Policy Networks Research Articles

A Frente pela Vida e a atualização do Movimento da Reforma Sanitária Brasileira

ABSTRACT This paper examined the Front for Life (Frente pela Vida, FpV), a movement of organised civil society in the health field, which sought to achieve political impact in response to the health crisis caused by the COVID-19 pandemic in the context of the Bolsonaro administration. Information was obtained through a document review of FpV publications, participant observation in meetings, demonstrations, livestreams and events in which the front participated or organised, as well as interviews of participants in its operating group. The analytical categories used were origin, trajectory, movements, management, the relationship between the FpV and the health sector reform movement, sustainability, challenges and the FpV’s prospects. The results showed that the FpV has led a large policy network of individual and collective subjects, scientific entities and organisations representing various segments of civil society to update the health sector movement and expand its social support base. Its trajectory has involved mainly technical, scientific and political action in multiple areas, working with the Executive, Judiciary and Legislative branches of government, as well as civil society. In conclusion, the FpV has proved to be an important social movement pressing the State to defend life and the universal right to health and to expand and strengthen Brazil’s Unified Health System, the SUS.

How the Austerity Agenda Disappeared Over a Million California Community College Students: A Faculty Perspective

The largest system of higher education in the nation, the California Community Colleges (CCC) has disappeared more than one million students in the past 15 years, a 35% decrease in its student body since 2008 despite California’s population increase. This dramatic shrinking of the CCC system was not an accident. It was manufactured by a vast neoliberal policy network and funded by edu-philanthropist foundations, with the goal of disrupting, defunding, and dismantling public education. Tracking faculty and student advocacy against so-called student success policies, this article concludes that edu-philanthropist foundations have created decades of policy designed to shrink and disinvest from California’s community colleges. Building on the literature documenting neoliberal educational reform within the public sector of the United States, this article is based on original ethnographic research and leadership experience within local and statewide faculty organizations, including participation in the CCC governance and legislative processes from 2007 to 2022. The article calls for the nation’s community colleges to build a coalition that will educate the public about the 50-plus-year neoliberal project to defund public education; expose the philanthrocapitalist takeover of community colleges; and advocate for reinvestment in the millions of students who need the community colleges the most.

Bridging the simulation-to-real gap of depth images for deep reinforcement learning

While deep reinforcement learning (DRL) models are effective at learning appropriate actions from high-dimensional data, they require large amounts of costly and time-consuming training data to be collected in real-world settings. For this reason, collecting data in simulations offers a promising alternative, but transferring policy networks from simulation to reality can be challenging due to differences in perception between the virtual and real worlds. This paper proposes a two-level method to bridge the simulation-to-reality (sim-to-real) gap for depth images, specifically for autonomous environmental navigation that uses DRL. Simulated depth images are first translated at a perception level through generative adversarial network (GAN) to make them look like real data from a depth sensor. Simulated and GAN-generated depth images are encoded into latent representations, and the encoder is trained in the latent space to make the two images paired. This encoder is trained simultaneously with a reinforcement learning network model to extract domain-invariant and task-relevant features from depth images and map the behavioral similarity of states to the latent space. Our experimental results demonstrate that our approach can effectively bridge the sim-to-real gap, enabling policies learned in simulation to maintain their control performance in the real world.

Autonomous Maneuver Planning for Small-Body Reconnaissance via Reinforcement Learning

This paper presents a reinforcement learning (RL) based approach for autonomous maneuver planning of low-altitude flybys for site-specific reconnaissance of small bodies. Combined with Monte Carlo tree search and deep neural networks, the proposed method generates optimal maneuvers, even under complex dynamics and abstractly science goals. Formulating the mission objective as an observability function, the RL issue can be framed in terms of a Markov decision process. The neural network, trained by a novel policy gradient algorithm with a clipped surrogate objective, learns both policy and value functions that map the action and state spaces to the expected long-term return. An adaptive refinement search technique is applied to further enhance the trained policy network, finding optimal maneuvers from the policy distributions. Experiment results on a simulated reconnaissance mission around asteroid Itokawa illustrate the efficiency and robustness of the proposed approach in achieving multitarget observation.

