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Jan Sprenger [44]J. Sprenger [2]
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Profile: Jan Sprenger (Tilburg University)
  1.  43
    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.  97
    Jonah N. Schupbach & Jan Sprenger (2011). The Logic of Explanatory Power. Philosophy of Science 78 (1):105-127.
    This article introduces and defends a probabilistic measure of the explanatory power that a particular explanans has over its explanandum. To this end, we propose several intuitive, formal conditions of adequacy for an account of explanatory power. Then, we show that these conditions are uniquely satisfied by one particular probabilistic function. We proceed to strengthen the case for this measure of explanatory power by proving several theorems, all of which show that this measure neatly corresponds to our explanatory intuitions. Finally, (...)
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  3. Carlo Martini, Jan Sprenger & Mark Colyvan (2013). Resolving Disagreement Through Mutual Respect. Erkenntnis 78 (4):881-898.
    This paper explores the scope and limits of rational consensus through mutual respect, with the primary focus on the best known formal model of consensus: the Lehrer–Wagner model. We consider various arguments against the rationality of the Lehrer–Wagner model as a model of consensus about factual matters. We conclude that models such as this face problems in achieving rational consensus on disagreements about unknown factual matters, but that they hold considerable promise as models of how to rationally resolve non-factual disagreements.
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  4. Stephan Hartmann & Jan Sprenger (2012). Judgment Aggregation and the Problem of Tracking the Truth. Synthese 187 (1):209-221.
    The aggregation of consistent individual judgments on logically interconnected propositions into a collective judgment on those propositions has recently drawn much attention. Seemingly reasonable aggregation procedures, such as propositionwise majority voting, cannot ensure an equally consistent collective conclusion. The literature on judgment aggregation refers to that problem as the discursive dilemma. In this paper, we motivate that many groups do not only want to reach a factually right conclusion, but also want to correctly evaluate the reasons for that conclusion. In (...)
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  5. Jan Sprenger (2010). Probability, Rational Single-Case Decisions and the Monty Hall Problem. Synthese 174 (3):331-340.
    The application of probabilistic arguments to rational decisions in a single case is a contentious philosophical issue which arises in various contexts. Some authors (e.g. Horgan, Philos Pap 24:209–222, 1995; Levy, Synthese 158:139–151, 2007) affirm the normative force of probabilistic arguments in single cases while others (Baumann, Am Philos Q 42:71–79, 2005; Synthese 162:265–273, 2008) deny it. I demonstrate that both sides do not give convincing arguments for their case and propose a new account of the relationship between probabilistic reasoning (...)
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  6.  99
    Stephan Hartmann, Gabriella Pigozzi & Jan Sprenger (2010). Reliable Methods of Judgment Aggregation. Journal for Logic and Computation 20:603--617.
    The aggregation of consistent individual judgments on logically interconnected propositions into a collective judgment on the same propositions has recently drawn much attention. Seemingly reasonable aggregation procedures, such as propositionwise majority voting, cannot ensure an equally consistent collective conclusion. The literature on judgment aggregation refers to such a problem as the \textit{discursive dilemma}. In this paper we assume that the decision which the group is trying to reach is factually right or wrong. Hence, we address the question of how good (...)
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  7.  36
    J. Sprenger & R. Heesen (2011). The Bounded Strength of Weak Expectations. Mind 120 (479):819-832.
    The rational price of the Pasadena and Altadena games, introduced by Nover and Hájek (2004 ), has been the subject of considerable discussion. Easwaran (2008 ) has suggested that weak expectations — the value to which the average payoffs converge in probability — can give the rational price of such games. We argue against the normative force of weak expectations in the standard framework. Furthermore, we propose to replace this framework by a bounded utility perspective: this shift renders the problem (...)
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  8.  56
    Jan Sprenger (2013). A Synthesis of Hempelian and Hypothetico-Deductive Confirmation. Erkenntnis 78 (4):727-738.
