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Teddy Seidenfeld [70]Teddy I. Seidenfeld [1]
  1.  87
    Coherent Choice Functions Under Uncertainty.Teddy Seidenfeld, Mark J. Schervish & Joseph B. Kadane - 2010 - Synthese 172 (1):157-176.
    We discuss several features of coherent choice functions —where the admissible options in a decision problem are exactly those that maximize expected utility for some probability/utility pair in fixed set S of probability/utility pairs. In this paper we consider, primarily, normal form decision problems under uncertainty—where only the probability component of S is indeterminate and utility for two privileged outcomes is determinate. Coherent choice distinguishes between each pair of sets of probabilities regardless the “shape” or “connectedness” of the sets of (...)
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  2.  48
    Why I Am Not an Objective Bayesian; Some Reflections Prompted by Rosenkrantz.Teddy Seidenfeld - 1979 - Theory and Decision 11 (4):413-440.
  3.  15
    Forecasting with Imprecise Probabilities.Teddy Seidenfeld, Mark J. Schervish & Joseph B. Kadane - unknown
    We review de Finetti’s two coherence criteria for determinate probabilities: coherence1defined in terms of previsions for a set of events that are undominated by the status quo – previsions immune to a sure-loss – and coherence2 defined in terms of forecasts for events undominated in Brier score by a rival forecast. We propose a criterion of IP-coherence2 based on a generalization of Brier score for IP-forecasts that uses 1-sided, lower and upper, probability forecasts. However, whereas Brier score is a strictly (...)
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  4.  28
    Decision Theory Without “Independence” or Without “Ordering”.Teddy Seidenfeld - 1988 - Economics and Philosophy 4 (2):267.
    It is a familiar argument that advocates accommodating the so-called paradoxes of decision theory by abandoning the “independence” postulate. After all, if we grant that choice reveals preference, the anomalous choice patterns of the Allais and Ellsberg problems violate postulate P2 of Savage's system. The strategy of making room for new preference patterns by relaxing independence is adopted in each of the following works: Samuelson, Kahneman and Tversky's “Prospect Theory”, Allais and Hagen, Fishburn, Chew and MacCrimmon, McClennen, and in closely (...)
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  5.  41
    Entropy and Uncertainty.Teddy Seidenfeld - 1986 - Philosophy of Science 53 (4):467-491.
    This essay is, primarily, a discussion of four results about the principle of maximizing entropy (MAXENT) and its connections with Bayesian theory. Result 1 provides a restricted equivalence between the two: where the Bayesian model for MAXENT inference uses an "a priori" probability that is uniform, and where all MAXENT constraints are limited to 0-1 expectations for simple indicator-variables. The other three results report on an inability to extend the equivalence beyond these specialized constraints. Result 2 established a sensitivity of (...)
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  6. Rethinking the Foundations of Statistics.Joseph B. Kadane, Mark J. Schervish & Teddy Seidenfeld - 1999 - Cambridge University Press.
    This important collection of essays is a synthesis of foundational studies in Bayesian decision theory and statistics. An overarching topic of the collection is understanding how the norms for Bayesian decision making should apply in settings with more than one rational decision maker and then tracing out some of the consequences of this turn for Bayesian statistics. There are four principal themes to the collection: cooperative, non-sequential decisions; the representation and measurement of 'partially ordered' preferences; non-cooperative, sequential decisions; and pooling (...)
     
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  7.  36
    Is Ignorance Bliss?Joseph B. Kadane, Mark Schervish & Teddy Seidenfeld - 2008 - Journal of Philosophy 105 (1):5-36.
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  8. Ending the Mendel-Fisher Controversy.Allan Franklin, A. W. F. Edwards, Daniel J. Fairbanks, Daniel L. Hartl & Teddy Seidenfeld - 2008 - Journal of the History of Biology 41 (4):775-777.
