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In recent years, the rapid growth of peer-to-peer (P2P) networks has provided a new paradigm for content distribution. To improve the efficiency of a P2P system, it is important to provide incentives for the peers to participate and contribute their resources. Various attempts have been made to reward/penalize peers by providing service differentiation based on a requesting peer's history or reputation. However, in a truly distributed, non-cooperative environment, maintaining and preventing the untruthful revealing of such information within the community impose larger computation and communication overheads to the system. These problems are further magnified when large-volume contents are being distributed because of the length distribution processes and the update of history or reputation has to keep up with the distribution process. In this paper, we address the incentive provisioning problem for distribution of large-volume content in P2P networks, and present a "seeing-is-believing" incentive-compatible mechanism (protocol) in which a peer will decide how much resources will be assigned to which neighbors based on what it has experienced. The protocol applies a utility-based resource-trading concept where peers will maximize their contributions for a fair or better return, and we show that by adopting this protocol, the system will achieve Cournot Equilibrium. Furthermore, our protocol is light-weight, completely decentralized, and cheat-proof. Experimental results illustrate significant improvements on the distribution efficiency of our protocol over other adopted alternatives.