    This paper synthesizes confirmation by instances and confirmation by successful predictions, and thereby the Hempelian and the hypothetico-deductive traditions in confirmation theory. The merger of these two approaches is subsequently extended to the piecemeal confirmation of entire theories. It is then argued that this synthetic account makes a useful contribution from both a historical and a systematic perspective.
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  9.  65
    Richard Dawid, Stephan Hartmann & Jan Sprenger (2015). The No Alternatives Argument. British Journal for the Philosophy of Science 66 (1):213-234.
    Scientific theories are hard to find, and once scientists have found a theory, H, they often believe that there are not many distinct alternatives to H. But is this belief justified? What should scientists believe about the number of alternatives to H, and how should they change these beliefs in the light of new evidence? These are some of the questions that we will address in this article. We also ask under which conditions failure to find an alternative to H (...)
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  10. Jan Sprenger, Falsificationist Confirmation.
    Existing accounts of hypothetico-deductive confirmation are able to circumvent the classical objections, but the confirmation of conjunctions of hypotheses brings them into trouble. Therefore this paper develops a new, falsificationist account of qualitative confirmation by means of Ken Gemes ' theory of content parts. The new approach combines the hypothetico-deductive view with falsificationist and instance confirmation principles. It is considerably simpler than the previous suggestions and gives a better treatment of conjunctive hypotheses while solving the tacking problems equally well.
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  11.  26
    Jan Sprenger (2015). A Novel Solution to the Problem of Old Evidence. Philosophy of Science 82 (3):383-401.
    One of the most troubling and persistent challenges for Bayesian Confirmation Theory is the Problem of Old Evidence. The problem arises for anyone who models scientific reasoning by means of Bayesian Conditionalization. This article addresses the problem as follows: First, I clarify the nature and varieties of the POE and analyze various solution proposals in the literature. Second, I present a novel solution that combines previous attempts while making weaker and more plausible assumptions. Third and last, I summarize my findings (...)
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  12.  27
    Jan Sprenger (2013). Testing a Precise Null Hypothesis: The Case of Lindley’s Paradox. Philosophy of Science 80 (5):733-744.
    The interpretation of tests of a point null hypothesis against an unspecified alternative is a classical and yet unresolved issue in statistical methodology. This paper approaches the problem from the perspective of Lindley's Paradox: the divergence of Bayesian and frequentist inference in hypothesis tests with large sample size. I contend that the standard approaches in both frameworks fail to resolve the paradox. As an alternative, I suggest the Bayesian Reference Criterion: it targets the predictive performance of the null hypothesis in (...)
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  13.  43
    Stephan Hartmann & Jan Sprenger (2010). The Weight of Competence Under a Realistic Loss Function. Logic Journal of the IGPL 18 (2):346-352.
    In many scientific, economic and policy-related problems, pieces of information from different sources have to be aggregated. Typically, the sources are not equally competent. This raises the question of how the relative weights and competences should be related to arrive at an optimal final verdict. Our paper addresses this question under a more realistic perspective of measuring the practical loss implied by an inaccurate verdict.
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  14.  71
    Jan Sprenger, Carlo Martini & Stephan Hartmann (2009). Consensual Decision-Making Among Epistemic Peers. Episteme 6 (2):110-129.
    This paper focuses on the question of how to resolve disagreement and uses the Lehrer-Wagner model as a formal tool for investigating consensual decision-making. The main result consists in a general definition of when agents treat each other as epistemic peers (Kelly 2005; Elga 2007), and a theorem vindicating the “equal weight view” to resolve disagreement among epistemic peers. We apply our findings to an analysis of the impact of social network structures on group deliberation processes, and we demonstrate their (...)
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  15.  37
    R. Muldoon, C. Lisciandra, C. Bicchieri, S. Hartmann & J. Sprenger (2014). On the Emergence of Descriptive Norms. 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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  16. Jan Sprenger, Surprise and Evidence in Statistical Model Checking.
    There is considerable confusion about the role of p-values in statistical model checking. To clarify that point, I introduce the distinction between measures of surprise and measures of evidence which come with different epistemological functions. I argue that p-values, often understood as measures of evidence against a null model, do not count as proper measures of evidence and are closer to measures of surprise. Finally, I sketch how the problem of old evidence may be tackled by acknowledging the epistemic role (...)
