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首页> 外文期刊>The Journal of Artificial Intelligence Research >Iterative Local Voting for Collective Decision-making in Continuous Spaces
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Iterative Local Voting for Collective Decision-making in Continuous Spaces

机译：连续空间中集体决策的迭代局部投票

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摘要

Many societal decision problems lie in high-dimensional continuous spaces not amenable to the voting techniques common for their discrete or single-dimensional counterparts. These problems are typically discretized before running an election or decided upon through negotiation by representatives. We propose a algorithm called Iterative Local Voting for collective decision-making in this setting. In this algorithm, voters are sequentially sampled and asked to modify a candidate solution within some local neighborhood of its current value, as defined by a ball in some chosen norm, with the size of the ball shrinking at a specified rate. We first prove the convergence of this algorithm under appropriate choices of neighborhoods to Pareto optimal solutions with desirable fairness properties in certain natural settings: when the voters' utilities can be expressed in terms of some form of distance from their ideal solution, and when these utilities are additively decomposable across dimensions. In many of these cases, we obtain convergence to the societal welfare maximizing solution. We then describe an experiment in which we test our algorithm for the decision of the U.S. Federal Budget on Mechanical Turk with over 2,000 workers, employing neighborhoods defined by various L-Norm balls. We make several observations that inform future implementations of such a procedure.
机译：许多社会决策问题都存在于高维连续空间中，这些空间不适合其离散或一维对应物所共有的投票技术。这些问题通常在进行选举之前离散化，或者由代表协商确定。对于这种情况下的集体决策，我们提出了一种称为迭代局部投票的算法。在该算法中，按顺序对选民进行采样，并要求其修改其当前值的某个局部邻域内的候选解决方案（如某个选定准则中的球所定义），并且球的大小以指定的比率缩小。我们首先证明下社区与某些天然设置理想的公平性帕累托最优解的适当选择该算法的收敛：当选民的实用程序可以从他们的理想解决方案，某种形式的距离来表示，而当这些工具在各个维度上可加分解。在许多情况下，我们都趋向于实现社会福利最大化解决方案。然后，我们描述了一个实验，在该实验中，我们测试了算法的使用情况，该算法用于根据超过2000名工人的机械土耳其人的美国联邦预算，采用由各种L-Norm球定义的邻域。我们提出了一些意见，这些意见可为此类程序的未来实现提供依据。

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