混合参与式系统中的价值偏好估计与消歧
Value Preferences Estimation and Disambiguation in Hybrid Participatory Systems
AI总结：
针对混合参与式系统中参与者选择与动机的冲突问题，提出优先考虑动机估计价值的方法及结合NLP与主动学习的消歧策略，在能源转型调查数据集上验证了不一致性解决对价值偏好估计的提升作用。
摘要：
在参与式系统中理解公民的价值观对于以公民为中心的政策制定至关重要。我们设想了一个混合参与式系统，其中参与者做出选择并为这些选择提供动机，AI智能体通过与他们互动来估计其价值偏好。我们关注参与者的选择和动机之间检测到冲突的情况，并提出了通过与参与者互动来解决检测到的不一致性的同时估计价值偏好的方法。我们将“价值评估是审慎后果性的”这一哲学立场付诸实践，即如果参与者的选择基于对价值偏好的审慎考虑，那么价值偏好可以在参与者为选择提供的动机中被观察到。因此，我们提出并比较了将从动机中估计的价值优先于仅从选择中估计的价值的价值偏好估计方法。然后，我们引入了一种结合自然语言处理（NLP）和主动学习（Active Learning）的消歧策略，以解决检测到的选择与动机之间的不一致性。我们在一个关于能源转型的大规模调查数据集上评估了所提出的方法。结果表明，明确解决选择与动机之间的不一致性可以改善对个人价值偏好的估计。与类似基线相比，消歧策略没有显示出实质性的改进——然而，我们讨论了该方法的新颖性如何开辟新的研究途径，并提出了改进建议以解决当前的局限性。
英文摘要：
Understanding citizens' values in participatory systems is crucial for citizen-centric policy-making. We envision a hybrid participatory system where participants make choices and provide motivations for those choices, and AI agents estimate their value preferences by interacting with them. We focus on situations where a conflict is detected between participants' choices and motivations, and propose methods for estimating value preferences while addressing detected inconsistencies by interacting with the participants. We operationalize the philosophical stance that "valuing is deliberatively consequential." That is, if a participant's choice is based on a deliberation of value preferences, the value preferences can be observed in the motivation the participant provides for the choice. Thus, we propose and compare value preferences estimation methods that prioritize the values estimated from motivations over the values estimated from choices alone. Then, we introduce a disambiguation strategy that combines Natural Language Processing and Active Learning to address the detected inconsistencies between choices and motivations. We evaluate the proposed methods on a dataset of a large-scale survey on energy transition. The results show that explicitly addressing inconsistencies between choices and motivations improves the estimation of an individual's value preferences. The disambiguation strategy does not show substantial improvements when compared to similar baselines--however, we discuss how the novelty of the approach can open new research avenues and propose improvements to address the current limitations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
