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Journal articles on the topic 'Policy learning'

To see the other types of publications on this topic, follow the link: Policy learning.

Author: Grafiati

Published: 4 June 2021

Last updated: 26 April 2022

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1

Moon, Jeremy. "POLICY LEARNING." Australian Journal of Public Administration 53, no. 1 (March 1994): 123–25. http://dx.doi.org/10.1111/j.1467-8500.1994.tb01867.x.

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2

Bakır, Caner. "Policy learning and policy change: learning from research citations." Policy Sciences 50, no. 4 (October 28, 2017): 585–97. http://dx.doi.org/10.1007/s11077-017-9299-8.

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3

Jordan, Grant. "Policy Without Learning." Public Policy and Administration 22, no. 1 (January 2007): 48–73. http://dx.doi.org/10.1177/0952076707071504.

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4

Wieslander, Malin. "Learning the (hidden) silence policy within the police." Studies in Continuing Education 41, no. 3 (July 17, 2018): 308–25. http://dx.doi.org/10.1080/0158037x.2018.1497592.

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5

Ma, Janaina, and Diego Mota Vieira. "Aprendizado e mudança em políticas públicas: explorando possibilidades no Modelo de Coalizões de Defesa." Revista de Administração Pública 54, no. 6 (December 2020): 1672–90. http://dx.doi.org/10.1590/0034-761220190381.

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Abstract This article aims to advance the discussion about the influence of knowledge and policy learning on policy change, taking the Advocacy Coalition Framework as reference. We propose unlinking the comprehension of learning through change in two perspectives. First, we suggest apprehending the relation between knowledge and policy learning, through the use of knowledge, assuming that different forms of learning are possible, depending on the context of decision-making. Then, relying on the contributions of the theory of gradual institutional change, we suggest using the notion of institutional dynamics, in order to capture the explanatory power of knowledge and policy learning both in stasis and change situations. We aim to contribute to diminish the skepticism presented in the literature about the influence of knowledge and policy learning in the policy process.

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6

SABATIER, PAUL A. "Knowledge, Policy-Oriented Learning, and Policy Change." Knowledge 8, no. 4 (June 1987): 649–92. http://dx.doi.org/10.1177/0164025987008004005.

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7

HATANAKA, Wataru, Fumihiro SASAKI, and Ryota YAMASHINA. "Active Perception Policy Learning by Reinforcement Learning." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2020 (2020): 2A1—L02. http://dx.doi.org/10.1299/jsmermd.2020.2a1-l02.

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8

Listokin, Yair. "Learning through Policy Variation." Yale Law Journal 118, no. 3 (December 1, 2008): 480. http://dx.doi.org/10.2307/20454719.

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9

May, Peter J. "Policy Learning and Failure." Journal of Public Policy 12, no. 4 (October 1992): 331–54. http://dx.doi.org/10.1017/s0143814x00005602.

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ABSTRACTMultiple knowledges are available for utilisation in policy choice. The rank ordering of knowledges for use in decisionmaking is thus a fundamental predecision. This article shows how this predecision necessarily constrains the processes associated with a politics of ideas, using cases from American international commodity policy. Even when the supposed preconditions of this sort of politics are present, policy change did not occur when the proposed ideas arose from a knowledge accorded secondary status in policymaking circles. Several implications are discussed for the influence and the study of ideational politics. Ultimately, the politics of ideas, so often portrayed through cases of innovation, may be quite conservative, contained by knowledge hierarchies which reflect prior politicaxl circumstances.

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10

Hetling, Andrea, Monika L. McDermott, and Mingus Mapps. "Symbolism Versus Policy Learning." American Politics Research 36, no. 3 (October 26, 2007): 335–57. http://dx.doi.org/10.1177/1532673x07313736.

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11

Ohanian, Lee E., Marco Del Negro, and Tao Zha. "Monetary policy and learning." Review of Economic Dynamics 8, no. 2 (April 2005): 257–61. http://dx.doi.org/10.1016/s1094-2025(05)00018-9.

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12

Podger, Andrew. "Policy Learning and Health." Australian Journal of Public Administration 59, no. 1 (March 2000): 127–28. http://dx.doi.org/10.1111/1467-8500.00146.

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13

Van der Knaap, Peter. "Policy Evaluation and Learning." Evaluation 1, no. 2 (July 1995): 189–216. http://dx.doi.org/10.1177/135638909500100205.

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14

O'Donovan, Kristin. "Policy Failure and Policy Learning: Examining the Conditions of Learning after Disaster." Review of Policy Research 34, no. 4 (March 14, 2017): 537–58. http://dx.doi.org/10.1111/ropr.12239.

