The politics of policy diffusion GILARDI, FABRIZIO; WASSERFALLEN, FABIO
European journal of political research,
November 2019, Letnik:
58, Številka:
4
Journal Article
Recenzirano
This article discusses the recent literature on policy diffusion and puts forward a new articulation of its political dimensions. Policy diffusion means that policies in one unit (country, state, ...city, etc.) are influenced by the policies of other units. The diffusion literature conceptualises these interdependencies with four mechanisms: learning, competition, coercion and emulation. The article identifies a model of diffusion that is dominant in the diffusion literature. According to this model, policies spread because decision makers evaluate the policy implications of the actions of other units. It is argued that the role of politics remains in the background in this model, and the article shows how going beyond a narrow focus on policy adoptions helps us to consider the politics of policy diffusion more explicitly.
•Generalises the definition of innovation in the business sector to all economic sectors.•Uses the System of National Accounts for definitions of economic sectors.•Uses a systems approach to the ...analysis of innovation.•Examines policy implications of measuring innovation in all sectors.
This paper combines general definitions of innovation applicable in all economic sectors with a systems approach, to develop a conceptual framework for the statistical measurement of innovation. The resulting indicators can be used for monitoring and evaluation of innovation policies that have been implemented, as well as for international comparisons. The extension of harmonised innovation measurement to all economic sectors has implications for innovation research and for policy learning.
In a wide variety of applications, including healthcare, bidding in first price auctions, digital recommendations, and online education, it can be beneficial to learn a policy that assigns treatments ...to individuals based on their characteristics. The growing policy-learning literature focuses on settings in which policies are learned from historical data in which the treatment assignment rule is fixed throughout the data-collection period. However, adaptive data collection is becoming more common in practice from two primary sources: (1) data collected from adaptive experiments that are designed to improve inferential efficiency and (2) data collected from production systems that progressively evolve an operational policy to improve performance over time (e.g., contextual bandits). Yet adaptivity complicates the problem of learning an optimal policy ex post for two reasons: first, samples are dependent and, second, an adaptive assignment rule may not assign each treatment to each type of individual sufficiently often. In this paper, we address these challenges. We propose an algorithm based on generalized augmented inverse propensity weighted (AIPW) estimators, which nonuniformly reweight the elements of a standard AIPW estimator to control worst case estimation variance. We establish a finite-sample regret upper bound for our algorithm and complement it with a regret lower bound that quantifies the fundamental difficulty of policy learning with adaptive data. When equipped with the best weighting scheme, our algorithm achieves minimax rate-optimal regret guarantees even with diminishing exploration. Finally, we demonstrate our algorithm’s effectiveness using both synthetic data and public benchmark data sets.
This paper was accepted by Hamid Nazerzadeh, data science.
Funding: This work is supported by the National Science Foundation Grant CCF-2106508. R. Zhan was supported by Golub Capital and the Michael Yao and Sara Keying Dai AI and Digital Technology Fund. Z. Ren was supported by the Office of Naval Research Grant N00014-20-1-2337. S. Athey was supported by the Office of Naval Research Grant N00014-19-1-2468. Z. Zhou is generously supported by the New York University’s 2022–2023 Center for Global Economy and Business faculty research grant and the Digital Twin research grant from Bain & Company.
Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4921 .
In this article an optimal control scheme is proposed to solve robust control problem for matched and unmatched system. In the proposed optimal approach the value functions are designed such that the ...obtained optimal control law guarantees asymptotic stability of the uncertain nonlinear system. Since the proposed robust optimal control problem is not straightforward to solve, an off‐policy reinforcement‐learning algorithm based on neural networks approximation is developed to obtain robust optimal control law iteratively. The robust control law for matched uncertain systems can be achieved via proposed off‐policy learning algorithm without requiring exact knowledge of system's dynamics. The advantages of the proposed robust optimal controller are verified by comparative simulations on an uncertain model of a car suspension system and a mathematical nonlinear model.
