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| 題 名 | 機器學習應用於公共政策決策實務需求之探討=A Study on the Application of Machine Learning for Public Policy Decision-Making |
|---|---|
| 作 者 | 張世賢; | 書刊名 | 科際整合月刊 |
| 卷 期 | 10:5 2025.05[民114.05] |
| 頁 次 | 頁58-78 |
| 分類號 | 572.9 |
| 關鍵詞 | 機器學習; 公共政策決策; 公共政策執行; 公共政策評估; 公共政策監督; Machine learning; Public policy decision-making; Public policy implementation; Public policy evaluation; Public policy supervision; |
| 語 文 | 中文(Chinese) |
| 中文摘要 | 對於公共政策決策實務,從市場的觀點,就有供給方與需求方。本文專注需求方。誰需要將機器學習應用於公共政策決策?1.政策決策人員,需要應用機器學習,產生決策知識的合理性、可信任性,據以決策、2. 政策執行人員,需要應用機器學習,獲得執行知識的合理性、可信任性,據以執行;並在特定情況,據以決定是否介入、如何妥適介入。3. 受政策影響之民眾,最主要的是政策的監督者、民意代表,需要應用機器學習,獲得政策監督的知識合理性、可信任性,據以評論政策缺失。 本文採用次級資料分析法,蒐集最新機器學習應用於公共政策決策實務需求之論文,加以彙總分析:這些需求者是否達成其需求之具體目標,有否落實,有否差距,差距之原因,以及如何改善。 本論文貢獻在1.實務言,提供對政策決策者、政策執行人員和受影響的民眾。包括政策監督人員,提供有效利用機器學習達成政策決策需求目標之改善。2.學理言,提供改善機器學習應用於公共決策效果之創見;強調數據與預測之重要性。 |
| 英文摘要 | In the practice of public policy decision-making, from the market perspective, there are supply sides and demand sides. This article focuses on the demand side. Who needs to apply machine learning to public policy decisions? 1. Policy decision-makers need to apply machine learning to generate rational and credible decision-making knowledge for decision-making; 2. Policy implementers need to apply machine learning to obtain timely and sufficient implementation knowledge for implementation; and in specific situations, decide whether to intervene and how to intervene appropriately. 3. The people affected by the policy, most importantly the policy supervisors and representatives of public opinion, need to apply machine learning to obtain the knowledge rationality and credibility of policy supervision and use it to comment on policy deficiencies. This article uses secondary data analysis to collect the latest papers on the application of machine learning to the practical needs of public policy decision-making, and summarizes and analyzes them: whether these demanders have achieved the specific goals of their needs, whether they have been implemented, whether there are gaps, the reasons for the gaps, and how to improve them. The contributions of this paper are: 1. Practical advice to policy makers, policy implementers and affected people including policy supervisors, providing improvements in the effective use of machine learning to achieve policy decision-making goals. 2. Theoretical analysis: provide insights to improve the effectiveness of machine learning in public decision-making; emphasize the importance of data and prediction. |
本系統中英文摘要資訊取自各篇刊載內容。