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  17.  53
    Matteo Colombo, Marie Postma & Jan Sprenger, Explanatory Value and Probabilistic Reasoning: An Empirical Study.
    The relation between probabilistic and explanatory reasoning is a classical topic in philosophy of science. Most philosophical analyses are concerned with the compatibility of Inference to the Best Explanation with probabilistic, Bayesian inference, and the impact of explanatory considerations on the assignment of subjective probabilities. This paper reverses the question and asks how causal and explanatory considerations are affected by probabilistic information. We investigate how probabilistic information determines the explanatory value of a hypothesis, and in which sense folk explanatory practice (...)
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  18.  35
    Jan Sprenger, The Objectivity of Subjective Bayesian Inference.
    Subjective Bayesianism is a major school of uncertain reasoning and statistical inference. Yet, it is often criticized for an apparent lack of objectivity. By and large, these criticisms come in three different forms. First, the lack of constraints on prior probabilities, second, the entanglement of statistical evidence and degree of belief, third, the apparent blindness to bias in experimental design. This paper responds to the above criticisms and argues in addition that frequentist statistics is no more objective than Bayesian statistics. (...)
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  19.  17
    Matteo Colombo & Jan Sprenger (2014). The Predictive Mind and Chess-Playing: A Reply to Shand. Analysis 74 (4):603-608.
    In a recent Analysis piece, John Shand (2014) argues that the Predictive Theory of Mind provides a unique explanation for why one cannot play chess against oneself. On the basis of this purported explanatory power, Shand concludes that we have an extra reason to believe that PTM is correct. In this reply, we first rectify the claim that one cannot play chess against oneself; then we move on to argue that even if this were the case, Shand’s argument does not (...)
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  20.  36
    Jan Sprenger (2011). Hypothetico-Deductive Confirmation. Philosophy Compass 6 (7):497-508.
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  21.  54
    Jan Sprenger (2011). Science Without (Parametric) Models: The Case of Bootstrap Resampling. Synthese 180 (1):65 - 76.
    Scientific and statistical inferences build heavily on explicit, parametric models, and often with good reasons. However, the limited scope of parametric models and the increasing complexity of the studied systems in modern science raise the risk of model misspecification. Therefore, I examine alternative, data-based inference techniques, such as bootstrap resampling. I argue that their neglect in the philosophical literature is unjustified: they suit some contexts of inquiry much better and use a more direct approach to scientific inference. Moreover, they make (...)
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  22.  30
    Jan Sprenger, Conditional Degree of Belief.
    It is a commonplace in epistemology that credences should equal known chances. It is less clear, however, that conditional credences should do so, too. Following Ramsey, this paper proposes a counterfactual interpretation of conditional probability which provides a justification for this equality without relying on the Principal Principle. As a result, we obtain a refined view of Bayesian inference where both learning and supposing have a place.
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  23.  41
    Jan Sprenger (2016). The Probabilistic No Miracles Argument. European Journal for Philosophy of Science 6 (2):173-189.
    This paper develops a probabilistic reconstruction of the No Miracles Argument in the debate between scientific realists and anti-realists. The goal of the paper is to clarify and to sharpen the NMA by means of a probabilistic formalization. In particular, I demonstrate that the persuasive force of the NMA depends on the particular disciplinary context where it is applied, and the stability of theories in that discipline. Assessments and critiques of “the” NMA, without reference to a particular context, are misleading (...)
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  24.  14
    Matteo Colombo, Marie Postma & Jan Sprenger, Explanatory Judgment, Probability, and Abductive Inference.
    Abductive reasoning assigns special status to the explanatory power of a hypothesis. But how do people make explanatory judgments? Our study clarifies this issue by asking: How does the explanatory power of a hypothesis cohere with other cognitive factors? How does probabilistic information affect explanatory judgments? In order to answer these questions, we conducted an experiment with 671 participants. Their task was to make judgments about a potentially explanatory hypothesis and its cognitive virtues. In the responses, we isolated three constructs: (...)
