We present a new strategic voting model where we use uncertainty representation to model preferences. Specifically, we use probability sets as uncertainty representations, together with lower and upper expected utility gains to take strategic decisions. Focusing on belief functions in particular, we demonstrate that this very expressive model includes in one sweep many existing models based on probabilities, sets or incomplete preferences. Additionally, we generalize several well-known convergence results from the literature to this broader representational setting. Furthermore, we illustrate how this model can capture more realistic scenarios for practical applications but also raises theoretical challenges.

翻译：我们提出了一种新的策略投票模型，通过不确定性表示来建模偏好。具体而言，我们利用概率集作为不确定性表示，并结合期望效用的下界与上界来制定策略决策。特别聚焦于信念函数，我们证明这一高度表达力的模型能够一次性涵盖许多基于概率、集合或不完全偏好的现有模型。此外，我们将文献中几个著名的收敛性结果推广至这一更广泛的表示框架中。进一步地，我们展示了该模型如何更真实地刻画实际应用场景，同时也揭示了其带来的理论挑战。