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15

Bennett, Colin J., and Michael Howlett. "The lessons of learning: Reconciling theories of policy learning and policy change." Policy Sciences 25, no. 3 (August 1992): 275–94. http://dx.doi.org/10.1007/bf00138786.

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16

Sanderson, Ian. "Evaluation, Policy Learning and Evidence‐Based Policy Making." Public Administration 80, no. 1 (January 2002): 1–22. http://dx.doi.org/10.1111/1467-9299.00292.

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17

Wawrzyński, Paweł. "Control Policy with Autocorrelated Noise in Reinforcement Learning for Robotics." International Journal of Machine Learning and Computing 5, no. 2 (April 2015): 91–95. http://dx.doi.org/10.7763/ijmlc.2015.v5.489.

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18

Adams, David, H. K. Colebatch, and Christopher K. Walker. "Learning about Learning: Discovering the Work of Policy." Australian Journal of Public Administration 74, no. 2 (January 20, 2015): 101–11. http://dx.doi.org/10.1111/1467-8500.12119.

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19

Mitchell, Duncan. "Devolution and learning disability policy." Learning Disability Practice 7, no. 7 (September 1, 2004): 39. http://dx.doi.org/10.7748/ldp.7.7.39.s32.

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20

Bullard, James, and Kaushik Mitra. "Learning about monetary policy rules." Journal of Monetary Economics 49, no. 6 (September 2002): 1105–29. http://dx.doi.org/10.1016/s0304-3932(02)00144-7.

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21

Sharp, Nancy J. "Learning Policy-Making Skills Together." Journal for Nurse Practitioners 2, no. 5 (May 2006): 344. http://dx.doi.org/10.1016/j.nurpra.2006.03.006.

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22

Evans, George W., and Bruce McGough. "Monetary policy, indeterminacy and learning." Journal of Economic Dynamics and Control 29, no. 11 (November 2005): 1809–40. http://dx.doi.org/10.1016/j.jedc.2005.06.008.

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23

Orphanides, Athanasios, and John C. Williams. "Expectations, learning and monetary policy." Journal of Economic Dynamics and Control 29, no. 11 (November 2005): 1807–8. http://dx.doi.org/10.1016/j.jedc.2005.06.009.

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24

Dennis, Richard, and Federico Ravenna. "Learning and optimal monetary policy." Journal of Economic Dynamics and Control 32, no. 6 (June 2008): 1964–94. http://dx.doi.org/10.1016/j.jedc.2007.08.002.

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25

Caprioli, Francesco. "Optimal fiscal policy under learning." Journal of Economic Dynamics and Control 58 (September 2015): 101–24. http://dx.doi.org/10.1016/j.jedc.2015.05.008.

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26

Schorfheide, Frank. "Learning and monetary policy shifts." Review of Economic Dynamics 8, no. 2 (April 2005): 392–419. http://dx.doi.org/10.1016/j.red.2005.01.001.

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27

Kudoh, Noritaka. "POLICY INTERACTION AND LEARNING EQUILIBRIA." Macroeconomic Dynamics 17, no. 4 (November 11, 2011): 920–35. http://dx.doi.org/10.1017/s1365100511000526.

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This note studies fiscal–monetary policy interactions in an endogenous growth model with multiple assets. The “growth-rate Laffer curve” clarifies an important tension between economic growth and government revenue and reveals that higher economic growth does not always finance a larger budget deficit. There are two Pareto-ranked balanced-growth equilibria, which can both be E-stable. Although fiscal policy can eliminate the expectational indeterminacy, it rules out the equilibrium with a higher growth rate and higher welfare. Near the lower bound of the nominal interest rate, an arbitrarily small budget deficit will select the low-growth equilibrium to be the unique E-stable equilibrium.

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28

Waters, George A. "LEARNING, COMMITMENT, AND MONETARY POLICY." Macroeconomic Dynamics 13, no. 4 (September 2009): 421–49. http://dx.doi.org/10.1017/s136510050908016x.

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This paper examines a class of interest rate rules that respond to public expectations and to lagged variables and considers varying levels of commitment that correspond to varying degrees of response to lagged output. Under commitment the policymaker adjusts the nominal rate with lagged output to impact public expectations. Within this class of rules, I provide a condition for nonexplosive and determinate solutions. Expectational stability obtains for any nonnegative response to lagged output. Simulation results show that modified commitment is best under least-squares learning. However, in the presence of parameter uncertainty and/or measurement error in the policymaker's data on public expectations, the best policy is one of partial commitment, where the response to lagged output is less than under modified commitment. The case for partial commitment is strengthened if the gain parameter in the learning mechanism is high, which can be interpreted as the use of few lags by public agents in the formation of expectations or as an indication of low credibility of the policymaker. The appointment of a conservative central banker ameliorates these concerns about modified commitment.

