Understanding the Uncovered Set

这项研究集中在立法行动的一个基本约束：可制定的提案集，这意味着有一个提案和修正案的议程，将提案作为立法者投票的结果。这一概念对于理解立法战略的许多方面至关重要：-议程设置：通过安排一系列提案和修正案按顺序进行表决来保证结果。无论是谁设定的议程，他们的努力都必须从可制定的开始，因为这一设定定义了以这种方式堆叠立法甲板可以产生哪些结果。-战略投票，在议程中的某个点投票支持不受欢迎的替代方案，以塑造最终结果。一群人可能会投票支持一项修正案，这项修正案会使他们反对的提案变得更糟，因为他们知道未经修改的提案会获得通过，但修改后的提案不会。只有当立法者知道什么是可制定的，使他们能够预测自己行为的后果时，这一战略才是可能的。-施压，通过迫使一些成员投票反对他们的政策目标或选民，确保有威胁和回报的结果。在这样做之前，政党领导人必须确定他们想要的结果是否可以设定，如果是，他们可以通过构建正确的议程并允许立法者自由投票来确保这一结果。虽然许多研究都集中在展示这些战术的实用性，但没有一项研究包含了什么是可以实施的规范。本说明书不需要说明这些策略是如何工作的，但对于评估它们在现实世界环境中的潜在影响或将结果归因于它们的使用是必要的。例如，一个政党在制定其纲领方面的成功可能源于其领导人在议程设置方面的成功，但也可能是因为可制定的结果自然偏向于该党，而政党领导人什么都不做。如果不知道它可以设定什么，就不可能区分这些解释。我们的方法以未覆盖集的可控性为特征，这是空间投票博弈的一个解决方案概念。未被覆盖的集合捕捉到了可控性的本质，并且是对多数规则性质的研究流中的重要提炼。问题是，没有人知道在给定的空间投票游戏或现实世界情况下，未被覆盖的集合是什么样子的。因此，不可能使用未覆盖的集合来预测现实世界的行为，或评估推动现实世界结果的因素。不可能确定未覆盖的集合预测是否与通过其他方法获得的预测不同。而且不可能测试未覆盖的套装。以确定这是一种分析性的好奇心，还是对立法行动的有价值的见解。我们提出的研究旨在解决所有这些问题。利用以前实现了估计未覆盖集的计算方法的工作，我们将进行一系列涉及群体决策的实验，以查看未覆盖集的行为预测是否有效。我们还将重新分析经典实验的数据，看看他们的发现是否与未涵盖的集合一致。我们还将使用我们的技术来看看未被覆盖的布景是否能很好地预测当代众议院的结果。最后，使用形式分析和模拟的组合，我们将改进我们的技术，并将未覆盖的集合预测与其他方法开发的预测进行比较。

This research focuses on a fundamental constraint on legislative action: the set of proposals that are enactable, meaning there is an agenda of proposals and amendments that has the proposal as the result when legislators cast their votes. This concept is critical to understanding many facets of legislative strategy: - agenda-setting: guaranteeing a result by arranging a series of proposals and amendments to be voted on sequentially. Regardless of who sets the agenda, their efforts must begin with what is enactable, as this set defines which outcomes can be produced by stacking the legislative deck in this way. - strategic voting, voting for an undesirable alternative at one point in an agenda to shape the ultimate outcome. A group might vote for an amendment that makes a proposal they oppose even worse, knowing the unamended proposal will be enacted but the amended proposal will not. This strategy is possible only if legislators know what is enactable, which allows them to predict the consequences of their behavior. - arm-twisting, securing an outcome with threats and rewards, by forcing some members to vote against their policy goals or constituents. Before doing so, party leaders must determine whether their desired outcome is enactable, for if it is, they can secure it by constructing the right agenda and allowing legislators to vote freely. While much research has focused on showing the utility of these tactics, none incorporate a specification of what is enactable. This specification is not needed to show how these tactics work, but is necessary to assess their potential impact in real-world settings, or to attribute outcomes to their use. For example, a party's success at enacting its program might result from its leaders success at agenda setting, but may also occur because enactable outcomes are naturally biased towards the party, without party leaders doing anything. Without knowing what it enactable, it is impossible to distinguish between these explanations. Our approach characterizes enactability in terms of the uncovered set, a solution concept for spatial voting games. The uncovered set captures the essence of enactability, and is an important refinement in the stream of research into the properties of majority rule. The problem is, no one knows what the uncovered set looks like for a given spatial voting game or real-world situation. As a result, it is impossible to use the uncovered set to make predictions about real-world behavior, or to evaluate the factors driving real-world outcomes. It is impossible to determine whether the uncovered sets predictions differ from those obtained by other methods. And it is impossible to test the uncovered set . to determine whether it is an analytic curiosity or a valuable insight into legislative action. The research we propose aims at addressing all of these questions. Using previous work that implements a computational method for estimating uncovered sets, we will conduct a series of experiments involving group decision-making to see if the uncovered sets behavioral predictions are valid. We will also reanalyze data from classic experiments to see if their findings are consistent with the uncovered set. We will also use our technique to see if the uncovered set is a good predictor of outcomes in the contemporary House. Finally, using a combination of formal analysis and simulations, we will refine our technique, and compare the uncovered sets predictions to those developed from other methods.