Promoting Community Enterprises in Thailand: Challenges and Opportunities

Objective: The study examines how policy implementation, policy network management, and capacity building can help promote community enterprises in Thailand. Theoretical Framework: A theoretical approach has been used in this study. Concepts such as Small and Micro Community Enterprise Policies, Policy Networks, Policy Implementation, and Community Enterprise Management Potential have been considered. Method: This qualitative study examined how policy implementation, policy network management, and capacity building can help promote community enterprises in Thailand. Several government agencies and community enterprises were interviewed for the research. While community enterprise promotion policies are widely supported, there are challenges in tailoring them to local contexts and ensuring effective collaboration among stakeholders. Bottom-up and context-sensitive policymaking is needed, as well as stronger interorganizational coordination and data sharing mechanisms. Results and Discussion: The research highlights the importance of tailoring policies to local contexts, fostering collaboration among stakeholders, and addressing capacity building needs in a comprehensive manner to promote the success of community enterprises in Thailand. The findings offer guidance for policymakers and practitioners while also suggesting areas for further research to deepen understanding and inform future interventions. Research Implications: This study contributes to a more nuanced understanding of what it takes to promote community enterprises in Thailand and offers valuable guidance for policymakers and practitioners working to support community-based entrepreneurship. The findings underscore the importance of context-sensitive policies, strong collaboration among stakeholders, and a holistic approach to capacity building. Originality/Value: The study contributes to a nuanced understanding of what shapes community enterprise success in Thailand. This report offers policymakers and practitioners valuable insights into creating an enabling environment for community-based entrepreneurship.

Educational Politics and Policy Change in Neoliberal Times: An Argumentative Discourse Analysis

<p>With the rise of neoliberal reforms and efforts to privatize education, there is a growing need to examine how actors and groups from the public and private sectors influence educational policy change together. In this article, we advance a critical approach to understanding the changing discursive space of educational politics by following discourses through an expansive policy network that goes beyond its traditional boundaries. Specifically, we draw on argumentative discourse analysis (ADA), which allows for the analysis of how and why various actors and groups come together to assign certain meanings to educational phenomena or problems, leading to policy responses or changes. Rooted in Foucault’s notions of discourse and power, ADA offers a unique approach to discourse analysis that can illuminate policy change through discourse coalitions. Three case studies from educational policy scholarship are discussed to illustrate the value and utility of ADA in future critical education policy studies.</p>

Visible-hidden hybrid automatic feature engineering via multi-agent reinforcement learning

Feature engineering is one of the most important and time-consuming steps of machine learning algorithms. In recent years, automatic feature engineering (AFE) methods have received a lot of attention due to their low cost and scalability. However, the existing AFE methods do not take into account the interactions of features in the evolution process and cannot achieve effective feature selection, which weakens the performance of these methods. To tackle the above issues, a novel visible-hidden hybrid automatic feature engineering (VHAFE) method is proposed in this work. Specifically, a visible-hidden hybrid feature transformation graph (VHFTG) is devised to represent both various kinds of feature transformation functions and the feature selection process. Afterward, a multi-pointer state identification method is proposed, which enables the interactions among derived features and the efficient utilization of historically derived features. Furthermore, the multi-agent reinforcement learning algorithm is introduced to optimize the evolution process of VHFTG, where an input variable selection network is devised as the auxiliary policy network to avoid the generation of excessive noise features. Additionally, the VHAFE achieves the state-of-the-art results on a total of 19 public datasets and 1 dataset collected from the actual industrial operation process of gas turbines, which demonstrates the effectiveness of the proposed method.

