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摘要翻译：
许多现实世界中与伦理相关的情况，特别是那些大规模社会选择的情况，如减缓气候变化，不仅涉及许多主体，其决策以复杂的方式相互作用，而且还涉及各种形式的不确定性，包括可量化的风险和不可量化的模糊性。在这类问题中，评估个人和群体对道德上不受欢迎的结果的道德责任或他们避免这种结果的责任是具有挑战性的，并容易产生责任认定不足或过高的风险。与基于严格因果关系或某些道义逻辑的现有方法不同，这些方法侧重于“负责”与“不负责”的二元分类，我们在这里提出了几种不同的定量责任度量，以概率为单位评估责任程度。为此，我们使用了一个基于扩展形式博弈树的改编版本的框架和一个公理方法，该方法指定了这些度量的许多潜在的期望性质，然后通过将它们应用于许多典型的社会选择情况来测试所开发的候选度量。我们发现，虽然这种责任度量的大多数属性可以通过某种变体来实现，但还没有找到一个明显优于其他度量的最佳度量。
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英文标题：
《Degrees of individual and groupwise backward and forward responsibility
in extensive-form games with ambiguity, and their application to social
choice problems》
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作者：
Jobst Heitzig and Sarah Hiller
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最新提交年份：
2020
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分类信息：
一级分类：Economics 经济学
二级分类：Theoretical Economics 理论经济学
分类描述：Includes theoretical contributions to Contract Theory, Decision Theory, Game Theory, General Equilibrium, Growth, Learning and Evolution, Macroeconomics, Market and Mechanism Design, and Social Choice.
包括对契约理论、决策理论、博弈论、一般均衡、增长、学习与进化、宏观经济学、市场与机制设计、社会选择的理论贡献。
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一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
Many real-world situations of ethical relevance, in particular those of large-scale social choice such as mitigating climate change, involve not only many agents whose decisions interact in complicated ways, but also various forms of uncertainty, including quantifiable risk and unquantifiable ambiguity. In such problems, an assessment of individual and groupwise moral responsibility for ethically undesired outcomes or their responsibility to avoid such is challenging and prone to the risk of under- or overdetermination of responsibility. In contrast to existing approaches based on strict causation or certain deontic logics that focus on a binary classification of `responsible' vs `not responsible', we here present several different quantitative responsibility metrics that assess responsibility degrees in units of probability. For this, we use a framework based on an adapted version of extensive-form game trees and an axiomatic approach that specifies a number of potentially desirable properties of such metrics, and then test the developed candidate metrics by their application to a number of paradigmatic social choice situations. We find that while most properties one might desire of such responsibility metrics can be fulfilled by some variant, an optimal metric that clearly outperforms others has yet to be found.
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PDF链接：
https://arxiv.org/pdf/2007.07352
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