AbstractDespite their increasing frequency and magnitude, research on how polycrises influence policymaking has been remarkably scarce. In this article, we approach this issue from an evidence-based ...policy learning perspective. We explore how the polycrisis involving the progressive intersections between the climate change crisis, the COVID-19 pandemic, and the energy crisis influenced evidence-based policy learning underlying the European Union’s climate policymaking. Our findings show that at the initial phases of the polycrisis, interdisciplinary scientific evidence was employed to depoliticize the climate change crisis and facilitate a paradigmatic policy shift. Yet, as relatively faster burning crises overlapped, such evidence played an increasingly substantiating role for previously established institutional choices, and then its role further diminished as more crises overlapped. These findings offer a more robust theoretical understanding of evidence-based policy learning and its contribution to policy change within polycrises. This also draws practitioners’ attention to the need for actively re-aligning evidence-based policy learning practices as political conditions evolve during polycrises.
All politics and policy issues involve the accumulation of data about problems and solutions in context of social interactions. Drawing on these data, policy actors acquire, translate, and ...disseminate new information and knowledge toward achieving political endeavors and for revising or strengthening their policy-related beliefs over time. 'Policy learning' is a concept that refers to this cognitive and social dynamic. Articles in this special issue examine the relationship between policy learning and policy change from different theoretical perspectives. In this introduction to the special issue, we describe the current approaches that structure the field and gaps in knowledge separating policy learning and policy change. We introduce a refined conceptual framework to outline and compare the articles in the issue. These articles point to several facets of the learning phenomenon. First, the articles focus on the nature and consequences of learning by specific groups of society, such as advocacy coalitions, epistemic communities, citizens, street-level bureaucrats, and policy brokers. Second, they present learning processes in which information and experience are used to acquire new knowledge on policy objectives to substantiate and legitimize them or to change or form beliefs. Third, they identify several cognitive and social processes to strengthen the connection between policy learning and policy change. Finally, the articles point to several psychological, social, and institutional factors fostering or impeding these cognitive and social processes. This introduction concludes with avenues for future research.
The traveller's guide to policy learning Stark, Alastair; van der Arend, Jenny
Journal of European public policy,
07/2024, Letnik:
31, Številka:
7
Journal Article
Recenzirano
Odprti dostop
This article claims that when it comes to policy learning success, the most important variables are those that influence how well a learned-lesson travels. Consequently, it offers a 'traveller's ...guide' to policy learning. The guide begins with the presentation of a new concept that we have labelled 'dynamic capacity' which aims to capture the ways in which learned lessons need to move across time and space and consolidate downwards into the institutional fabric of a policy system. Interview data generated from lesson-learning actors in a specific policy community is then presented as a means of outlining the variables that prevent policy lessons from moving, and the strategies that might give them momentum. The hope is that this traveller's guide can help us build better theories of policy learning and encourage more effective learning practices that better recognise the 'others' who may not generate policy lessons, but greatly influence their impact.
This article re-assesses the literature on policy transfer and diffusion in light of what constitutes failure or limited success. First, it looks at imperfect, incomplete or uninformed transfer ...processes. Second, it addresses the concept of 'negative lesson-drawing' as well as the role of interlocutors who complicate policy transfer processes. Third, the idea of 'transfer' as a neat linear transmission of an intact policy approach is criticised by drawing attention to hybridity, synthesis, adaptation and 'localisation'. Finally, policy 'translation' is a better conceptual framework for comprehending the learning and policy innovations that come with the trial and error inherent in policymaking.
The field of policy learning is characterised by concept stretching and a lack of systematic findings. To systematise them, we combine the classic Sartorian approach to classification with the more ...recent insights on explanatory typologies, distinguishing between the genus and the different species within it. By drawing on the technique of explanatory typologies to introduce a basic model of policy learning, we identify four major genera in the literature. We then generate variation within each cell by using rigorous concepts drawn from adult education research. By looking at learning through the lenses of knowledge utilisation, we show that the basic model can be expanded to reveal sixteen different species. These types are all conceptually possible, but are not all empirically established in the literature. Our reconstruction of the field sheds light on mechanisms and relations associated with alternative operationalisations of learning and the role of actors in the process of knowledge construction and utilisation. By providing a comprehensive typology, we mitigate concept-stretching problems and lay the foundations for the systematic comparison across and within cases of policy learning.
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