Making Money from What You Know - How to Sell Information?


  • Shani Alkoby University of Texas
  • Zihe Wang Shanghai University of Finance and Economics
  • David Sarne Bar-Ilan University
  • Pingzhong Tang Tsinghua University



Information plays a key role in many decision situations. The rapid advancement in communication technologies makes information providers more accessible, and various information providing platforms can be found nowadays, most of which are strategic in the sense that their goal is to maximize the providers’ expected profit. In this paper, we consider the common problem of a strategic information provider offering prospective buyers information which can disambiguate uncertainties the buyers have, which can be valuable for their decision making. Unlike prior work, we do not limit the information provider’s strategy to price setting but rather enable her flexibility over the way information is sold, specifically enabling querying about specific outcomes and the elimination of a subset of non-true world states alongside the traditional approach of disclosing the true world state. We prove that for the case where the buyer is self-interested (and the information provider does not know the true world state beforehand) all three methods (i.e., disclosing the true worldstate value, offering to check a specific value, and eliminating a random value) are equivalent, yielding the same expected profit to the information provider. For the case where buyers are human subjects, using an extensive set of experiments we show that the methods result in substantially different outcomes. Furthermore, using standard machine learning techniques the information provider can rather accurately predict the performance of the different methods for new problem settings, hence substantially increase profit.




How to Cite

Alkoby, S., Wang, Z., Sarne, D., & Tang, P. (2019). Making Money from What You Know - How to Sell Information?. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 2421-2428.



AAAI Technical Track: Human-AI Collaboration