On Modeling Human Perceptions of Allocation Policies with Uncertain Outcomes
On Modeling Human Perceptions of Allocation Policies with Uncertain Outcomes
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DOI：
10.1145/3465456.3467617
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发表时间：
2021-03
期刊：
影响因子：
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通讯作者：
Hoda Heidari;Solon Barocas;J. Kleinberg;K. Levy
中科院分区：
文献类型：
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作者：
Hoda Heidari;Solon Barocas;J. Kleinberg;K. Levy
许多政策分配的危害或利益在本质上是不确定的：它们在人口中产生分布，其中个人有不同的概率遭受危害或受益。因此，比较不同的政策涉及到比较它们相应的概率分布，我们观察到，在许多情况下，在实践中选择的政策很难用仅仅基于它们产生的总损害或利益的预期值的偏好来解释。在期望值分析不是一个充分的解释框架的情况下，什么是社会偏好的合理模型？在这里，我们调查基于行为科学的概率加权框架的解释，几十年来，行为科学已经确定了人们如何感知概率的系统性偏见。我们表明，概率加权可以用来预测的概率分布的伤害和好处，功能完全不同的期望值分析的偏好，并在一些情况下提供了潜在的解释政策偏好，似乎很难激励其他手段。特别是，我们确定最佳的政策，最大限度地减少感知总伤害和感知总收益最大化，考虑到概率加权的扭曲效应，我们讨论了一些现实世界的政策，类似于这样的分配策略。我们的分析并不提供具体的政策选择建议，而是从根本上解释性的，试图描述观察到的现象在政策选择。
Many policies allocate harms or benefits that are uncertain in nature: they produce distributions over the population in which individuals have different probabilities of incurring harm or benefit. Comparing different policies thus involves a comparison of their corresponding probability distributions, and we observe that in many instances the policies selected in practice are hard to explain by preferences based only on the expected value of the total harm or benefit they produce. In cases where the expected value analysis is not a sufficient explanatory framework, what would be a reasonable model for societal preferences over these distributions? Here we investigate explanations based on the framework of probability weighting from the behavioral sciences, which over several decades has identified systematic biases in how people perceive probabilities. We show that probability weighting can be used to make predictions about preferences over probabilistic distributions of harm and benefit that function quite differently from expected-value analysis, and in a number of cases provide potential explanations for policy preferences that appear hard to motivate by other means. In particular, we identify optimal policies for minimizing perceived total harm and maximizing perceived total benefit that take the distorting effects of probability weighting into account, and we discuss a number of real-world policies that resemble such allocational strategies. Our analysis does not provide specific recommendations for policy choices, but is instead fundamentally interpretive in nature, seeking to describe observed phenomena in policy choices.