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  1. Michael K. Miller, Guanchun Wang, Sanjeev R. Kulkarni & Daniel N. Osherson, Wishful Thinking and Social Influence in the 2008 U.S. Presidential Election.
    This paper analyzes individual probabilistic predictions of state outcomes in the 2008 U.S. presidential election. Employing an original survey of more than 19,000 respondents, ours is the first study of electoral forecasting to involve multiple subnational predictions and to incorporate the influence of respondents’ home states. We relate a range of demographic, political, and cognitive variables to individual accuracy and predictions, as well as to how accuracy improved over time. We find strong support for wishful thinking bias in expectations, as (...)
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  2. Guanchun Wang, Sanjeev R. Kulkarni & Daniel N. Osherson, Aggregating Large Sets of Probabilistic Forecasts by Weighted Coherent Adjustment.
    Stochastic forecasts in complex environments can benefit from combining the estimates of large groups of forecasters (“judges”). But aggregating multiple opinions faces several challenges. First, human judges are notoriously incoherent when their forecasts involve logically complex events. Second, individual judges may have specialized knowledge, so different judges may produce forecasts for different events. Third, the credibility of individual judges might vary, and one would like to pay greater attention to more trustworthy forecasts. These considerations limit the value of simple aggregation (...)
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  3. Guanchun Wang, Sanjeev Kulkarni & Daniel N. Osherson, Improving Aggregated Forecasts of Probability.
    ��The Coherent Approximation Principle (CAP) is a method for aggregating forecasts of probability from a group of judges by enforcing coherence with minimal adjustment. This paper explores two methods to further improve the forecasting accuracy within the CAP framework and proposes practical algorithms that implement them. These methods allow flexibility to add fixed constraints to the coherentization process and compensate for the psychological bias present in probability estimates from human judges. The algorithms were tested on a data set of nearly (...)
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  4. Martin M. Monti, Lawrence M. Parsons & Daniel N. Osherson (2012). Response to Tzourio-Mazoyer and Zago: Yes, There is a Neural Dissociation Between Language and Reasoning. Trends in Cognitive Sciences 16 (10):495-496.
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  5. Martin M. Monti, Simon Grant & Daniel N. Osherson (2005). A Note on Concave Utility Functions. Mind and Society 4 (1):85-96.
    The classical theory of preference among monetary bets represents people as expected utility maximizers with concave utility functions. Critics of this account often rely on assumptions about preferences over wide ranges of total wealth. We derive a prediction of the theory that bears on bets at any fixed level of wealth, and test the prediction behaviorally. Our results are discrepant with the classical account. Competing theories are also examined in light of our data.
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  6. Daniel N. Osherson (1999). On the Adequacy of Prototype Theory as a Theory of Concepts Daniel N. Osherson and Edward E. Smith. In Eric Margolis & Stephen Laurence (eds.), Concepts: Core Readings. Mit Press. 261.
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  7. Daniel N. Osherson & Scott Weinstein (1993). Relevant Consequence and Empirical Inquiry. Journal of Philosophical Logic 22 (4):437 - 448.
    A criterion of adequacy is proposed for theories of relevant consequence. According to the criterion, scientists whose deductive reasoning is limited to some proposed subset of the standard consequence relation must not thereby suffer a reduction in scientific competence. A simple theory of relevant consequence is introduced and shown to satisfy the criterion with respect to a formally defined paradigm of empirical inquiry.
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  8. Daniel N. Osherson, Joshua Stern, Ormond Wilkie, Michael Stob & Edward E. Smith (1991). Default Probability. Cognitive Science 15 (2):251-269.
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  9. Daniel N. Osherson, Michael Stob & Scott Weinstein (1991). A Universal Inductive Inference Machine. Journal of Symbolic Logic 56 (2):661-672.
    A paradigm of scientific discovery is defined within a first-order logical framework. It is shown that within this paradigm there exists a formal scientist that is Turing computable and universal in the sense that it solves every problem that any scientist can solve. It is also shown that universal scientists exist for no regular logics that extend first-order logic and satisfy the Löwenheim-Skolem condition.
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  10. Haim Gaifman, Daniel N. Osherson & Scott Weinstein (1990). A Reason for Theoretical Terms. Erkenntnis 32 (2):149 - 159.
