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Proper scoring rules incentivize experts to accurately report beliefs, assuming predictions cannot influence outcomes.
Verification of forecasts expressed in terms of probability
G. W. Brier · 1950
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Elicitation of personal probabilities and expectations
L. J. Savage · 1971
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Optimal forecasting incentives
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R. C. Jeffrey · 1990
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Combinatorial information market design
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Gaussian Processes for Machine Learning
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A utility framework for bounded-loss market makers
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Strictly proper scoring rules, prediction, and estimation
T. Gneiting and A. E. Raftery · 2007
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Computational aspects of prediction markets
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The basic ai drives
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A unified framework for dynamic pari-mutuel information market design
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Level sets and extrema of random processes and fields
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Prediction mechanisms that do not incentivize undesirable actions
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A. Othman and T. Sandholm · 2010
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Thinking inside the box: Controlling and using an oracle ai
S. Armstrong, A. Sandberg, and N. Bostrom · 2012
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Risks and mitigation strategies for oracle ai
S. Armstrong · 2013
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Epistemic decision theory
H. Greaves · 2013
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Superintelligence
N. Bostrom · 2014
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Eliciting predictions and recommendations for decision making
Y. Chen, I. A. Kash, M. Ruberry, and V. Shnayder · 2014
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Predicting own action: Self-fulfilling prophecy induced by proper scoring rules
M. Oka, T. Todo, Y. Sakurai, and M. Yokoo · 2014
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An overview of applications of proper scoring rules
Stochastic optimization for performative prediction
C. Mendler-Dünner, J. Perdomo, T. Zrnic, and M. Hardt · 2020
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Performative prediction
J. Perdomo, T. Zrnic, C. Mendler-Dünner, and M. Hardt · 2020
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Avoiding tampering incentives in deep RL via decoupled approval
J. Uesato, R. Kumar, V. Krakovna, T. Everitt, R. Ngo, and S. Legg · 2020
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P. Weirich · 2020
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Reinforcement learning in newcomblike environments
J. Bell, L. Linsefors, C. Oesterheld, and J. Skalse · 2021
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S. Armstrong and X. O’Rorke · 2017
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Standard ML Oracles vs counterfactual ones
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Partial agency
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O. Evans, O. Cotton-Barratt, L. Finnveden, A. Bales, A. Balwit, P. Wills, L. Righetti, and W. Saunders · 2021
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How to learn when data reacts to your model: performative gradient descent
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Infinitely often, probability 1, Borel-Cantelli, and the law of large numbers, 2021
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Binary scoring rules that incentivize precision
E. Neyman, G. Noarov, and S. M. Weinberg · 2021
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Optimal policies tend to seek power
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Discovering latent knowledge in language models without supervision
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Scoring rules for performative binary prediction
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Performative power
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Regret minimization with performative feedback
M. Jagadeesan, T. Zrnic, and C. Mendler-Dünner · 2022
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Optimization of scoring rules
Y. Li, J. D. Hartline, L. Shan, and Y. Wu · 2022
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The alignment problem from a deep learning perspective
R. Ngo, L. Chan, and S. Mindermann · 2022
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On second-order scoring rules for epistemic uncertainty quantification
V. Bengs, E. Hüllermeier, and W. Waegeman · 2023
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