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Chiara Lisciandra
Tilburg University
  1. Disagreement Behind the Veil of Ignorance.Ryan Muldoon, Chiara Lisciandra, Mark Colyvan, Carlo Martini, Giacomo Sillari & Jan Sprenger - 2014 - Philosophical Studies 170 (3):377-394.
    In this paper we argue that there is a kind of moral disagreement that survives the Rawlsian veil of ignorance. While a veil of ignorance eliminates sources of disagreement stemming from self-interest, it does not do anything to eliminate deeper sources of disagreement. These disagreements not only persist, but transform their structure once behind the veil of ignorance. We consider formal frameworks for exploring these differences in structure between interested and disinterested disagreement, and argue that consensus models offer us a (...)
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  2.  50
    On the Emergence of Descriptive Norms.Ryan Muldoon, Chiara Lisciandra, Cristina Bicchieri, Stephan Hartmann & Jan Sprenger - 2014 - Politics, Philosophy and Economics 13 (1):3-22.
    A descriptive norm is a behavioral rule that individuals follow when their empirical expectations of others following the same rule are met. We aim to provide an account of the emergence of descriptive norms by first looking at a simple case, that of the standing ovation. We examine the structure of a standing ovation, and show it can be generalized to describe the emergence of a wide range of descriptive norms.
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    Robustness Analysis and Tractability in Modeling.Chiara Lisciandra - 2017 - European Journal for Philosophy of Science 7 (1):79-95.
    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 of models (...)
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    Conformorality. A Study on Group Conditioning of Normative Judgment.Chiara Lisciandra, Marie Postma-Nilsenová & Matteo Colombo - 2013 - Review of Philosophy and Psychology (4):751-764.
    How does other people’s opinion affect judgments of norm transgressions? In our study, we used a modification of the famous Asch paradigm to examine conformity in the moral domain. The question we addressed was how peer group opinion alters normative judgments of scenarios involving violations of moral, social, and decency norms. The results indicate that even moral norms are subject to conformity, especially in situations with a high degree of social presence. Interestingly, the degree of conformity can distinguish between different (...)
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    Why Are There Descriptive Norms? Because We Looked for Them.Ryan Muldoon, Chiara Lisciandra & Stephan Hartmann - 2014 - Synthese 191 (18):4409-4429.
    In this work, we present a mathematical model for the emergence of descriptive norms, where the individual decision problem is formalized with the standard Bayesian belief revision machinery. Previous work on the emergence of descriptive norms has relied on heuristic modeling. In this paper we show that with a Bayesian model we can provide a more general picture of the emergence of norms, which helps to motivate the assumptions made in heuristic models. In our model, the priors formalize the belief (...)
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    Editorial: Formal Epistemology Meets Experimental Philosophy. [REVIEW]Stephan Hartmann, Chiara Lisciandra & Edouard Machery - 2013 - Synthese 190 (8):1333-1335.
  7.  7
    The Role of Psychology in Behavioral Economics: The Case of Social Preferences.Chiara Lisciandra - 2018 - Studies in History and Philosophy of Science Part A 72:11-21.
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    Robustness Analysis Versus Reliable Process Reasoning.Chiara Lisciandra - 2015 - Metascience 24 (1):37-41.
    Robert Hudson’s book is a contribution to the recent debate on robustness analysis in scientific practice, with a specific focus on the empirical sciences. In this context, robustness analysis is defined as a way to increase the probability of a certain hypothesis by showing that the same result is obtained from several, alternative methods. The rationale underlying this practice is that it would be highly unlikely if different, independent means of observation provided the same wrong outcome.We do not believe in (...)
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