Automated design of social choice rules by optimization and rule extraction

本研究项目的目标是通过结合现有的自动化机制设计技术（只能应用于小问题实例）和规则发现技术，开发一种自动生成可应用于大规模问题实例的社会选择规则的方法。在本研究中，我们首先开发了实现这一目标的重要核心技术，即基于自动化机制设计的结果提取满足期望属性的社会选择规则的方法。这一成果在信息技术论坛（FIT）上发表，并获得了最佳论文奖（Funai最佳论文奖）。此外，现有的自动机构设计技术使用整数规划来获得给定准则的最优规则。因此，参与者的类型必须表示为一组离散值。我们开发了一种新的方法来直接获得社会选择规则基于另一种优化技术称为量词消去，它可以处理非线性，连续变量。

The goal of this research project is to develop a method for automatically generating social choice rules that can be applied to large-scale problem instances by combining existing automated mechanism design techniques (which can be applied only for small-scare problem instances) and rule discovery techniques. In this research, we first develop an important core technology for achieving this goal, i.e., a method for extracting social choice rules that satisfy desirable properties based on the results obtained by automated mechanism design. This result was presented at Forum on Information Technology (FIT) and obtained the best paper award (Funai best paper award). Furthermore, the existing automated mechanism design techniques use integer programming to obtain optimal rules for a given criterion. As a result, the types of participants must be represented as a set of discrete values. We develop a new method for directly obtaining social choice rules based on another optimization technique called quantifier elimination, which can handle non-linear, continuous variables.