Well-resourced and well-connected individuals, or “policy entrepreneurs,” often play an important role in advocating and securing the adoption of policies. There is a striking lack of inquiry into the ways that social networks shape the ability of these actors to achieve their aims, including the ways in which network ties may channel policy conflict. To address these gaps, we analyze data from an original survey and an original database of policies to assess the success of policy entrepreneurs active in a highly contentious arena: municipal policymaking concerning high-volume hydraulic fracturing (HVHF) in New York. We use text-mining to collect social network data from local newspaper archives, then use those data to construct municipal HVHF policy networks. Municipal anti-HVHF policy entrepreneurs appear more successful when they operate in less cohesive networks, act as bridges to relative newcomers to the governance network, and have a larger number of network connections. Pro-HVHF policy entrepreneurs appear more successful when they can forge high-value connections to key decision-makers. Policy entrepreneurs on both sides of the issue are more successful when they have a greater number of sympathetic coalition partners.
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SPID: A New Database for Inferring Public Policy Innovativeness and Diffusion Networks
Despite its rich tradition, there are key limitations to researchers' ability to make generalizable inferences about state policy innovation and diffusion. This paper introduces new data and methods to move from empirical analyses of single policies to the analysis of comprehensive populations of policies and rigorously inferred diffusion networks. We have gathered policy adoption data appropriate for estimating policy innovativeness and tracing diffusion ties in a targeted manner (e.g., by policy domain, time period, or policy type) and extended the development of methods necessary to accurately and efficiently infer those ties. Our state policy innovation and diffusion (SPID) database includes 728 different policies coded by topic area. We provide an overview of this new dataset and illustrate two key uses: (i) static and dynamic innovativeness measures and (ii) latent diffusion networks that capture common pathways of diffusion between states across policies. The scope of the data allows us to compare patterns in both across policy topic areas. We conclude that these new resources will enable researchers to empirically investigate classes of questions that were difficult or impossible to study previously, but whose roots go back to the origins of the political science policy innovation and diffusion literature.
more » « less- NSF-PAR ID:
- 10457799
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Policy Studies Journal
- Volume:
- 48
- Issue:
- 2
- ISSN:
- 0190-292X
- Page Range / eLocation ID:
- p. 517-545
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract This paper presents an implementation of Connected Spaces (CxS)—an ambient help seeking interface designed and developed for a project‐based computing classroom. We use actor network theory (ANT) to provide an underutilized posthumanist lens to understand the creation of collaborative connections in this Computational Action‐based implementation. Posthumanism offers an emerging and critical extension to sociocultural perspectives on understanding learning, by pushing us to decenter the human, and consider the active roles that human and non‐human entities play in learning environments by actively shaping each other. We analyse how students in this class adjusted their help‐seeking and collaborative habits following the introduction of CxS, a tool designed to foster (more inter‐group) collaboration. ANT proposes generalized symmetry—a principle of considering human, non‐human and more than human entities with equivalent and comparable agency, leading to describing phenomena as networks of actors in different evolving relationships with each other. Analysing collaborative interactions as fostered by CxS using an ANT approach supports design‐based research—an iterative design revision process highlighting understandings about design as well as learning—by providing a temporal and informative lens into the relationship between actors and tools within the environment. Our key findings include a framing of technologies in classrooms as bridging
agentic gaps between students and becoming actors engaging in different behaviours; learners enacting new agencies through technologies (for instance a more comfortable non‐intrusive help seeker), and the need for voicing and teachers to connect help networks in CxS equipped classrooms.Practitioner notes What is already known about this topic
Collaborative learning is a valuable skill and practice; opportunities to mentor others are critical in empowering minoritized learners, especially in STEM and computing disciplines.
School norms solidify a power and expertise hierarchy between teachers and learners and fail to productively support learners in learning from each other.
Additionally, lack of awareness about peers' knowledge is a common hindrance in students knowing who to ask for help and how.
What this paper adds
An example of a designed interface called Connected Spaces with potential to foster more inter‐student collaboration, especially outside of mandated within‐group collaboration—in the form of cross‐group help seeking and help giving.
A design based research study using actor network theory highlighting the limitations of Connected Spaces in sparking notable behaviour change among students by itself but being retooled as a teacher support tool in enabling cross‐group collaborations.
Presenting conceptions of collaboration through technologies as bridging agentic gaps and acting with new agencies in performing help‐seeking related actions.
Provoking the idea of testing emerging technologies in classrooms along with sharing our analyses and reflections with the classroom as a key idea in computing education—surfacing the gap between designed intentions and the different kinds of extra social work needed in the on‐ground success of different technologies.