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  25. Jan Sprenger (2009). Statistics Between Inductive Logic and Empirical Science. Journal of Applied Logic 7 (2):239--250.
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  26.  18
    Jan Sprenger, Foundations for a Probabilistic Theory of Causal Strength.
    This paper develops axiomatic foundations for a probabilistic-interventionist theory of causal strength. Transferring methods from Bayesian confirmation theory, I proceed in three steps: I develop a framework for defining and comparing measures of causal strength; I argue that no single measure can satisfy all natural constraints; I prove two representation theorems for popular measures of causal strength: Pearl's causal effect measure and Eells' difference measure. In other words, I demonstrate these two measures can be derived from a set of plausible (...)
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  27.  46
    Jan Sprenger (2009). Evidence and Experimental Design in Sequential Trials. Philosophy of Science 76 (5):637-649.
    To what extent does the design of statistical experiments, in particular sequential trials, affect their interpretation? Should postexperimental decisions depend on the observed data alone, or should they account for the used stopping rule? Bayesians and frequentists are apparently deadlocked in their controversy over these questions. To resolve the deadlock, I suggest a three‐part strategy that combines conceptual, methodological, and decision‐theoretic arguments. This approach maintains the pre‐experimental relevance of experimental design and stopping rules but vindicates their evidential, postexperimental irrelevance. †To (...)
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  28.  55
    Rogier De Langhe, Stephan Hartmann & Jan Sprenger (2014). Introduction: The Progress of Science. Studies in History and Philosophy of Science Part A 46:54.
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  29. Stephan Hartmann & Jan Sprenger (forthcoming). Bayesian Epistemology. In Duncan Pritchard & Sven Bernecker (eds.), Routledge Companion to Epistemology. Routledge
    Bayesian epistemology addresses epistemological problems with the help of the mathematical theory of probability. It turns out that the probability calculus is especially suited to represent degrees of belief (credences) and to deal with questions of belief change, confirmation, evidence, justification, and coherence. Compared to the informal discussions in traditional epistemology, Bayesian epis- temology allows for a more precise and fine-grained analysis which takes the gradual aspects of these central epistemological notions into account. Bayesian epistemology therefore complements traditional epistemology; it (...)
     
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  30.  14
    Jan Sprenger (forthcoming). Two Impossibility Results for Measures of Corroboration. British Journal for the Philosophy of Science:axw016.
    According to influential accounts of scientific method, e.g., critical rationalism, scientific knowledge grows by repeatedly testing our best hypotheses. But despite the popularity of hypothesis tests in statistical inference and science in general, their philosophical foundations remain shaky. In particular, the interpretation of non-significant results---those that do not refute the tested hypothesis---poses a major philosophical challenge. To what extent do they corroborate the tested hypothesis or provide a reason to accept it? Karl R. Popper sought for measures of corroboration that (...)
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  31.  23
    Stephan Hartmann & Jan Sprenger (2011). Mathematics and Statistics in the Social Sciences. In Ian C. Jarvie & Jesus Zamora-Bonilla (eds.), The SAGE Handbook of the Philosophy of Social Sciences. Sage Publications 594-612.
  32.  3
    Jan Sprenger (forthcoming). Two Impossibility Results for Measures of Corroboration. British Journal for the Philosophy of Science:axw016.
    According to influential accounts of scientific method, such as critical rationalism, scientific knowledge grows by repeatedly testing our best hypotheses. But despite the popularity of hypothesis tests in statistical inference and science in general, their philosophical foundations remain shaky. In particular, the interpretation of non-significant results—those that do not reject the tested hypothesis—poses a major philosophical challenge. To what extent do they corroborate the tested hypothesis, or provide a reason to accept it? Popper sought for measures of corroboration that could (...)
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  33.  24
    Jan Sprenger (2010). Statistical Inference Without Frequentist Justifications. In M. Dorato M. Suàrez (ed.), Epsa Epistemology and Methodology of Science. Springer 289--297.
