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Can theories be tested?: a cryptographic treatment of forecast testing

Published: 09 January 2013 Publication History

Abstract

How do we test if a weather forecaster actually knows something about whether it will rain or not? Intuitively, a "good" forecast test should be complete---namely, a forecaster knowing the distribution of Nature should be able to pass the test with high probability, and sound---an uninformed forecaster should only be able to pass the test with small probability. We provide a comprehensive cryptographic study of the feasibility of complete and sound forecast testing, introducing various notions of both completeness and soundness, inspired by the literature on interactive proofs. Our main technical result is an incompleteness theorem for our most basic notion of computationally sound and complete forecast testing: If Nature is implemented by a polynomial-time algorithm, then every complete polynomial-time test can be passed by a completely uninformed polynomial-time forecaster (i.e., a computationally-bounded "charlatan") with high probability. We additionally study alternative notions of soundness and completeness and present both positive and negative results for these notions.

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Cited By

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  • (2023)Indistinguishable Predictions and Multi-group Fair LearningAdvances in Cryptology – EUROCRYPT 202310.1007/978-3-031-30545-0_1(3-21)Online publication date: 23-Apr-2023
  • (2021)Outcome indistinguishabilityProceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing10.1145/3406325.3451064(1095-1108)Online publication date: 15-Jun-2021
  • (2015)From Weak to Strong Zero-Knowledge and ApplicationsTheory of Cryptography10.1007/978-3-662-46494-6_4(66-92)Online publication date: 2015

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    cover image ACM Conferences
    ITCS '13: Proceedings of the 4th conference on Innovations in Theoretical Computer Science
    January 2013
    594 pages
    ISBN:9781450318594
    DOI:10.1145/2422436
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 09 January 2013

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    Author Tags

    1. forecast testing
    2. incompleteness
    3. multiplicative weights

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    ITCS '13
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    ITCS '13: Innovations in Theoretical Computer Science
    January 9 - 12, 2013
    California, Berkeley, USA

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    View all
    • (2023)Indistinguishable Predictions and Multi-group Fair LearningAdvances in Cryptology – EUROCRYPT 202310.1007/978-3-031-30545-0_1(3-21)Online publication date: 23-Apr-2023
    • (2021)Outcome indistinguishabilityProceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing10.1145/3406325.3451064(1095-1108)Online publication date: 15-Jun-2021
    • (2015)From Weak to Strong Zero-Knowledge and ApplicationsTheory of Cryptography10.1007/978-3-662-46494-6_4(66-92)Online publication date: 2015

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