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RCN: Coordinating and Advancing Analytical Approaches for Policy Design

RCN：协调和推进政策设计的分析方法

基本信息

批准号：
1917908
负责人：
Saba Siddiki
金额：
$ 46.19万
依托单位：
Syracuse University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-15 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1917908&HistoricalAwards=false
关键词：
RCN Coordinating Advancing Analytical Approaches

项目摘要

Effective governance hinges on how individuals interpret and respond to public policies, which are comprised of carefully designed language that conveys who can do what, when, and how. Adequately understanding the link between the design of public policies and behavior requires two complementary advancements. First, scientists must promote intellectual exchange on the development of generalizable and reliable approaches for studying policy designs. Second, these same scientists must coordinate with others focused on assessing how behavior emerges in response to, or sometimes alongside, policy designs. In the former case, policy design scholars confront a trade-off between detailed small-sample case studies that capture nuance in particular contexts, and large-sample studies, which fail to pick up on nuance but generalize across policies and contexts. In the latter case, behavioral scientists confront a different set of challenges. Behavioral scientists have advanced understanding of how individuals interpret and respond to specific instructions embedded in policy designs, but offer few insights about behavioral responses to different combinations of instructions, or how policy design and behavior co-evolve. Exchanging insights with policy design scholars focused on characterizing the language of public policy allows behavioral scientists to effectively respond to such issues. This Research Coordination Network (RCN) initiates an interdisciplinary and international network of policy design scholars and behavioral scientists to harness and integrate their insights to better understand the interplay between policy design and behavior. In support of this objective, RCN participants share and coordinate scientific contributions relating to: (1) theoretical and methodological development of a broadly applicable approach for dissecting the content of public policies, called the Institutional Grammar (IG); (2) development of theoretically informed metrics for evaluating policy designs; and (3) assessments of actual and simulated behavior in policy governed settings. RCN contributions derive from three key focal areas around which the collaboration evolves: (1) IG-Extract: Development of methods on open source software to automatically extract rules from rich text (such as policy documents) and format them into an IG structured database; (2) IG-Analytics: Identification of theoretically-informed metrics for measuring policy designs and evaluating the quality of policies coded in accordance with the IG; and (3) IG-Behave: Exploration of how the IG can be leveraged to support game theoretic, agent based modeling, and large-n observational research, focusing specifically on how policy designs influence actual and simulated behavior, and how behavior evolves in relation to policies. Tools and techniques developed through the RCN are based on principles of Commons-Based Peer Production (CBPP), which emphasizes the use of collaborative platforms and innovative licensing for engaging scholars with common intellectual interests in scientific problem solving. As a package, the RCN contributes understanding regarding how institutional analysis, independently or coupled with behavioral assessments, can be used in the study of policy and governance. Ultimately, the RCN offers guidance for more effective governance in a wide array of policy domains. This guidance is valuable for policy designers responsible for constructing the language of public policies for successfully achieving policy objectives.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

有效的治理取决于个人如何解释和回应公共政策，这些政策由精心设计的语言组成，传达谁可以做什么，何时以及如何做。正确理解公共政策设计与行为之间的联系需要两个互补的进步。第一，科学家必须促进知识交流，以便为研究政策设计制定可推广和可靠的方法。第二，这些科学家必须与其他专注于评估行为如何响应或有时与政策设计一起出现的科学家协调。在前一种情况下，政策设计学者面临着在详细的小样本案例研究和大样本研究之间的权衡，小样本案例研究捕捉了特定背景下的细微差别，大样本研究未能抓住细微差别，但概括了政策和背景。在后一种情况下，行为科学家面临着一系列不同的挑战。行为科学家对个人如何解释和响应嵌入在政策设计中的特定指令有了深入的了解，但对不同指令组合的行为反应，或者政策设计和行为如何共同进化，却没有提供多少见解。与政策设计学者交换见解，专注于描述公共政策的语言，使行为科学家能够有效地应对这些问题。该研究协调网络（RCN）发起了一个跨学科的政策设计学者和行为科学家的国际网络，以利用和整合他们的见解，以更好地了解政策设计和行为之间的相互作用。为了支持这一目标，RCN参与者分享和协调有关以下方面的科学贡献：（1）广泛适用的方法的理论和方法发展，用于剖析公共政策的内容，称为制度语法（IG）;（2）发展理论上知情的指标，用于评估政策设计;（3）评估政策管理环境中的实际和模拟行为。RCN的贡献来自三个关键的重点领域，围绕这三个领域开展合作：（1）IG-Extract：开发开放源码软件的方法，从富文本中自动提取规则（2）IG-Analytics：识别理论上知情的指标，用于衡量政策设计和评估根据IG编码的政策的质量;和（3）IG行为：探索如何利用IG来支持博弈论，基于代理的建模和大n观测研究，特别关注政策设计如何影响实际和模拟行为，以及行为如何与政策相关。通过RCN开发的工具和技术是基于基于共同的同行生产（CBPP）的原则，它强调使用协作平台和创新的许可，让学者在解决科学问题方面具有共同的智力兴趣。作为一个包，RCN有助于了解如何机构分析，独立或与行为评估相结合，可以用于政策和治理的研究。最终，RCN为在广泛的政策领域进行更有效的治理提供了指导。这份指南对于负责构建公共政策语言以成功实现政策目标的政策设计者来说是有价值的。这份奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来获得支持。