用系统思维+外部性分析,找出优化决策的隐藏代价和影响者。
Rethinking Optimization: A Systems-Based Approach to Social Externalities
- 结合系统思维与经济外部性理论,识别被忽视的利益相关方。
- 揭示三种常见问题:忽视、错误评估、短期目标优先导致的负面后果。
- 适合关注政策设计、算法伦理与复杂系统治理的研究者参考。
优化广泛应用于各类决策场景,虽提升效率却常因实施不当引发意外后果,尤其在涉及显著外部性的社会经济情境中更为突出。外部性指优化过程之外第三方所承受的成本或收益,其影响往往未被纳入考量。本文提出一种融合系统思维与外部性概念的框架,旨在厘清问题根源:何人受影响、如何受影响、何时及如何将他们纳入优化流程。经济外部性及其量化方法有助于识别利益相关方及其影响程度;系统思维则提供整体性视角,揭示外部性间的关联、反馈回路及介入时机。该框架系统分析了三类典型不良实践:忽视外部性、量化错误以及过度追求短期目标,从而为优化决策中的非预期后果提供可操作的改进路径。
原文摘要 · Abstract (English)
Optimization is widely used for decision making across various domains, valued for its ability to improve efficiency. However, poor implementation practices can lead to unintended consequences, particularly in socioeconomic contexts where externalities (costs or benefits to third parties outside the optimization process) are significant. To propose solutions, it is crucial to first characterize involved stakeholders, their goals, and the types of subpar practices causing unforeseen outcomes. This task is complex because affected stakeholders often fall outside the direct focus of optimization processes. Also, incorporating these externalities into optimization requires going beyond traditional economic frameworks, which often focus on describing externalities but fail to address their normative implications or interconnected nature, and feedback loops. This paper suggests a framework that combines systems thinking with the economic concept of externalities to tackle these challenges. This approach aims to characterize what went wrong, who was affected, and how (or where) to include them in the optimization process. Economic externalities, along with their established quantification methods, assist in identifying "who was affected and how" through stakeholder characterization. Meanwhile, systems thinking (an analytical approach to comprehending relationships in complex systems) provides a holistic, normative perspective. Systems thinking contributes to an understanding of interconnections among externalities, feedback loops, and determining "when" to incorporate them in the optimization. Together, these approaches create a comprehensive framework for addressing optimization's unintended consequences, balancing descriptive accuracy with normative objectives. Using this, we examine three common types of subpar practices: ignorance, error, and prioritization of short-term goals.
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