Strategic Information Platforms – Selective Disclosure and The Price of “Free”

This work was presented at HAIDM 2014. The 2014 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: This paper deals with platforms that provide agents easier access to the type of opportunities in which they are interested (e.g., eCommerce platforms, used cars bulletins and dating web-sites). We show that under various common service schemes, a platform can benefit from not necessarily listing all the opportunities with which it is familiar, even if there is no marginal cost for listing any additional opportunity. The main implication of this result is that platforms should extract their expected-profit-maximizing service terms not based solely on the fees charged from users, but they should also use the subset that will be listed as the decision variable in the optimization problem. The analysis applies to four well-known service schemes that a platform may use to price its services. We show that neither of these schemes generally dominates the others or is dominated by any of the others. For the common case of homogeneous preferences, however, several dominance relationships can be proved, enabling the platform to identify the schemes that should be used as a default. Furthermore, the analysis provides a game-theoretic search-based explanation for a possible preference of buyers to pay for the service rather than receive it for free (e.g., when the service is sponsored by ads), a phenomena that has been justified in prior literature typically with the argument of willingness to pay a premium for an ad-free experience or more reliable platforms. The paper shows that this preference can hold both for the users and the platform in a given setting, even if both sides are fully strategic.

Keywords: Platforms and Services, Economics of Information, Two-Sided Markets, Price of Free, Service Schemes.

Citation: Chen Hajaj and David Sarne. Strategic Information Platforms – Selective Disclosure and The Price of “Free”.

Download: http://bit.ly/1VwVZSz

About P. Andreadis

Pre-Doctoral Research Assistant in AI and Social Computation @ University of Edinburgh.

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