  9.  18
    Sleeping Beauty’s Credences.Jessi Cisewski, Joseph B. Kadane, Mark J. Schervish, Teddy Seidenfeld & Rafael Stern - 2016 - Philosophy of Science 83 (3):324-347.
    The Sleeping Beauty problem has spawned a debate between “thirders” and “halfers” who draw conflicting conclusions about Sleeping Beauty's credence that a coin lands heads. Our analysis is based on a probability model for what Sleeping Beauty knows at each time during the experiment. We show that conflicting conclusions result from different modeling assumptions that each group makes. Our analysis uses a standard “Bayesian” account of rational belief with conditioning. No special handling is used for self-locating beliefs or centered propositions. (...)
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  10.  3
    Probability and Evidence.Teddy Seidenfeld & Paul Horwich - 1984 - Philosophical Review 93 (3):474.
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  11.  52
    Calibration, Coherence, and Scoring Rules.Teddy Seidenfeld - 1985 - Philosophy of Science 52 (2):274-294.
    Can there be good reasons for judging one set of probabilistic assertions more reliable than a second? There are many candidates for measuring "goodness" of probabilistic forecasts. Here, I focus on one such aspirant: calibration. Calibration requires an alignment of announced probabilities and observed relative frequency, e.g., 50 percent of forecasts made with the announced probability of.5 occur, 70 percent of forecasts made with probability.7 occur, etc. To summarize the conclusions: (i) Surveys designed to display calibration curves, from which a (...)
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  12. A Conflict Between Finite Additivity and Avoiding Dutch Book.Teddy Seidenfeld & Mark J. Schervish - 1983 - Philosophy of Science 50 (3):398-412.
    For Savage (1954) as for de Finetti (1974), the existence of subjective (personal) probability is a consequence of the normative theory of preference. (De Finetti achieves the reduction of belief to desire with his generalized Dutch-Book argument for Previsions.) Both Savage and de Finetti rebel against legislating countable additivity for subjective probability. They require merely that probability be finitely additive. Simultaneously, they insist that their theories of preference are weak, accommodating all but self-defeating desires. In this paper we dispute these (...)
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  13.  10
    Nonconglomerability for Countably Additive Measures That Are Not Κ-Additive.Teddy Seidenfeld, Mark J. Schervish & Joseph B. Kadane - 2017 - Review of Symbolic Logic 10 (2):284-300.
    Let κ be an uncountable cardinal. Using the theory of conditional probability associated with de Finetti and Dubins, subject to several structural assumptions for creating sufficiently many measurable sets, and assuming that κ is not a weakly inaccessible cardinal, we show that each probability that is not κ-­additive has conditional probabilities that fail to be conglomerable in a partition of cardinality no greater than κ. This generalizes our result, where we established that each finite but not countably additive probability has (...)
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  14.  20
    On the Shared Preferences of Two Bayesian Decision Makers.Teddy Seidenfeld, Joseph B. Kadane & Mark J. Schervish - 1989 - Journal of Philosophy 86 (5):225-244.
  15.  16
    A Rubinesque Theory of Decision.Joseph B. Kadane, Teddy Seidenfeld & Mark J. Schervish - unknown
  16. Direct Inference and Inverse Inference.Teddy Seidenfeld - 1978 - Journal of Philosophy 75 (12):709-730.
    The JSTOR Archive is a trusted digital repository providing for long-term preservation and access to leading academic journals and scholarly literature from around the world. The Archive is supported by libraries, scholarly societies, publishers, and foundations. It is an initiative of JSTOR, a not-for-profit organization with a mission to help the scholarly community take advantage of advances in technology. For more information regarding JSTOR, please contact support@jstor.org.
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  17.  33
    State-Dependent Utilities.Mark J. Schervish, Teddy Seidenfeld & Joseph B. Kadane - unknown
    Several axiom systems for preference among acts lead to a unique probability and a state-independent utility such that acts are ranked according to their expected utilities. These axioms have been used as a foundation for Bayesian decision theory and subjective probability calculus. In this article we note that the uniqueness of the probability is relative to the choice of whatcounts as a constant outcome. Although it is sometimes clear what should be considered constant, in many cases there are several possible (...)
