In open environments, there is no central control over agent behaviors. On the contrary, agents in such systems can be assumed to be primarily driven by self-interests. Under the assumption that agents remain in the system for significant periods, or that the agent composition changes only slowly, we have previously presented a prescriptive strategy for promoting and sustaining cooperation among self-interested agents. The adaptive, probabilistic policy we have prescribed promotes reciprocate cooperation that improves both individual and group performance in the end. In the short run, however, selfish agents could still exploit reciprocate agents. In this paper, we evaluate the hypothesis that the exploitative tendencies of selfish agents can be effectively curbed if reciprocate agents share their “opinions” of other agents. Since the true nature of agents are not known a priori and is learned from experience, believing others can also pose other hazards. We provide a learned trust-based evaluation function that is shown to resist both individual and concerted deception on the part of selfish agents.