  In coalitional games, traditional coalitional game theory does not apply if
different participants hold different opinions about the payoff function that
corresponds to each subset of the coalition. In this paper, we propose a
framework in which players can exchange opinions about their views of payoff
functions and then decide the distribution of the value of the grand coalition.
When all players are truth-telling, the problem of opinion consensus is
decoupled from the coalitional game, but interesting dynamics will arise when
players are strategic in the consensus phase. Assuming that all players are
rational, the model implies that, if influential players are risk-averse, an
efficient fusion of the distributed data is achieved at pure strategy Nash
equilibrium, meaning that the average opinion will not drift. Also, without the
assumption that all players are rational, each player can use an algorithmic
R-learning process, which gives the same result as the pure strategy Nash
equilibrium with rational players.
