Bridging Systems Thinking to Policy Networks: An Application to Developing Network Accountability of Contracting Out System

系統思考與政策網絡之應用: 委外系統網絡課責的建立

10.30409/JPA.200412_(13).0004

李翠萍(Tsuey-Ping Lee)

系統思考 ； 網絡學習 ； 網絡課責 ； 政策網絡 ； Thinking ； Network learning ； Network Accountability ； Policy Networks

公共行政學報

13期（2004 / 12 / 01）

91 - 119

英文

系統思考(systems thinking)和政策網絡(policy networks)在公共政策研究領域中已盛行多年，由于此兩種途徑有許多相似的特質，而同時才者的相异之處又可在政策研究中達到互補的功能，因此，本研究試圖將系統思考與政策網絡相連結，來探討社會福利委外系統中網絡課責(network accountability)的建構。首先，系統思考在「揭露限制系統續效的結構性瑕疵」、與「解釋為何意圖良善的的政策會導致非預期的結果」兩方面，被視為一個有效的工具。同時，系統思考的優點在於提供系統行動者一個積極的觀點來思考各自在系統中所扮演的角色，并采取全方位的整體思維來檢視系統，更提供了「槓桿解」以扮演的角色，而另外一方面，政策網絡的概念強調網絡參與者之間透過資源交換而產生的相互依賴關系，此一概念能夠幫助網絡參與者視自己為資源的擁有者，而非僅是消極回應環境的行動者．本研究認為，課責系統能藉由改變網絡行動者的自我認知(self-recognition)而建構得更為完善，并進而改善系統的續效。

This study intends to propose a combination of systems thinking and policy networks perspective in developing network accountability of contracting out social services. Both systems thinking and policy networks have been applied to public policy analysis for many years. As a perspective that emphasizes the whole rather than fragmented parts, systems thinking has been considered effective in uncovering structural flaws that limit system performance and explaining why a well-intended policy intervention fails to produce expected results. The strength of systems thinking is to offer system actors: 1) an active perspective of their own role in the system, 2) a holistic view of the system, and furthermore, 3) the leverage solutions for improving system performance. On the other hand, the concept of policy networks emphasizes interdependent relations among network participants and their resource exchanges. This concept can help network participants identify themselves as resource holders rather than passive actors merely responding to reality. This paper attempts to examine the similarities and differences between systems thinking and policy networks. This study believes that a system of accountability can be constructed more completely by altering self-recognition of network actors, thus improving system performance.