From a perspective of designing or engineering for opinion formation games in social networks, the "opinion maximization (or minimization)" problem has been studied mainly for designing subset selecting algorithms. We define a two-player zero-sum Stackelberg game of competitive opinion optimization by letting the player under study as the leader minimize the sum of expressed opinions by doing so-called "internal opinion design", knowing that the other adversarial player as the follower is to maximize the same objective by also conducting her own internal opinion design. We furthermore consider multiagent learning, specifically using the Optimistic Gradient Descent Ascent, and analyze its convergence to equilibria in the simultaneous version of competitive opinion optimization.
翻译:从设计或工程设计社会网络舆论形成游戏的角度来看,“理想最大化(或最小化)”问题主要是为了设计子集选择算法而研究的。 我们定义了一种双玩者零和Stackelberg竞争优化观点游戏,让正在学习的玩家作为领导者通过做所谓的“内部观点设计”来尽量减少表达观点的总和,知道作为追随者的另一对立玩家通过同时进行自己的内部观点设计来最大限度地实现同样的目标。 我们还考虑多试剂学习,特别是使用“乐观渐进源头”并用竞争性观点优化同时版来分析它与平衡的趋同。