应对理论不确定时的归纳风险：基于部分识别的决策制定
Coping with Inductive Risk When Theories are Underdetermined: Decision Making with Partial Identification
AI总结：
本文探讨了在理论不确定情况下，通过部分识别方法进行经济政策分析，以降低归纳风险并提升决策可信度。
摘要：
关于理论不确定性的意义在科学哲学和实践中仍存在争议。当研究用于决策时，这种问题具有实际重要性，因为科学不确定性导致归纳风险。本文旨在增强哲学家与公共政策研究者之间的沟通，描述了部分识别的计量经济学分析及其在福利经济政策分析中的应用。部分识别研究发现，不确定性和归纳风险对预测重要社会结果的可信预测以及可信的公共决策具有高度相关性。它提供了数学工具，以表征当可用数据和有根据的假设结合以预测人口结果时出现的一类广泛科学不确定性。将部分识别研究与不确定性下的合理决策标准相结合，可以产生一致的方法，以在不接受多个经验上不确定的理论之一的情况下做出政策选择。本文认为，部分识别研究应得到对不确定性和归纳风险的哲学讨论的关注。
英文摘要：
Controversy about the significance of underdetermination of theories persists in the philosophy and conduct of science. The issue has practical import when research is used to inform decision making, because scientific uncertainty yields inductive risk. Seeking to enhance communication between philosophers and researchers who study public policy, this paper describes econometric analysis of partial identification and its use in welfare-economic policy analysis. Study of partial identification finds underdetermination and inductive risk to be highly consequential for credible prediction of important societal outcomes and, hence, for credible public decision making. It provides mathematical tools to characterize a broad class of scientific uncertainties that arise when available data and well-supported assumptions are combined to predict population outcomes. Combining study of partial identification with criteria for reasonable decision making under uncertainty yields coherent approaches to make policy choices without accepting one among multiple empirically underdetermined theories. The paper argues that study of partial identification warrants attention in philosophical discourse on underdetermination and inductive risk.
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