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  18.  57
    Remarks on the Theory of Conditional Probability: Some Issues of Finite Versus Countable Additivity.Teddy Seidenfeld - 2000 - In Vincent F. Hendricks, Stig Andur Pederson & Klaus Frovin Jørgensen (eds.), Probability Theory: Philosophy, Recent History and Relations to Science. Synthese Library, Kluwer.
    This paper discusses some differences between the received theory of regular conditional distributions, which is the countably additive theory of conditional probability, and a rival theory of conditional probability using the theory of finitely additive probability. The focus of the paper is maximally "improper" conditional probability distributions, where the received theory requires, in effect, that P{a: P = 0} = 1. This work builds upon the results of Blackwell and Dubins.
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  19.  9
    Two Measures of Incoherence: How Not to Gamble If You Must.Mark J. Schervish, Teddy Seidenfeld & Joseph B. Kadane - unknown
    The degree of incoherence, when previsions are not made in accordance with a probability measure, is measured by either of two rates at which an incoherent bookie can be made a sure loser. Each bet is considered as an investment from the points of view of both the bookie and a gambler who takes the bet. From each viewpoint, we define an amount invested (or escrowed) for each bet, and the sure loss of incoherent previsions is divided by the escrow (...)
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  20.  6
    On the Shared Preferences of Two Bayesian Decision Makers.Teddy Seidenfeld, Joseph B. Kadane & Mark J. Schervish - 1989 - Journal of Philosophy 86 (5):225.
  21.  9
    Philosophical Problems of Statistical Inference.Teddy Seidenfeld - 1981 - Philosophical Review 90 (2):295-298.
  22.  32
    Divisive Conditioning: Further Results on Dilation.Timothy Herron, Teddy Seidenfeld & Larry Wasserman - 1997 - Philosophy of Science 64 (3):411-444.
    Conditioning can make imprecise probabilities uniformly more imprecise. We call this effect "dilation". In a previous paper (1993), Seidenfeld and Wasserman established some basic results about dilation. In this paper we further investigate dilation on several models. In particular, we consider conditions under which dilation persists under marginalization and we quantify the degree of dilation. We also show that dilation manifests itself asymptotically in certain robust Bayesian models and we characterize the rate at which dilation occurs.
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  23. Proper Scoring Rules, Dominated Forecasts, and Coherence.Teddy Seidenfeld - unknown
    De Finetti introduced the concept of coherent previsions and conditional previsions through a gambling argument and through a parallel argument based on a quadratic scoring rule. He shows that the two arguments lead to the same concept of coherence. When dealing with events only, there is a rich class of scoring rules which might be used in place of the quadratic scoring rule. We give conditions under which a general strictly proper scoring rule can replace the quadratic scoring rule while (...)
     
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  24.  11
    Decisions with Indeterminate Probabilities.Teddy Seidenfeld - 1983 - Behavioral and Brain Sciences 6 (2):259.
  25.  5
    Bruno de Finetti and Imprecision.Paolo Vicig & Teddy Seidenfeld - unknown
    We review several of de Finetti’s fundamental contributions where these have played and continue to play an important role in the development of imprecise probability research. Also, we discuss de Finetti’s few, but mostly critical remarks about the prospects for a theory of imprecise probabilities, given the limited development of imprecise probability theory as that was known to him.
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  26.  61
    Preference for Equivalent Random Variables: A Price for Unbounded Utilities.Teddy Seidenfeld, Mark J. Schervish & Joseph B. Kadane - 2009 - Journal of Mathematical Economics 45:329-340.
    When real-valued utilities for outcomes are bounded, or when all variables are simple, it is consistent with expected utility to have preferences defined over probability distributions or lotteries. That is, under such circumstances two variables with a common probability distribution over outcomes – equivalent variables – occupy the same place in a preference ordering. However, if strict preference respects uniform, strict dominance in outcomes between variables, and if indifference between two variables entails indifference between their difference and the status quo, (...)
