Incentive for Distributed Optimization in Multi-User Network: A Study of Two Scenarios

Incentives for distributed optimization are investigated in two types of scenarios in which network users have private valuations (objective functions). A network center aims at maximizing the sum of users' valuations in the first scenario or the sum of its own valuations in the second scenario. It is shown that nontrivial strategies can be found by a user so that it can improve its own utility by providing false information to the center without leading a distributed algorithm to diverge. It demonstrates that a dual variable based pricing mechanism in distributed optimization cannot guarantee truthful reporting. While truthful reporting can be realized by using the classic Groves mechanism in the first scenario, the possibility of incentivizing truthful reporting in the second scenario depending on whether the center is willing to consider the valuations of the users in addition to those of its own.