Develop Model Harmonization ATFM and A-CDM with Integrated Policy Network Supporting Air Traffic Services

Develop Model Harmonization ATFM and A-CDM with Integrated Policy Network Supporting Air Traffic Services

Navigating the tides of change: The local dynamics of China’s marine economic policy

Local governments are playing a key role in policy formulation and implementation because of the shifting tide during China’s marine economic expansion. This study employs all-encompassing methodologies, including policy quantification, policy networks, and topic modeling, to analyze 4241 marine economic policy texts from 11 coastal regions in China. It reveals the spatiotemporal evolution characteristics of local government marine economic policies (LGMEPs). The main results show that: (1) Overall, since 1956, the number and power of LGMEPs have continued to grow and emerge in four stages: initiation, shallow exploration, rapid development, and optimized adjustment. Significant differences can be found in the marine economy policies among regions. Some eastern and southern regions revealed more quantity and power in their policies. (2) The number of institutions involved in marine economy policy formulation has been increasing, and complex and close joint issuance networks have been formed. Only the regions of Shanghai and Hainan formed coordinated joint issuance networks, while other regions lacked either core institutional leadership or collaborative institutional links. (3) Marine economy policy topics have shifted from traditional marine industry to safe operations, modern marine industries, and marine ecological environmental protection. The resource base and development goals of each region led to marked variations in marine economic policy topics. The findings here provide local governments with a reference for crafting marine economic strategies and policies.

Eliminating Primacy Bias in Online Reinforcement Learning by Self-Distillation.

Excessive invalid explorations at the beginning of training lead deep reinforcement learning process to fall into the risk of overfitting, further resulting in spurious decisions, which obstruct agents in the following states and explorations. This phenomenon is termed primacy bias in online reinforcement learning. This work systematically investigates the primacy bias in online reinforcement learning, discussing the reason for primacy bias, while the characteristic of primacy bias is also analyzed. Besides, to learn a policy generalized to the following states and explorations, we develop an online reinforcement learning framework, termed self-distillation reinforcement learning (SDRL), based on knowledge distillation, allowing the agent to transfer the learned knowledge into a randomly initialized policy at regular intervals, and the new policy network is used to replace the original one in the following training. The core idea for this work is distilling knowledge from the trained policy to another policy can filter biases out, generating a more generalized policy in the learning process. Moreover, to avoid the overfitting of the new policy due to excessive distillations, we add an additional loss in the knowledge distillation process, using L2 regularization to improve the generalization, and the self-imitation mechanism is introduced to accelerate the learning on the current experiences. The results of several experiments in DMC and Atari 100k suggest the proposal has the ability to eliminate primacy bias for reinforcement learning methods, and the policy after knowledge distillation can urge agents to get higher scores more quickly.

Carbon Emissions in Cloud Computing: Challenges and Opportunities for Sustainability

In the land of the unreal cloud computing, where bits go on unseen routes, the grim truth manifests itself to the abundant marvels in among the virtuals; carbon emissions. The following concerns the novel problem of greenhouse gas collection within cloud computing - it finds the most hidden source of pollution and building blocks behind this digital paradox. From the humming data centers, crammed with complex meshes of hardware and processes drawing massive amounts of energy, and the kaleidoscope of sources power these huge fiends, an actual stage is created to allow emissions to appear on it in silence. And in the midst of this complexity, even if we do not see very clearly where we are heading towards solutions, there is some light, making us capable for what comes. Source is the right word. The alchemist of innovation, from the energy saving squeezing to the resource sharing singing, all found the soil. Transparency thus turns out to be a steering light amid the obscurity and leads stakeholders towards the trajectories of greener spheres as carbon accounting frameworks show the way. Coordination pivots to the focal point linking stakeholders in the sector as well as the policy and advocacy networks. This weft and waft forms a device that corresponds to the structure of collective action. Hence, beneath the infinitude of the cloud, the two play a dance together where the advanced technology and environmental preservation work together to pave the way of a future that celebrates resilience and sustainability.