Implications for practice and/or policy
Designers and researchers should create and test more interfaces alongside teachers across different classrooms and contexts aimed at supporting different kinds of voluntary collaborative interactions.
Curricula, standards and school practices should further center providing students with opportunities to engage as mentors and build communities of learning across disciplines to empower minoritized students.
Researchers engaging in design based research should consider using more posthumanist lenses to examine educational technologies and how they affect change in learning environments.
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Policy Points Promoting healthy public policies is a national priority, but state policy adoption is driven by a complex set of internal and external factors.
This study employs new social network methods to identify underlying connections among states and to predict the likelihood of new firearm‐related policy adoption given changes to this interstate network.
This approach could be used to assess the likelihood that a given state will adopt a specific new firearm‐related law and to identify points of influence that could either inhibit or promote wider diffusion of specific laws.
Context US states are largely responsible for the regulation of firearms within their borders. Each state has developed a different legal environment with regard to firearms based on different values and beliefs of citizens, legislators, governors, and other stakeholders. Predicting the types of firearm laws that states may adopt is therefore challenging.
Methods We propose a parsimonious model for this complex process and provide credible predictions of state firearm laws by estimating the likelihood they will be passed in the future. We employ a temporal exponential‐family random graph model to capture the bipartite state law–state network data over time, allowing for complex interdependencies and their temporal evolution. Using data on all state firearm laws over the period 1979–2020, we estimate these models’ parameters while controlling for factors associated with firearm law adoption, including internal and external state characteristics. Predictions of future firearm law passage are then calculated based on a number of scenarios to assess the effects of a given type of firearm law being passed in the future by a given state.
Findings Results show that a set of internal state factors are important predictors of firearm law adoption, but the actions of neighboring states may be just as important. Analysis of scenarios provide insights into the mechanics of how adoption of laws by specific states (or groups of states) may perturb the rest of the network structure and alter the likelihood that new laws would become more (or less) likely to continue to diffuse to other states.
Conclusions The methods used here outperform standard approaches for policy diffusion studies and afford predictions that are superior to those of an ensemble of machine learning tools. The proposed framework could have applications for the study of policy diffusion in other domains.
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We study the problem of maximizing payoff generated over a period of time in a general class of closed queueing networks with a finite, fixed number of supply units that circulate in the system. Demand arrives stochastically, and serving a demand unit (customer) causes a supply unit to relocate from the “origin” to the “destination” of the customer. The key challenge is to manage the distribution of supply in the network. We consider general controls including customer entry control, pricing, and assignment. Motivating applications include shared transportation platforms and scrip systems. Inspired by the mirror descent algorithm for optimization and the backpressure policy for network control, we introduce a rich family of mirror backpressure (MBP) control policies. The MBP policies are simple and practical and crucially do not need any statistical knowledge of the demand (customer) arrival rates (these rates are permitted to vary in time). Under mild conditions, we propose MBP policies that are provably near optimal. Specifically, our policies lose at most [Formula: see text] payoff per customer relative to the optimal policy that knows the demand arrival rates, where K is the number of supply units, T is the total number of customers over the time horizon, and η is the demand process’ average rate of change per customer arrival. An adaptation of MBP is found to perform well in numerical experiments based on data from NYC Cab.
This paper was accepted by Gabriel Weintraub, revenue management and market analytics.
Funding: Y. Kanoria was supported by the National Science Foundation’s Division of Civil, Mechanical, and Manufacturing Innovation [Grant CMMI-1653477].
Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4934 .
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Abstract The United States is experiencing growing impacts of climate change but currently receives a limited policy response from its national leadership. Within this policy void, many state governments are stepping up and taking action on adaptation planning. Yet we know little about why some states adopt State Adaptation Plans (SAPs), while others do not. This article investigates factors that predict the emergence of SAPs, both in terms of policy adoption and policy intensity (goal ambitiousness). Applying the diffusion of innovation theory, I consider the relative influence of internal state characteristics, regional pressures, and test for conditional effects between government ideologies and severity of the problem. The results show interesting differences between predictors that influence policy adoption and ambitiousness. States are more motivated to adopt a policy when faced with greater climate vulnerability, have more liberal citizenry, and where governments have crossed policy hurdles by previously passing mitigation plans. The intensity of policies and goal setting, moreover, is more likely to be driven by interest group politics and diffuse through policy learning or sharing information among neighboring states in Environmental Protection Agency regions. These findings support an emerging scholarship that uses more complex dependent variables in policy analysis. These variables have the potential to differentiate symbolic from substantive policies and capture finer information about predictors of importance.
