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Robust forecast aggregation

Author

Listed:
  • Itai Arieli

    (Faculty of Industrial Engineering and Management, Technion–Israel Institute of Technology, Haifa 3200003, Israel)

  • Yakov Babichenko

    (Faculty of Industrial Engineering and Management, Technion–Israel Institute of Technology, Haifa 3200003, Israel)

  • Rann Smorodinsky

    (Faculty of Industrial Engineering and Management, Technion–Israel Institute of Technology, Haifa 3200003, Israel)

Abstract

Bayesian experts who are exposed to different evidence often make contradictory probabilistic forecasts. An aggregator, ignorant of the underlying model, uses this to calculate his or her own forecast. We use the notions of scoring rules and regret to propose a natural way to evaluate an aggregation scheme. We focus on a binary state space and construct low regret aggregation schemes whenever there are only two experts that either are Blackwell-ordered or receive conditionally independent and identically distributed (i.i.d.) signals. In contrast, if there are many experts with conditionally i.i.d. signals, then no scheme performs (asymptotically) better than a ( 0.5 , 0.5 ) forecast.

Suggested Citation

  • Itai Arieli & Yakov Babichenko & Rann Smorodinsky, 2018. "Robust forecast aggregation," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(52), pages 12135-12143, December.
    • Handle: RePEc:nas:journl:v:115:y:2018:p:e12135-e12143
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    Citations

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    Cited by:

    1. Satopää, Ville A., 2021. "Improving the wisdom of crowds with analysis of variance of predictions of related outcomes," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1728-1747.
    2. Arieli, Itai & Babichenko, Yakov & Smorodinsky, Rann, 2020. "Identifiable information structures," Games and Economic Behavior, Elsevier, vol. 120(C), pages 16-27.
    3. Henrique de Oliveira & Yuhta Ishii & Xiao Lin, 2021. "Robust Merging of Information," Papers 2106.00088, arXiv.org.
    4. Babichenko, Yakov & Talgam-Cohen, Inbal & Xu, Haifeng & Zabarnyi, Konstantin, 2022. "Regret-minimizing Bayesian persuasion," Games and Economic Behavior, Elsevier, vol. 136(C), pages 226-248.
    5. Yakov Babichenko & Dan Garber, 2021. "Learning Optimal Forecast Aggregation in Partial Evidence Environments," Mathematics of Operations Research, INFORMS, vol. 46(2), pages 628-641, May.
    6. Itay Kavaler & Rann Smorodinsky, 2019. "A Cardinal Comparison of Experts," Papers 1908.10649, arXiv.org, revised Feb 2020.

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