Weber, T. A., (2019). Dynamic Learning in Markets: Pricing, Advertising, and Information Acquisition. Proceedings of the 52nd Annual Hawaii International Conference on System Sciences (HICSS). Honolulu, 8-11 January 2019, 6628-6637.
https://doi.org/10.24251/HICSS.2019.794

has been cited by the following article:

• TITLE: Multiplicative Normal Noise and Nonconcavity in the Value of Information

  AUTHORS: Stefan Behringer

  KEYWORDS: Value of Information, Inverse Problems, Bayesian Learning, Monopoly, Multiplicative Noise, Nonconcavity

  JOURNAL NAME: Theoretical Economics Letters, Vol.11 No.1, February 25, 2021

  ABSTRACT: This paper investigates a Bayesian inverse problem of a price setting monopolist facing a random demand. In contrast to previous investigations an unknown true market potential of demand is distorted by two independent Gaussian errors, a zero-mean additive and a unity-mean multiplicative one. The multi-period game allows for learning from realized market demands (signals). Interestingly increasing the level of noise of a multiplicative error in this dynamic setting can actually improve the Value of Information of signals to the firm, a result that cannot hold for a single additive error or in a static context.