群决策离散选择模型下的选择集优化
Choice Set Optimization Under Discrete Choice Models of Group Decisions
摘要：
人们做出选择或展示偏好的方式会受到可用选项集（通常称为选择集）的强烈影响。此外，在小群体内的个人层面或大群体的子群体内，通常存在异质性偏好。考虑到选择数据的可用性，现在有许多模型可以捕捉这种行为，以便进行有效的预测-然而，在理解如何直接改变选择集来影响决策者集合的偏好方面，几乎没有工作。在这里，我们使用离散选择模型来开发一个优化框架，这样的干预群体影响力的几个问题，即最大限度地提高协议或分歧，促进特定的选择。我们表明，这些问题一般是NP难的，但施加限制揭示了一个基本的边界：促进选择可以比鼓励共识或播种不和更容易。我们设计的近似算法的硬问题，并表明他们的工作以及现实世界的选择数据。
英文摘要：
The way that people make choices or exhibit preferences can be strongly affected by the set of available alternatives, often called the choice set. Furthermore, there are usually heterogeneous preferences, either at an individual level within small groups or within sub-populations of large groups. Given the availability of choice data, there are now many models that capture this behavior in order to make effective predictions--however, there is little work in understanding how directly changing the choice set can be used to influence the preferences of a collection of decision-makers. Here, we use discrete choice modeling to develop an optimization framework of such interventions for several problems of group influence, namely maximizing agreement or disagreement and promoting a particular choice. We show that these problems are NP-hard in general, but imposing restrictions reveals a fundamental boundary: promoting a choice can be easier than encouraging consensus or sowing discord. We design approximation algorithms for the hard problems and show that they work well on real-world choice data.
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