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Profile: Ryan Muldoon (University of Pennsylvania)
  1.  33
    Ryan Muldoon, Chiara Lisciandra, Mark Colyvan, Carlo Martini, Giacomo Sillari & Jan Sprenger (2014). Disagreement Behind the Veil of Ignorance. 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. Michael Weisberg & Ryan Muldoon (2009). Epistemic Landscapes and the Division of Cognitive Labor. Philosophy of Science 76 (2):225-252.
    Because of its complexity, contemporary scientific research is almost always tackled by groups of scientists, each of which works in a different part of a given research domain. We believe that understanding scientific progress thus requires understanding this division of cognitive labor. To this end, we present a novel agent-based model of scientific research in which scientists divide their labor to explore an unknown epistemic landscape. Scientists aim to climb uphill in this landscape, where elevation represents the significance of the (...)
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  3.  21
    Ryan Muldoon (2013). Diversity and the Division of Cognitive Labor. Philosophy Compass 8 (2):117-125.
    In epistemology and the philosophy of science, there has been an increasing interest in the social aspects of belief acquisition. In particular, there has been a focus on the division of cognitive labor in science. This essay explores several different models of the division of cognitive labor, with particular focus on Kitcher, Strevens, Weisberg and Muldoon, and Zollman. The essay then shows how many of the benefits of the division of cognitive labor flow from leveraging agent diversity. The essay concludes (...)
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  4.  35
    Ryan Muldoon, Tony Smith & Michael Weisberg (2012). Segregation That No One Seeks. Philosophy of Science 79 (1):38-62.
    This paper examines a series of Schelling-like models of residential segregation, in which agents prefer to be in the minority. We demon- strate that as long as agents care about the characteristics of their wider community, they tend to end up in a segregated state. We then investigate the process that causes this, and conclude that the result hinges on the similarity of informational states amongst agents of the same type. This is quite di erent from Schelling-like behavior, and sug- (...)
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  5.  6
    Ryan Muldoon (forthcoming). Decision-Making Made Simple. Metascience:1-3.
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  6.  91
    Ryan Muldoon & Michael Weisberg (2011). Robustness and Idealization in Models of Cognitive Labor. Synthese 183 (2):161-174.
    Scientific research is almost always conducted by communities of scientists of varying size and complexity. Such communities are effective, in part, because they divide their cognitive labor: not every scientist works on the same project. Philip Kitcher and Michael Strevens have pioneered efforts to understand this division of cognitive labor by proposing models of how scientists make decisions about which project to work on. For such models to be useful, they must be simple enough for us to understand their dynamics, (...)
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  7.  47
    Ryan Muldoon, Michael Borgida & Michael Cuffaro (2012). The Conditions of Tolerance. Politics, Philosophy and Economics 11 (3):322-344.
    The philosophical tradition of liberal political thought has come to see tolerance as a crucial element of a liberal political order. However, while much has been made of the value of toleration, little work has been done on individual-level motivations for tolerant behavior. In this article, we seek to develop an account of the rational motivations for toleration and of where the limits of toleration lie. We first present a very simple model of rational motivations for toleration. Key to this (...)
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  8.  30
    Ryan Muldoon (2007). Robust Simulations. Philosophy of Science 74 (5):873-883.
    As scientists begin to study increasingly complex questions, many have turned to computer simulation to assist in their inquiry. This methodology has been challenged by both analytic modelers and experimentalists. A primary objection of analytic modelers is that simulations are simply too complicated to perform model verification. From the experimentalist perspective it is that there is no means to demonstrate the reality of simulation. The aim of this paper is to consider objections from both of these perspectives, and to argue (...)
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  9.  62
    Cristina Bicchieri & Ryan Muldoon, Social Norms.
  10.  15
    Ryan Muldoon (2015). Expanding the Justificatory Framework of Mill's Experiments in Living. Utilitas 27 (2):179-194.
    In On Liberty, Mill introduced the concept of . I will provide an account of what Mill saw to be the basic problem he was addressing – the extensive pressure to fit in with the crowd, and how this bred mediocrity. I connect this to worries about public reason models of justification. I argue that a generalized version of Mill's argument offers us a better path to political justification stemming from experimentation. Rather than grounding political justification on shared political reasons, (...)
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  11.  15
    Ryan Muldoon (2013). Evolution and Rationality: Decisions, Co-Operation and Strategic Behaviour, Samir Okasha and Ken Binmore (Eds.). Cambridge University Press, 2012, X + 281 Pages. [REVIEW] Economics and Philosophy 29 (3):425-430.
  12.  12
    Ryan Muldoon, Chiara Lisciandra & Stephan Hartmann (2014). Why Are There Descriptive Norms? Because We Looked for Them. 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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  13.  9
    Cyrille Imbert, Ryan Muldoon, Jan Sprenger & Kevin Zollman (2014). Introduction, SI of Synthese “The Collective Dimension of Science”. Synthese 191 (1):1-2.
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  14. J. Christopher Hunt, Kareem Khalifa, Ryan Muldoon, Tony Smith, Michael Weisberg, Michelle G. Gibbons, Elliott O. Wagner, Andreas Wagner, Angela Potochnik & Brian McGill (2012). 1. On Ad Hoc Hypotheses On Ad Hoc Hypotheses (Pp. 1-14). Philosophy of Science 79 (1).
     
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  15. Ryan Muldoon (2016). Social Contract Theory for a Diverse World: Beyond Tolerance. Routledge.
    Very diverse societies pose real problems for Rawlsian models of public reason. This is for two reasons: first, public reason is unable accommodate diverse perspectives in determining a regulative ideal. Second, regulative ideals are unable to respond to social change. While models based on public reason focus on the justification of principles, this book suggests that we need to orient our normative theories more toward discovery and experimentation. The book develops a unique approach to social contract theory that focuses on (...)
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