Coalitional game with opinion exchange

TL;DR

This paper introduces a framework for opinion exchange in coalitional games, allowing players to share and update payoff views, leading to stable and efficient consensus under strategic and rational behaviors.

Contribution

It develops a novel model integrating opinion exchange with coalitional game theory, analyzing strategic and algorithmic consensus mechanisms.

Findings

Efficient opinion fusion achieved at Nash equilibrium with risk-averse influential players.

Algorithmic R-learning converges to the same outcome as rational strategic consensus.

The framework decouples opinion exchange from coalition formation under truthful reporting.

Abstract

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.