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  27.  40
    The Independence Postulate, Hypothetical and Called-Off Acts: A Further Reply to Rabinowicz. [REVIEW]Teddy Seidenfeld - 2000 - Theory and Decision 48 (4):319-322.
    The Independence postulate links current preferences between called-off acts with current preferences between constant acts. Under the assumption that the chance-events used in compound von Neumann-Morgenstern lotteries are value-neutral, current preferences between these constant acts are linked to current preferences between hypothetical acts, conditioned by those chance events. Under an assumption of stability of preferences over time, current preferences between these hypothetical acts are linked to future preferences between what are then and there constant acts. Here, I show that a (...)
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  28.  14
    Standards for Modest Bayesian Credences.Jessi Cisewski, Joseph B. Kadane, Mark J. Schervish, Teddy Seidenfeld & Rafael Stern - 2018 - Philosophy of Science 85 (1):53-78.
    Gordon Belot argues that Bayesian theory is epistemologically immodest. In response, we show that the topological conditions that underpin his criticisms of asymptotic Bayesian conditioning are self-defeating. They require extreme a priori credences regarding, for example, the limiting behavior of observed relative frequencies. We offer a different explication of Bayesian modesty using a goal of consensus: rival scientific opinions should be responsive to new facts as a way to resolve their disputes. Also we address Adam Elga’s rebuttal to Belot’s analysis, (...)
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  29.  12
    Agreeing to Disagree and Dilation.Jiji Zhang, Hailin Liu & Teddy Seidenfeld - unknown
    We consider Geanakoplos and Polemarchakis’s generalization of Aumman’s famous result on “agreeing to disagree", in the context of imprecise probability. The main purpose is to reveal a connection between the possibility of agreeing to disagree and the interesting and anomalous phenomenon known as dilation. We show that for two agents who share the same set of priors and update by conditioning on every prior, it is impossible to agree to disagree on the lower or upper probability of a hypothesis unless (...)
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  30.  35
    P's in a Pod: Some Recipes for Cooking Mendel's Data.Teddy Seidenfeld - unknown
    In 1936 R.A.Fisher asked the pointed question, "Has Mendel's Work Been Rediscovered?" The query was intended to open for discussion whether someone altered the data in Gregor Mendel's classic 1866 research report on the garden pea, "Experiments in Plant-Hybridization." Fisher concluded, reluctantly, that the statistical counts in Mendel's paper were doctored in order to create a better intuitive fit between Mendelian expected values and observed frequencies. That verdict remains the received view among statisticians, so I believe. Fisher's analysis is a (...)
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  31.  37
    When Several Bayesians Agree That There Will Be No Reasoning to a Foregone Conclusion.Joseph B. Kadane, Mark J. Schervish & Teddy Seidenfeld - 1996 - Philosophy of Science 63 (3):289.
    When can a Bayesian investigator select an hypothesis H and design an experiment (or a sequence of experiments) to make certain that, given the experimental outcome(s), the posterior probability of H will be lower than its prior probability? We report an elementary result which establishes sufficient conditions under which this reasoning to a foregone conclusion cannot occur. Through an example, we discuss how this result extends to the perspective of an onlooker who agrees with the investigator about the statistical model (...)
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  32.  31
    Comments on Causal Decision Theory.Teddy Seidenfeld - 1984 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1984:201 - 212.
    PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association, Vol. 1984, Volume Two: Symposia and Invited Papers. (1984), pp. 201-212.
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  33.  23
    When Normal and Extensive Form Decisions Differ.Teddy Seidenfeld - 1994 - In Dag Prawitz, Brian Skyrms & Dag Westerståhl (eds.), Logic, Methodology and Philosophy of Science. Elsevier. pp. 451-463.
