{"title":"逆向工程和策略设计","authors":"R. Weaver","doi":"10.4337/9781788118194.00020","DOIUrl":null,"url":null,"abstract":"A mechanistic perspective on policy analysis and design has been described as focused on “a theory of a system of interlocking parts that transmit causal forces from X to Y” (Beach and Pedersen, 2013, p. 29). Hedström and Ylikoski (2010, p. 53) argue that a “mechanism-based explanation describes the causal process selectively. It does not aim at an exhaustive account of all details but seeks to capture the crucial elements of the process by abstracting away the irrelevant details.” In the approach used in this volume, first-order causal mechanisms are seen as those that “alter the behavior of individuals, groups and structures to achieve a specific outcome” through use of policy activators embedded in government policy (Capano, Howlett and Ramesh, Chapter 1 this volume). 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引用次数: 2
摘要
政策分析和设计的机械论观点被描述为专注于“将因果力从X传递到Y的连锁部分系统的理论”(Beach和Pedersen, 2013,第29页)。Hedström和Ylikoski (2010, p. 53)认为“基于机制的解释选择性地描述了因果过程”。它的目的不是详尽地描述所有细节,而是通过抽象掉不相关的细节来捕捉过程的关键要素。”在本卷中使用的方法中,一阶因果机制被视为那些通过使用嵌入政府政策的政策激活因子“改变个人、群体和结构的行为以达到特定结果”的机制(Capano, Howlett和Ramesh,本卷第一章)。二级因果机制是利用在个人和集体行为中起作用的机制的知识,为政策“激活者”的修订提供信息。本章考察了在政策部门中起作用的一级和二级因果机制,许多政府都试图通过使用政策激活因素:家庭退休储蓄来影响政策部门。它利用这一分析,为政策研究中因果机制的理解和利用,特别是逆向工程的潜力和局限性,得出更广泛的影响,即利用对因果机制如何运作的理解来设计预测结果“与理想结果一致”的机制(Maskin, 2008,第567页;由政府寻求的。因此,逆向工程可以看作是二阶机制的一种形式。在这一分析中,政府政策激活因素是影响个人和家庭退休储蓄决定(并最终影响总体水平)的几个因素之一。在本卷中使用的术语中,从图10.1所示的简化因果模型的第二阶段到第三阶段的因果机制——政策激活因素和其他可能影响个人和家庭退休储蓄行为的因素——都是一阶因果机制。退休储蓄行为反过来影响
A mechanistic perspective on policy analysis and design has been described as focused on “a theory of a system of interlocking parts that transmit causal forces from X to Y” (Beach and Pedersen, 2013, p. 29). Hedström and Ylikoski (2010, p. 53) argue that a “mechanism-based explanation describes the causal process selectively. It does not aim at an exhaustive account of all details but seeks to capture the crucial elements of the process by abstracting away the irrelevant details.” In the approach used in this volume, first-order causal mechanisms are seen as those that “alter the behavior of individuals, groups and structures to achieve a specific outcome” through use of policy activators embedded in government policy (Capano, Howlett and Ramesh, Chapter 1 this volume). Second-order causal mechanisms are the use of knowledge about mechanisms at work in individual and collective behaviors to inform revisions to policy “activators.” This chapter examines the firstand second-order causal mechanisms at work in a policy sector that many governments have tried to influence through use of policy activators: retirement savings by households. It uses that analysis to draw broader implications for the understanding and utilization of causal mechanisms in policy research, and in particular the potential of and limitations on reverse engineering, that is, using an understanding of how causal mechanisms operate to design mechanisms whose predicted outcomes “coincide with the desirable outcome” (Maskin, 2008, p. 567; emphasis in original) sought by government. Reverse engineering can thus be seen as one form of second-order mechanism. In this analysis, government policy activators are one of several factors that shape individual and household decisions on (and ultimately aggregate levels of) retirement savings. In the terminology used in this volume, the causal mechanisms at work in moving from Stage 2 to Stage 3 of the simplified causal model shown in Figure 10.1 – both policy activators and other factors that may influence individual and household retirement savings behavior – are first-order causal mechanisms. Retirement savings behavior in turn affects