    The presence of nonobservational vocabulary is shown to be necessary for wide application of a conservative principle of theory revision.
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  11. Daniel N. Osherson & Edward E. Smith (eds.) (1990). An Invitation to Cognitive Science. MIT Press.
    The volumes are self contained and can be used individually in upper-level undergraduate and graduate courses ranging from introductory psychology, linguistics, ...
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  12. Daniel N. Osherson & Scott Weinstein (1990). On Advancing Simple Hypotheses. Philosophy of Science 57 (2):266-277.
    We consider drawbacks to scientific methods that prefer simple hypotheses to complex ones that cover the same data. The discussion proceeds in the context of a precise model of scientific inquiry.
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  13. Daniel N. Osherson & Scott Weinstein (1989). Identifiable Collections of Countable Structures. Philosophy of Science 56 (1):94-105.
    A model of idealized scientific inquiry is presented in which scientists are required to infer the nature of the structure that makes true the data they examine. A necessary and sufficient condition is presented for scientific success within this paradigm.
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  14. Daniel N. Osherson & Scott Weinstein (1989). On Charitable Translation. Philosophical Studies 56 (2):127 - 134.
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  15. Daniel N. Osherson & Scott Weinstein (1989). Paradigms of Truth Detection. Journal of Philosophical Logic 18 (1):1 - 42.
    Alternative models of idealized scientific inquiry are investigated and compared. Particular attention is devoted to paradigms in which a scientist is required to determine the truth of a given sentence in the structure giving rise to his data.
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  16. Daniel N. Osherson, Michael Stob & Scott Weinstein (1988). Mechanical Learners Pay a Price for Bayesianism. Journal of Symbolic Logic 53 (4):1245-1251.
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  17. Daniel N. Osherson & Scott Weinstein (1988). Finite Axiomatizability and Scientific Discovery. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1988:409 - 412.
    This paper provides a mathematical model of scientific discovery. It is shown in the context of this model that any discovery problem that can be solved by a computable scientist can be solved by a computable scientist all of whose conjectures are finitely axiomatizable theories.
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  18. Edward E. Smith, Daniel N. Osherson, Lance J. Rips & Margaret Keane (1988). Combining Prototypes: A Selective Modification Model. Cognitive Science 12 (4):485-527.
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  19. Daniel N. Osherson, Edward E. Smith & Eldar B. Shafir (1986). Some Origins of Belief. Cognition 24 (3):197-224.
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  20. Daniel N. Osherson (1985). Computer Output. Cognition 20 (3):261-264.
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  21. Daniel N. Osherson (1985). Learning Theory and Neural Reduction: A Comment. In Jacques Mehler & R. Fox (eds.), Neonate Cognition: Beyond the Blooming Buzzing Confusion. Lawrence Erlbaum. 399.
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  22. Edward E. Smith & Daniel N. Osherson (1984). Conceptual Combination with Prototype Concepts. Cognitive Science 8 (4):337-361.
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  23. Daniel N. Osherson & Edward E. Smith (1982). Gradedness and Conceptual Combination. Cognition 12 (3):299-318.
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  24. Daniel N. Osherson, Michael Stob & Scott Weinstein (1982). Ideal Learning Machines. Cognitive Science 6 (3):277-290.
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  25. Daniel N. Osherson & Scott Weinstein (1982). A Note on Formal Learning Theory. Cognition 11 (1):77-88.
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  26. Daniel N. Osherson (1981). Modularity as an Issue for Cognitive Science. Cognition 10 (1-3):241-242.
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  27. Daniel N. Osherson & Edward E. Smith (1981). On the Adequacy of Prototype Theory as a Theory of Concepts. Cognition 9 (1):35-58.
  28. Daniel N. Osherson (1978). Three Conditions on Conceptual Naturalness. Cognition 6 (4):263-289.
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  29. Daniel N. Osherson & Thomas Wasow (1976). Task-Specificity and Species-Specificity in the Study of Language: A Methodological Note. Cognition 4 (2):203-214.
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  30. Daniel N. Osherson & Ellen Markman (1974). Language and the Ability to Evaluate Contradictions and Tautologies. Cognition 3 (3):213-226.
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