Deep reinforcement learning for multi-objective game strategy selection

Multi-objective game (MOG) is a fundamental model for the decision-making problems in which each player must consider multi-dimensional payoffs that reflect different objectives. Typically, solving MOG involves refining the set of equilibrium strategies, which is also known as MOG strategy selection (MOGS). However, existing MOG algorithms only allow one metric for MOGS, which limits the application in real-world scenarios where the players may have different preferences over multiple metrics. In this paper, we first develop a preference-based MOGS framework to encompass multiple metrics with different preferences in MOGS. Based on the framework, we introduce the concept of comprehensive evaluation value (CEV) to evaluate the quality of a strategy set given the preference of each metric. Using CEV as a reward signal, we formulate the problem of finding the optimal strategy set as a Markov decision process, and use deep reinforcement learning to train a policy for MOG strategy selection given the metrics and the corresponding preferences. Specifically, we combine a rational strategy filtering procedure with a Transformer-based encoder–decoder policy network to refine the strategies given the preferences, and then we use a revised REINFORCE algorithm to train the policy network. Besides, we introduce variable beam search decoding to improve the quality of a rollout by keeping track of the most promising strategy sets and choosing the best one. We benchmark our algorithm on the MOG instances generated by GAMUT, and extensive experiments demonstrate that our algorithm can generate the strategy set significantly better than the state-of-the-art baselines with lower computational overhead given different preferences. Furthermore, we compare our approach on real-world problems, showing the great advantages in both performance and runtime.

Flexible job shop scheduling via deep reinforcement learning with meta-path-based heterogeneous graph neural network

The flexible job shop scheduling problem (FJSP) is an important production scheduling problem in intelligent manufacturing. How to model the complex FJSP more accurately and improve the efficiency and generalization of scheduling policies is an urgent challenge to be solved. Therefore, a new end-to-end deep reinforcement learning (DRL) method combined with the meta-path-based heterogeneous graph neural network (MHGNN) is proposed to effectively solve FJSP. The task of solving FJSP is decomposed into two subtasks of operation selection and machine allocation. This dual-task FJSP is represented by introducing a heterogeneous graph and modeled as the dual Markov decision process (DMDP). A MHGNN is proposed to embed the global scheduling states of the dual-task FJSP. A heterogeneous graph neural network (GNN) framework is designed for the dual-task FJSP to efficiently encode the operation nodes and machine nodes, where the extracted embedded feature information is used to represent the operation selection policy and the machine allocation policy. Two policy networks are designed to efficiently predict the operation selection policy and the machine allocation policy, and a soft double-actors critic algorithm is proposed to train these two policy networks, where the trained policies can be used to efficiently solve FJSP instances of different scales. The experiments on three public benchmarks show that the proposed method outperforms the well-known heuristic scheduling rules and some advanced methods for solving FJSP. In particular, when solving large-scale FJSPs, the proposed method performs more prominently in terms of solution quality and solution time.

Temporal-spatial evolution of the integrated landscape patterns in the Zhengzhou Metropolitan Area based on nighttime light data

Metropolitan integration development refers to the interconnection between cities and the coordinated development of various aspects such as economy, society, culture, and nature, which is the goal of metropolitan area development. With the Zhengzhou metropolitan area as the research area and based on nighttime light data from 2012 to 2021, we comprehensively used landscape index and landscape morphology spatial pattern analysis methods, systematically evaluated the integration process of the metropolitan area from the perspective of spatial expansion and spatial connection, analyzed the spatiotemporal variations of the landscape pattern of the metropolitan area, and revealed the spatiotemporal expansion and connection patterns of the metropolitan area. The results showed that the area of the Zhengzhou metropolitan area expanded year by year from 2012 to 2021, with the number of landscape patches continuously increasing, showing an agglomeration phenomenon. In the metropolitan area, there was a trend towards stability and multi-directional coordinated growth. The contribution of non-central cities to expansion increased annually, while the expansion patterns of various constituent cities gradually shifted from internal filling to external expansion. The connection scale within the metropolitan area had been expanding annually, with enhanced intercity connections. Intercity connection belts and channels for material and information exchange were emerging, and the integrated network of urban agglomeration connections was gradually forming. Metropolitan integration planning provided positive guidance for the development of metropolitan areas. We should fully leverage the driving effects of metropolitan areas, pay attention to the integration of Zhengzhou-Kaifeng and Zhengzhou-Xuchang, promote the formation of emerging growth poles in Xinxiang and Jiaozuo, as well as regional coordinated development, strengthen the network of policies, economy, transportation, information, etc., and form a diversified and integrated development situation.

