论文标题
在离散选择小组决策模型下的选择集优化
Choice Set Optimization Under Discrete Choice Models of Group Decisions
论文作者
论文摘要
人们做出选择或表现偏好的方式可能会受到一组可用替代方案的强烈影响，通常称为选择集。此外，通常在小组内的个体级别或大型群体中的个人级别上都有异质偏好。鉴于选择数据的可用性，现在有许多模型捕获这种行为以做出有效的预测 - 但是，在理解如何直接更改选择集的工作中几乎没有工作来影响决策者集合的偏好。在这里，我们使用离散的选择建模来开发这种干预措施的优化框架，以解决小组影响的几个问题，即最大化一致性或分歧并促进特定选择。我们表明，这些问题通常是NP坚硬的，但是施加的限制揭示了一个基本的边界：促进选择比鼓励共识或播种不和谐更容易。我们为严重问题设计了近似算法，并表明它们在现实世界选择数据上效果很好。
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.
