让不确定预测更可信：通过双向沟通实现真实披露。
Truthful Elicitation of Imprecise Forecasts
- 用集合形式表示预测，结合决策者聚合规则反向激励真实反馈。
- 在不确定条件下，仍能通过随机化评分规则实现诚实报告。
- 适合安全关键领域，帮助决策者管理预测中的认知不确定性。
概率预测的质量对不确定性下的决策至关重要。尽管合理评分规则可激励精确预测的诚实报告，但在预测者对其信念存在认知不确定性时，这些规则效果有限，限制了其在安全关键领域的应用。为此，我们提出一种针对模糊预测（以信念集合形式给出）的评分框架。尽管确定性评分规则存在不可能性结果，我们通过连接社会选择理论，引入双向通信机制：决策者先披露其下游决策中用于解决预测模糊性的聚合规则（如平均或极小极大）。这一信息帮助预测者在获取阶段化解犹豫。进一步证明，通过在聚合过程上随机化合理评分规则，可实现模糊预测的诚实披露。该方法使决策者能够获取并整合预测者的认知不确定性，从而提升决策可信度。
原文摘要 · Abstract (English)
The quality of probabilistic forecasts is crucial for decision-making under uncertainty. While proper scoring rules incentivize truthful reporting of precise forecasts, they fall short when forecasters face epistemic uncertainty about their beliefs, limiting their use in safety-critical domains where decision-makers (DMs) prioritize proper uncertainty management. To address this, we propose a framework for scoring imprecise forecasts -- forecasts given as a set of beliefs. Despite existing impossibility results for deterministic scoring rules, we enable truthful elicitation by drawing connection to social choice theory and introducing a two-way communication framework where DMs first share their aggregation rules (e.g., averaging or min-max) used in downstream decisions for resolving forecast ambiguity. This, in turn, helps forecasters resolve indecision during elicitation. We further show that truthful elicitation of imprecise forecasts is achievable using proper scoring rules randomized over the aggregation procedure. Our approach allows DM to elicit and integrate the forecaster's epistemic uncertainty into their decision-making process, thus improving credibility.
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