Implementasi Program Keluarga Harapan Di Desa Ciwangi Kecamatan Balubur Limbangan Kabupaten Garut

This research was conducted with the aim of understanding the implementation of the Family Hope Program (PKH) in the Ciwangi Village, Balubur Limbangan District, Garut Regency. Several issues were identified in the implementation process, causing it to not run smoothly. This was due to various factors related to policy substance, the behavior of implementers, network interaction, target group participation, and resources in the implementation in Ciwangi Village, Balubur Limbangan Subdistrict, Garut Regency. The theory used in this research is the policy implementation theory proposed by Muchlis Hamdi (2014), which consists of policy substance, the behavior of implementers, network interaction, target group participation, and resources. This research utilized a qualitative research method with data collection techniques through observation, interviews, and documentation. The informant selection technique employed was purposive sampling, with a total of 8 informants. The findings indicate that the implementation of the Family Hope Program in Ciwangi Village, Balubur Limbangan District, Garut Regency, has not fulfilled several factors crucial for the success of policy implementation. However, there are also factors that have been fully met, such as policy substance and network interaction.

Clinical VMAT machine parameter optimization for localized prostate cancer using deep reinforcement learning.

Volumetric modulated arc therapy (VMAT) machine parameter optimization (MPO) remains computationally expensive and sensitive to input dose objectives creating challenges for manual and automatic planning. Reinforcement learning (RL) involves machine learning through extensive trial-and-error, demonstrating performance exceeding humans, and existing algorithms in several domains. To develop and evaluate an RL approach for VMAT MPO for localized prostate cancer to rapidly and automatically generate deliverable VMAT plans for a clinical linear accelerator (linac) and compare resultant dosimetry to clinical plans. We extended our previous RL approach to enable VMAT MPO of a 3D beam model for a clinical linac through a policy network. It accepts an input state describing the current control point and predicts continuous machine parameters for the next control point, which are used to update the input state, repeating until plan termination. RL training was conducted to minimize a dose-based cost function for prescription of 60Gy in 20 fractions using CT scans and contours from 136 retrospective localized prostate cancer patients, 20 of which had existing plans used to initialize training. Data augmentation was employed to mitigate over-fitting, and parameter exploration was achieved using Gaussian perturbations. Following training, RL VMAT was applied to an independent cohort of 15 patients, and the resultant dosimetry was compared to clinical plans. We also combined the RL approach with our clinical treatment planning system (TPS) to automate final plan refinement, and creating the potential for manual review and edits as required for clinical use. RL training was conducted for 5000 iterations, producing 40 000 plans during exploration. Mean±SD execution time to produce deliverable VMAT plans in the test cohort was 3.3±0.5 s which were automatically refined in the TPS taking an additional 77.4±5.8 s. When normalized to provide equivalent target coverage, the RL+TPS plans provided a similar mean±SD overall maximum dose of 63.2±0.6Gy and a lower mean rectum dose of 17.4±7.4 compared to 63.9±1.5Gy (p=0.061) and 21.0±6.0 (p=0.024) for the clinical plans. An approach for VMAT MPO using RL for a clinical linac model was developed and applied to automatically generate deliverable plans for localized prostate cancer patients, and when combined with the clinical TPS shows potential to rapidly generate high-quality plans. The RL VMAT approach shows promise to discover advanced linac control policies through trial-and-error, and algorithm limitations and future directions are identified and discussed.

