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响应公共政策，这些政策由精心设计的语言组成，这些语言传达了可以做什么，何时以及如何做的。充分了解公共政策和行为设计之间的联系需要两个互补的进步。首先，科学家必须促进知识交流，以开发可概括和可靠的研究政策设计方法。其次，这些相同的科学家必须与其他专注于评估行为如何应对政策设计或有时以及有时以及有时与政策设计的方式进行协调。在前一种情况下，政策设计学者在详细的小样本案例研究之间面临着一个权衡，这些小样本案例研究在特定情况下捕捉细微差别，而大样本研究未能提高细微差别，但跨越了政策和环境。在后一种情况下，行为科学家面临着不同的挑战。行为科学家对个人如何解释和响应政策设计中嵌入的特定指令有了深入的了解，但很少有关于对不同指令组合或政策设计和行为共同发展的行为响应的见解。与专注于表征公共政策语言的政策设计学者交流见解，使行为科学家可以有效地应对此类问题。该研究协调网络（RCN）启动了政策设计学者和行为科学家的跨学科和国际网络，以利用和整合他们的见解，以更好地了解政策设计与行为之间的相互作用。为了支持这一目标，RCN参与者共享并协调与以下方面的科学贡献：（1）理论和方法论开发一种广泛适用的方法，用于剖析公共政策的内容，称为机构语法（IG）；（2）开发理论知情的指标来评估政策设计；（3）评估政策规范设置中的实际和模拟行为。 RCN的贡献源自协作发展的三个关键焦点区域：（1）IG-INFRACT：开源软件上的方法开发方法，以自动从富文本（例如策略文档）中提取规则并将其格式化为IG结构化数据库；（2）IG-Analytics：识别用于衡量策略设计和根据IG编码的策略质量的理论信息指标的识别；（3）Ig-Behave：探索如何利用IG来支持游戏理论，基于代理的建模和大型N观察性研究，专门关注政策设计如何影响实际和模拟行为，以及行为如何在与政策的关系中演变。通过RCN开发的工具和技术是基于基于公共的同行生产原则（CBPP），该原则强调使用协作平台和创新许可，以使学者在科学问题解决方面具有共同的知识分子利益。作为一揽子计划，RCN对制度分析如何独立或与行为评估相结合的方式有助于了解政策和治理研究。最终，RCN为在广泛的政策领域提供更有效的治理提供了指导。该指南对于负责成功实现政策目标的公共政策语言的政策设计师来说是有价值的。该奖项反映了NSF的法定使命，并使用基金会的知识分子优点和更广泛的影响审查标准，认为值得通过评估来获得支持。