TY - GEN

T1 - When should an expert make a prediction?

AU - Azar, Yossi

AU - Ban, Amir

AU - Mansour, Yishay

N1 - Funding Information:
Azar was supported in part by The Israeli Centers of Research Excellence (I-CORE) program, (Center No. 4/11), and by a grant from the Israel Science Foundation (ISF). Ban was supported in part by a grant from the Len Blavatnik and the Blavatnik Family Foundation and a grant from the Israel Science Foundation (ISF). Mansour was supported in part by The Israeli Centers of Research Excellence (I-CORE) program, (Center No. 4/11), by a grant from the Israel Science Foundation (ISF), by a grant from United States-Israel Binational Science Foundation (BSF), and by a grant from the Len Blavatnik and the Blavatnik Family Foundation.

PY - 2016/7/21

Y1 - 2016/7/21

N2 - We consider a setting where in a known future time, a certain continuous random variable will be realized. There is a public prediction that gradually converges to its realized value, and an expert that has access to a more accurate prediction. Our goal is to study when should the expert reveal his information, assuming that his reward is based on a logarithmic market scoring rule (i.e., his reward is proportional to the gain in loglikelihood of the realized value). Our contributions are: (1) we characterize the expert's optimal policy and show that it is threshold based. (2) we analyze the expert's asymptotic expected optimal reward and show a tight connection to the Law of the Iterated Logarithm, and (3) we give an efficient dynamic programming algorithm to compute the optimal policy.

AB - We consider a setting where in a known future time, a certain continuous random variable will be realized. There is a public prediction that gradually converges to its realized value, and an expert that has access to a more accurate prediction. Our goal is to study when should the expert reveal his information, assuming that his reward is based on a logarithmic market scoring rule (i.e., his reward is proportional to the gain in loglikelihood of the realized value). Our contributions are: (1) we characterize the expert's optimal policy and show that it is threshold based. (2) we analyze the expert's asymptotic expected optimal reward and show a tight connection to the Law of the Iterated Logarithm, and (3) we give an efficient dynamic programming algorithm to compute the optimal policy.

UR - http://www.scopus.com/inward/record.url?scp=84983548357&partnerID=8YFLogxK

U2 - 10.1145/2940716.2940729

DO - 10.1145/2940716.2940729

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AN - SCOPUS:84983548357

T3 - EC 2016 - Proceedings of the 2016 ACM Conference on Economics and Computation

SP - 125

EP - 142

BT - EC 2016 - Proceedings of the 2016 ACM Conference on Economics and Computation

PB - Association for Computing Machinery, Inc

T2 - 17th ACM Conference on Economics and Computation, EC 2016

Y2 - 24 July 2016 through 28 July 2016

ER -