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The Strategy of Experts for Repeated Predictions

  • Amir Ban
  • Yossi Azar
  • Yishay Mansour
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10660)

Abstract

We investigate the behavior of experts who seek to make predictions with maximum impact on an audience. At a known future time, a certain continuous random variable will be realized. A public prediction gradually converges to the outcome, and an expert has access to a more accurate prediction. We study when the expert should reveal his information, when his reward is based on a proper scoring rule (e.g., is proportional to the change in log-likelihood of the outcome).

In Azar et al. (2016), we analyzed the case where the expert may make a single prediction. In this paper, we analyze the case where the expert is allowed to revise previous predictions. This leads to a rather different set of dilemmas for the strategic expert. We find that it is optimal for the expert to always tell the truth, and to make a new prediction whenever he has a new signal. We characterize the expert’s expectation for his total reward, and show asymptotic limits.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Blavatnik School of Computer ScienceTel Aviv UniversityTel AvivIsrael

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