interactive agents and the problem of social influence or social accuracy

The paper by Bayarri and Degroot contributed to two mail prediction:

1-as an expert’s decision weight approaches zero (very small) his/her optimal prediction is to report a large number for one option.

2-as an expert’s decision weight approaches 1, one should never report probability zero to any option.

In both cases, the experts try to maximize their posterior decision weight. This suggests that, in an interactive environment, a participant with low reliability should be overconfident about his decision in order to maximize his/her influence. In this point of view, overconfidence is an optimal behavior.

Here the question is that do people try to maximize their influence? If this is the case, then we would expect that people get reward when their opinion is accepted as group’s opinion regardless of the outcome of the decision. It is also could be true that they try to maximize a synthesis of accuracy and influence. For example in reinforcement learning paradigm, the cumulative reward an agent receive is a linear combination of being correct (accuracy) and being selected (influence). It is there a task we could separate these two kinds of rewards?