    The "traditional" view of normative decision theory, as reported (for example) in chapter 2 of Luce and RaiÃa's [1957] classic work, Games and Decisions, proposes a reduction of sequential decisions problems to non-sequential decisions: a reduction of extensive forms to normal forms. Nonetheless, this reduction is not without its critics, both from inside and outside expected utility theory, It islay purpose in this essay to join with those critics by advocating the following thesis.
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  34.  35
    Substitution of Indifferent Options at Choice Nodes and Admissibility: A Reply to Rabinowicz.Teddy Seidenfeld - 2000 - Theory and Decision 48 (4):305-310.
    Tiebreak rules are necessary for revealing indifference in non- sequential decisions. I focus on a preference relation that satisfies Ordering and fails Independence in the following way. Lotteries a and b are indifferent but the compound lottery f, 0.5b> is strictly preferred to the compound lottery f, 0.5a>. Using tiebreak rules the following is shown here: In sequential decisions when backward induction is applied, a preference like the one just described must alter the preference relation between a and b at (...)
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  35.  15
    The Extent of Dilation of Sets of Probabilities and the Asymptotics of Robust Bayesian Inference.Timothy Herron, Teddy Seidenfeld & Larry Wasserman - 1994 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1994:250 - 259.
    We report two issues concerning diverging sets of Bayesian (conditional) probabilities-divergence of "posteriors"-that can result with increasing evidence. Consider a set P of probabilities typically, but not always, based on a set of Bayesian "priors." Fix E, an event of interest, and X, a random variable to be observed. With respect to P, when the set of conditional probabilities for E, given X, strictly contains the set of unconditional probabilities for E, for each possible outcome X = x, call this (...)
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  36. What Experiment Did We Just Do?Joseph B. Kadane, Mark J. Schervish & Teddy Seidenfeld - unknown
    Experimenters sometimes insist that it is unwise to examine data before determining how to analyze them, as it creates the potential for biased results. I explore the rationale behind this methodological guideline from the standpoint of an error statistical theory of evidence, and I discuss a method of evaluating evidence in some contexts when this predesignation rule has been violated. I illustrate the problem of potential bias, and the method by which it may be addressed, with an example from the (...)
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  37.  41
    Remarks on the Theory of Conditional Probability: Some Issues of Finite Versus Countable Additivity.Teddy Seidenfeld - unknown
    This paper (based on joint work with M.J.Schervish and J.B.Kadane) discusses some differences between the received theory of regular conditional distributions, which is the countably additive theory of conditional probability, and a rival theory of conditional probability using the theory of finitely additive probability. The focus of the paper is maximally "improper" conditional probability distributions, where the received theory requires, in effect, that P{a: P(a|a) = 0} = 1. This work builds upon the results of Blackwell and Dubins (1975).
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  38.  9
    Outline of a Theory of Partially Ordered Preferences.Teddy Seidenfeld - 1993 - Philosophical Topics 21 (1):173-189.
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  39.  24
    A Rate of Incoherence Applied to Fixed-Level Testing.Mark J. Schervish, Teddy Seidenfeld & Joseph B. Kadane - 2002 - Proceedings of the Philosophy of Science Association 2002 (3):S248-S264.
    It has long been known that the practice of testing all hypotheses at the same level , regardless of the distribution of the data, is not consistent with Bayesian expected utility maximization. According to de Finetti’s “Dutch Book” argument, procedures that are not consistent with expected utility maximization are incoherent and they lead to gambles that are sure to lose no matter what happens. In this paper, we use a method to measure the rate at which incoherent procedures are sure (...)
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  40.  22
    A Rate of Incoherence Applied to Fixed‐Level Testing.Mark J. Schervish, Teddy Seidenfeld & Joseph B. Kadane - 2002 - Philosophy of Science 69 (S3):S248-S264.
