用机器学习模型预测和解释:社会科学作为试金石
Oliver Buchholz1, Thomas Grote1
1University of Tübingen, Cluster of Excellence: "Machine Learning: New Perspectives for Science", Ethics and Philosophy Lab, Maria von Linden Str. 6, D-72076 Tübingen, Germany.
Studies in history and philosophy of science
|November 4, 2024
概括
机器学习 (ML) 模型在自然科学方面表现有希望,但在社会科学方面却扎不前. 本研究提出了一种综合性方法,将解释和ML模型结合起来,以提高社会科学研究中的预测成功率.
科学领域:
- 社会科学 社会科学 社会科学
- 计算社会科学 计算社会科学
- 预测建模预测建模
背景情况:
- 机器学习 (ML) 模型在自然科学中的预测任务中取得了重大成功.
- 然而,它们在社会科学中的应用和好处,特别是在预测生命轨迹方面,仍然不那么明显,传统的指标往往不成功.
研究的目的:
- 调查社会科学预测中的ML模型性能差距背后的原因.
- 突出预测作为社会科学的关键目标,以及解释.
- 提出一种新的建模方法,以提高社会科学中ML模型的有效性.
主要方法:
- 对两个社会科学案例研究与自然科学范式进行比较分析.
- 在社会科学背景下,识别阻碍纯ML预测的特定约束.
- 开发一个整合性建模框架.
主要成果:
- 该研究确定了阻碍社会科学中纯ML预测成功的关键约束因素.
- 它强调了预测作为社会科学的科学目标的重要性,与解释相比.
- 拟议的综合性方法提供了一个潜在的解决方案,以弥合业绩差距.
结论:
- 将解释性模型与预测性ML模型相结合,对于推进社会科学研究至关重要.
- 这种混合方法可以克服复杂社会系统中纯粹的ML预测的局限性.
- 未来的研究应该专注于实施和验证这些综合战略.
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