RCN: Coordinating and Advancing Analytical Approaches for Policy Design

点击翻译按钮获取中文摘要

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.