TY - GEN
T1 - The strategy of experts for repeated predictions
AU - Ban, Amir
AU - Azar, Yossi
AU - Mansour, Yishay
N1 - Publisher Copyright: © Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85037046488&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-71924-5_4
DO - 10.1007/978-3-319-71924-5_4
M3 - منشور من مؤتمر
SN - 9783319719238
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 44
EP - 57
BT - Web and Internet Economics - 13th International Conference, WINE 2017, Proceedings
A2 - Devanur, Nikhil R.
A2 - Lu, Pinyan
T2 - 13th International Conference on Web and Internet Economics, WINE 2017
Y2 - 17 December 2017 through 20 December 2017
ER -