论文标题
通过神经网络重新分布公共项目问题
Redistribution in Public Project Problems via Neural Networks
论文作者
论文摘要
多基因系统中的许多重要问题涉及资源分配。如果这样做会增加自己的公用事业，那么自我利益的代理可能会撒谎。因此，有必要使用所需的属性和目标设计机制（集体决策规则）。 VCG的再分配机制是有效的（重视资源最重视资源的代理商），防止策略（代理没有动机来衡量其估值），并且预算平衡较弱（没有赤字）。 我们专注于经典公共项目问题的VCG再分配机制，其中一组代理需要决定是否建立不可判有的公共项目。我们通过具有两个福利最大化目标的神经网络设计机制：在最坏情况下最佳，并且预期最佳。先前的研究表明，三种代理的两种最差的最佳机制，但尚未确定3种代理的最佳最佳机制。为了最大化预期福利，没有现有的结果。 我们使用神经网络设计VCG重新分布机制。神经网络已被用来设计具有单位需求的多单元拍卖的再分配机制。我们表明，对于公共项目问题，先前提出的神经网络，这导致了具有单位需求的多单元拍卖的最佳/近乎最佳的机制，对公共项目问题的表现非常糟糕。我们在多个方面显着改善了现有网络：我们进行了一个GAN网络，以生成最坏情况类型的概况并将先前的分布提供到损失函数中，以提供最佳期望目标的质量梯度……
Many important problems in multiagent systems involve resource allocations. Self-interested agents may lie about their valuations if doing so increases their own utilities. Therefore, it is necessary to design mechanisms (collective decision-making rules) with desired properties and objectives. The VCG redistribution mechanisms are efficient (the agents who value the resources the most will be allocated), strategy-proof (the agents have no incentives to lie about their valuations), and weakly budget-balanced (no deficits). We focus on the VCG redistribution mechanisms for the classic public project problem, where a group of agents needs to decide whether or not to build a non-excludable public project. We design mechanisms via neural networks with two welfare-maximizing objectives: optimal in the worst case and optimal in expectation. Previous studies showed two worst-case optimal mechanisms for 3 agents, but worst-case optimal mechanisms have not been identified for more than 3 agents. For maximizing expected welfare, there are no existing results. We use neural networks to design VCG redistribution mechanisms. Neural networks have been used to design the redistribution mechanisms for multi-unit auctions with unit demand. We show that for the public project problem, the previously proposed neural networks, which led to optimal/near-optimal mechanisms for multi-unit auctions with unit demand, perform abysmally for the public project problem. We significantly improve the existing networks on multiple fronts: We conduct a GAN network to generate worst-case type profiles and feed prior distribution into loss function to provide quality gradients for the optimal-in-expectation objective......
