to the decisions of important figures, and using evidence to (...)
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  6.  19
    Differential Information, Arbitrage, and Subjective Value.Catherine Greene - 2019 - Topoi 1:1-9.
    de Bruin et al. (The Stanford Encyclopedia of Philosophy, Stanford University, Stanford, 2018) write that it is a philosophically interesting question “whether there is such a thing as an 'intrinsic' value of financial assets” noting that the calculation of any intrinsic price will depend, in part, on subjective elements. McCauley suggest that there are at least five different notions of the ‘true value’ of an asset in finance theory, and argues, consistent with de Bruin et al. that in many cases (...)
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  7. Differential Information, Arbitrage, and Subjective Value.Catherine Greene - 2021 - Topoi 40 (4):745-753.
    de Bruin et al. The Stanford encyclopedia of philosophy, Stanford University, Stanford, 2018) write that it is a philosophically interesting question “whether there is such a thing as an “intrinsic” value of financial assets” noting that the calculation of any intrinsic price will depend, in part, on subjective elements. McCauley suggest that there are at least five different notions of the ‘true value’ of an asset in finance theory, and argues, consistent with de Bruin et al. that in many cases (...)
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  8.  7
    Laws in the Social Sciences.Catherine Greene - 2017 - Dissertation, London School of Economics and Political Science
    The social sciences are often thought to be inferior to the natural sciences because they do not have laws. Bohman writes that “the social sciences have never achieved much in the way of predictive general laws—the hallmark of naturalistic knowledge—and so have often been denied the honorific status of ‘sciences’” (1994, pg. vii). Philosophers have suggested a number of reasons for the dearth of laws in the social sciences, including the frequent use of ceteris paribus conditions in the social sciences, (...)
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  9.  3
    A Crisis of Beliefs: Investor Psychology and Financial Fragility, by Nicola Gennaioli and Andrei Shleifer. Princeton, NJ: Princeton University Press, 2018. 264 Pp. [REVIEW]Catherine Greene - 2020 - Business Ethics Quarterly 30 (4):613-616.
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  10.  1
    Big Data and the Reference Class Problem: What Can We Legitimately Infer About Individuals.Catherine Greene - 2019 - Computer Ethics- Philosophical Inquiry (CEPE) Proceedings 1 (2019).
    Big data increasingly enables prediction of the behaviour and characteristics of individuals. This is ethically concerning on privacy grounds. However, this article discusses other reasons for concern. These predictions usually rely on generalisations about what certain sorts of people tend to do. Generalisations of this sort are often under scrutiny in legal cases, where, for example, lawyers argue that people with prior convictions are more likely to be guilty of the crime they are currently on trial for. This article applies (...)
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