Selective opportunity disclosure at the service of strategic information platforms
This paper studies the strategic behavior of 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 four 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 take the subset of opportunities to be included in their listings as a decision variable, alongside the fees set for the service in their expected profit "maximizing" optimization problem. We show that none of the four schemes generally dominates any of the others or is dominated by any. For the case of homogeneous preferences, however, several dominance relationships can be proved. 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) The paper shows that this preference can hold both for the users and the platform simultaneously in a given setting, even if both sides are fully strategic. Finally, the paper analyzes the potential improvement in the platform's expected profit that can be achieved by considering hybrid service schemes that combine the basic ones. In particular, we focus in the Two Part Tariff scheme that combines the two commonly used subscription and pay per click schemes.
Autonomous Agents and Multi Agent Systems