    It has long been known that the practice of testing all hypotheses at the same level , regardless of the distribution of the data, is not consistent with Bayesian expected utility maximization. According to de Finetti’s “Dutch Book” argument, procedures that are not consistent with expected utility maximization are incoherent and they lead to gambles that are sure to lose no matter what happens. In this paper, we use a method to measure the rate at which incoherent procedures are sure (...)
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  41.  29
    Sleeping Beauty’s Credences.Jessica Cisewski, Joseph B. Kadane, Mark J. Schervish, Teddy Seidenfeld & Rafael Stern - unknown
    The Sleeping Beauty problem has spawned a debate between “Thirders” and “Halfers” who draw conflicting conclusions about Sleeping Beauty’s credence that a coin lands Heads. Our analysis is based on a probability model for what Sleeping Beauty knows at each time during the Experiment. We show that conflicting conclusions result from different modeling assumptions that each group makes. Our analysis uses a standard “Bayesian” account of rational belief with conditioning. No special handling is used for self-locating beliefs or centered propositions. (...)
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  42. The Rest of Sleeping Beauty.Jessi Cisewski, Joseph B. Kadane, Mark J. Schervish, Teddy Seidenfeld & Rafael Stern - unknown
     
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  43.  1
    Rethinking the Foundations of Statistics.Henry E. Kyburg, Joseph B. Kadane, Mark J. Schervish & Teddy Seidenfeld - 2000 - Journal of Philosophy 97 (12):677.
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  44. Three Contrasts Between Two Senses of Coherence.Teddy Seidenfeld - unknown
    = { 1, …, n} is a finite partition of the sure event: a set of states. Consider two acts A1, A2 defined by the their outcomes relative to.
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  45. Extensions of Expected Utility Theory and Some Limitations of Pairwise Comparisons.Teddy Seidenfeld - unknown
    We contrast three decision rules that extend Expected Utility to contexts where a convex set of probabilities is used to depict uncertainty: Γ-Maximin, Maximality, and E-admissibility. The rules extend Expected Utility theory as they require that an option is inadmissible if there is another that carries greater expected utility for each probability in a (closed) convex set. If the convex set is a singleton, then each rule agrees with maximizing expected utility. We show that, even when the option set is (...)
     
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  46. Coherence with Proper Scoring Rules.Mark Schervish, Teddy Seidenfeld & Mark Schervish Joseph - unknown
    • Coherence1 for previsions of random variables with generalized betting; • Coherence2 for probability forecasts of events with Brier score penalty; • Coherence3 probability forecasts of events with various proper scoring rules.
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  47. Forecasting with Imprecise/Indeterminate Probabilities [IP] – Some Preliminary Findings.Teddy Seidenfeld, Mark Schervish & Jay Kadane - unknown
    Part 1 Background on de Finetti’s twin criteria of coherence: Coherence1: 2-sided previsions free from dominance through a Book. Coherence2: Forecasts free from dominance under Brier (squared error) score. Part 2 IP theory based on a scoring rule.
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  48. Independence for Full Conditional Measures, Graphoids and Bayesian Networks.Teddy Seidenfeld - unknown
    This paper examines definitions of independence for events and variables in the context of full conditional measures; that is, when conditional probability is a primitive notion and conditioning is allowed on null events. Several independence concepts are evaluated with respect to graphoid properties; we show that properties of weak union, contraction and intersection may fail when null events are present. We propose a concept of “full” independence, characterize the form of a full conditional measure under full independence, and suggest how (...)
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  49.  3
    Rejoinder.Teddy Seidenfeld - 1988 - Economics and Philosophy 4 (2):309.
  50.  41
    The Fundamental Theorems of Prevision and Asset Pricing.Teddy Seidenfeld - unknown
    DeFinetti took the concept of random variables as gambles very seriously, and used the concept to motivate the familiar concepts of probability and expectation. For each gamble X, he assumed that “You” would assign a value P (X), called the prevision of X so that you would be willing to accept the gamble β[X − P (X)] as fair for all positive and negative values β. The only constraint that deFinetti envisioned for you and your previsions is that you insisted (...)
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