Framing Europe on the local level – policy networks in German cities and their activities for political cohesion in Europe

ABSTRACT Political practice and academic discourse similarly assume that the local level has the potential to strengthen political cohesion in the European Union: it is the tier closest to citizens and implements a large share of European legislation. But what political cohesion actually is and how it can be achieved is less clear. The paper addresses this gap by analysing the local framing of political cohesion in Europe. Drawing on case studies in eight German cities, local Europe-related networks are investigated, focusing on how their ideas about their role in crafting political cohesion links to their Europe-related activities.

Q-Learning-Based Priority Dispatching Rule Preference Model for Non-Permutation Flow Shop

Non-permutation flow shop scheduling (NPFS) is an extension of the traditional permutation flow shop scheduling, with a broader solution space. The effectiveness of reinforcement learning in solving flow shop scheduling problems has been demonstrated through its powerful combinatorial optimization capabilities. However, the design and training of the end-to-end policy network is complex, leading to long online training time and limited adaptability of offline training. To overcome these problems, we introduced a NPFS dynamic decision-making process and then proposed a novel NPFS method that combines the Q-learning algorithm with the priority dispatching rule (PDR) set. The NPFS dynamic decision-making process involves decomposing the entire process into multiple sub-job queues for scheduling. The PDR demonstrates better scheduling performance when applied to smaller job queues. By utilizing the Q-learning algorithm, PDRs with superior performance are assigned to sub-scheduling queues based on the generation process of sub-job sequences, resulting in an optimized NPFS strategy. The limited number of PDRs in the PDR set and the small number of sub-job queues in the NPFS process contribute to the efficiency of Q-learning in solving NPFS problem. Finally, we demonstrate the superiority of the proposed NPFS method by a series of numerical experiments using the machine-based assignment PDR method and NSGA-II algorithms as performance benchmarks.

The Hierarchical Configurations of Policy Networks: A Study of Decision-Making Processes in Urban Transport

This study sheds light on a commonly overlooked aspect in network literature: the potential for hierarchized behavior within networks. Providing a longitudinal case study of decision-making behavior in urban transport policy networks, the study also adds to calls for longitudinal studies in the field of public administration. By analyzing documents and interviews, the study reveals the gradual shift towards hierarchical decision-making within networked structures. Furthermore, it highlights how organizational variables shape networked behavior over time, both facilitating and constraining hierarchized behavior. Consequently, this article suggests that hierarchized and collaborative dynamics co-exist and co-evolve within policy networks.

National strategies for knowledge translation in health policy-making: A scoping review of grey literature

Background and objectivesWithout strategic actions in its support, the translation of scientific research evidence into health policy is often absent or delayed. This review systematically maps and assesses national-level strategic documents in the field of knowledge translation (KT) for health policy, and develops a practical template that can support Evidence-informed Policy Network (EVIPNet) Europe countries in producing national strategies for evidence-informed policy-making.MethodsWebsites of organizations with strategic responsibilities in KT were electronically searched, on the basis of pre-defined criteria, in July–August 2017, and an updated search was carried out in April–June 2021. We included national strategies or elements of national strategies that dealt with KT activities, as well as similar strategies of individual institutions with a national policy focus. Two reviewers screened the strategies for inclusion. Data were analysed using qualitative content analysis.ResultsA total of 65 unique documents were identified, of which 17 were eligible and analysed for their structure and content. Of the 17, 1 document was a national health KT action plan and 6 documents were institution-level KT strategies. The remaining 10 strategies, which were also included were 2 national health strategies, 5 national health research strategies and 3 national KT strategies (not specific to the field of health alone). In all, 13 structural elements and 7 major themes of health policy KT strategies were identified from the included documents.ConclusionKT in health policy, as emerged from the national strategies that our mapping identified, is based on the production and accessibility of policy-relevant research, its packaging for policy-making and the activities related to knowledge exchange. KT strategies may play different roles in the complex and context-specific process of policy-making. Our findings show that the main ideas of health-specific evidence-informed policy literature appear in these strategies, but their effectiveness depends on the way stakeholders use them. Specific knowledge-brokering institutions and organizational capacity, advocacy about the use of evidence, and close collaboration and co-decision-making with key stakeholders are essential in furthering the policy uptake of research results.