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  34.  19
    Jan Sprenger (2013). The Role of Bayesian Philosophy Within Bayesian Model Selection. European Journal for Philosophy of Science 3 (1):101-114.
    Bayesian model selection has frequently been the focus of philosophical inquiry (e.g., Forster, Br J Philos Sci 46:399–424, 1995; Bandyopadhyay and Boik, Philos Sci 66:S390–S402, 1999; Dowe et al., Br J Philos Sci 58:709–754, 2007). This paper argues that Bayesian model selection procedures are very diverse in their inferential target and their justification, and substantiates this claim by means of case studies on three selected procedures: MML, BIC and DIC. Hence, there is no tight link between Bayesian model selection and (...)
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  35.  6
    Jan Sprenger (2011). Science Without Models: The Case of Bootstrap Resampling. Synthese 180 (1):65-76.
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  36.  28
    Cecilia Nardini & Jan Sprenger (2013). Bias and Conditioning in Sequential Medical Trials. Philosophy of Science 80 (5):1053-1064.
    Randomized Controlled Trials are currently the gold standard within evidence-based medicine. Usually, they are conducted as sequential trials allowing for monitoring for early signs of effectiveness or harm. However, evidence from early stopped trials is often charged with being biased towards implausibly large effects. To our mind, this skeptical attitude is unfounded and caused by the failure to perform appropriate conditioning in the statistical analysis of the evidence. We contend that a shift from unconditional hypothesis tests in the style of (...)
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  37.  6
    Jan Sprenger, From Evidential Support to a Measure of Corroboration.
    According to influential accounts of scientific method, e.g., critical rationalism, scientific knowledge grows by repeatedly testing our best hypotheses. In comparison to rivaling accounts of scientific reasoning such as Bayesianism, these accounts are closer to crucial aspects of scientific practice. But despite the preeminence of hypothesis tests in statistical inference, their philosophical foundations are shaky. In particular, the interpretation of "insignificant results"---outcomes where the tested hypothesis has survived the test---poses a major epistemic challenge that is not sufficiently addressed by the (...)
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  38.  20
    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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  39.  6
    Dominik Klein & Jan Sprenger (2015). Modelling Individual Expertise in Group Judgements. Economics and Philosophy 31 (1):3-25.
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  40.  24
    Jan Sprenger (2012). Environmental Risk Analysis: Robustness Is Essential for Precaution. Philosophy of Science 79 (5):881-892.
  41.  14
    Stephan Hartmann & Jan Sprenger (2012). The Future of Philosophy of Science: Introduction. [REVIEW] European Journal for Philosophy of Science 2 (2):157-159.
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  42.  7
    Jan Sprenger (forthcoming). Stephan Hartmann, Carlo Martini. Episteme.
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  43.  3
    Stephan Hartmann, Carlo Martini & Jan Sprenger (2010). Editorial. Logic Journal of the IGPL 18 (2):277-277.
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  44.  7
    Carlo Martini, Jan Sprenger & Mark Colyvan (2014). Erratum To: Resolving Disagreement Through Mutual Respect. [REVIEW] Erkenntnis 79 (3):669-670.
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  45.  8
    Jan Sprenger (2012). A Unifying Framework of Probabilistic Reasoning. Metascience 21 (2):459-462.
    A unifying framework of probabilistic reasoning Content Type Journal Article Category Book Review Pages 1-4 DOI 10.1007/s11016-011-9573-x Authors Jan Sprenger, Tilburg Center for Logic and Philosophy of Science, Tilburg University, P.O. Box 90153, 5000 LE Tilburg, The Netherlands Journal Metascience Online ISSN 1467-9981 Print ISSN 0815-0796.
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  46. Stephan Hartmann, Carlo Martini & Jan Sprenger (eds.) (2010). Formal Modeling in Social Epistemology. [REVIEW] Logic Journal of the IGPL (Special Issue).
    Special issue. With contributions by Rogier De Langhe and Matthias Greiff, Igor Douven and Alexander Riegler, Stephan Hartmann and Jan Sprenger, Carl Wagner, Paul Weirich, and Jesús Zamora Bonilla.
     
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