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29

Pittard, Vanessa. "Evidence for e-learning policy." Technology, Pedagogy and Education 13, no. 2 (July 2004): 181–94. http://dx.doi.org/10.1080/14759390400200179.

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30

Jenks, Rosemary. "Immigration Policy: Learning from Neighbours." Environmental Conservation 20, no. 2 (1993): 174. http://dx.doi.org/10.1017/s0376892900037760.

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31

Benz, Arthur, and Dietrich Fürst. "Policy Learning in Regional Networks." European Urban and Regional Studies 9, no. 1 (January 2002): 21–35. http://dx.doi.org/10.1177/096977640200900102.

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32

Pemberton, Hugh. "Learning, Governance and Economic Policy." British Journal of Politics and International Relations 5, no. 4 (November 2003): 500–524. http://dx.doi.org/10.1111/1467-856x.00117.

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33

Lynch, Heather. "Lifelong learning, policy and desire." British Journal of Sociology of Education 29, no. 6 (November 2008): 677–89. http://dx.doi.org/10.1080/01425690802423353.

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34

Hewson, Peter W., James Stewart, and Section Coeditors. "Learning section—editorial policy statement." Science Education 78, no. 3 (June 1994): 213–15. http://dx.doi.org/10.1002/sce.3730780304.

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35

Knoepfel, Peter, and Ingrid Kissling-Näf. "Social Learning in Policy Networks." Policy & Politics 26, no. 3 (July 1, 1998): 343–67. http://dx.doi.org/10.1332/030557398782213638.

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36

Downe, James, Steve Martin, and Tony Bovaird. "Learning from complex policy evaluations." Policy & Politics 40, no. 4 (October 1, 2012): 505–23. http://dx.doi.org/10.1332/030557312x645766.

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37

Luedtke, Alex, and Antoine Chambaz. "Performance guarantees for policy learning." Annales de l'Institut Henri Poincaré, Probabilités et Statistiques 56, no. 3 (August 2020): 2162–88. http://dx.doi.org/10.1214/19-aihp1034.

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38

Beers, Pieter J., Marjolein B. A. van Asselt, Jan D. Vermunt, and Paul A. Kirschner. "Policy makers, information and learning." Journal of Workplace Learning 15, no. 2 (April 2003): 70–79. http://dx.doi.org/10.1108/13665620310464111.

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39

Banerjee, Bikramjit, Sandip Sen, and Jing Peng. "On-policy concurrent reinforcement learning." Journal of Experimental & Theoretical Artificial Intelligence 16, no. 4 (October 2004): 245–60. http://dx.doi.org/10.1080/09528130412331297956.

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40

Bertocchi, Graziella, and Michael Spagat. "Learning, experimentation, and monetary policy." Journal of Monetary Economics 32, no. 1 (August 1993): 169–83. http://dx.doi.org/10.1016/0304-3932(93)90040-m.

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41

Hwang, Kao-Shing, Jin-Ling Lin, Haobin Shi, and Yu-Ying Chen. "Policy Learning with Human Reinforcement." International Journal of Fuzzy Systems 18, no. 4 (May 3, 2016): 618–29. http://dx.doi.org/10.1007/s40815-016-0194-9.

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42

Meseguer, Covadonga. "Learning and economic policy choices." European Journal of Political Economy 22, no. 1 (March 2006): 156–78. http://dx.doi.org/10.1016/j.ejpoleco.2005.06.002.

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43

Sturzenegger, Federico. "Endogenous learning and trade policy." Economics Letters 49, no. 4 (October 1995): 429–33. http://dx.doi.org/10.1016/0165-1765(95)00714-q.

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44

Athey, Susan, and Stefan Wager. "Policy Learning With Observational Data." Econometrica 89, no. 1 (2021): 133–61. http://dx.doi.org/10.3982/ecta15732.

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In many areas, practitioners seek to use observational data to learn a treatment assignment policy that satisfies application‐specific constraints, such as budget, fairness, simplicity, or other functional form constraints. For example, policies may be restricted to take the form of decision trees based on a limited set of easily observable individual characteristics. We propose a new approach to this problem motivated by the theory of semiparametrically efficient estimation. Our method can be used to optimize either binary treatments or infinitesimal nudges to continuous treatments, and can leverage observational data where causal effects are identified using a variety of strategies, including selection on observables and instrumental variables. Given a doubly robust estimator of the causal effect of assigning everyone to treatment, we develop an algorithm for choosing whom to treat, and establish strong guarantees for the asymptotic utilitarian regret of the resulting policy.

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45

Garcia. "LEARNING, UNCERTAINTY AND MONETARY POLICY." Annals of Economics and Statistics, no. 145 (2022): 5. http://dx.doi.org/10.2307/48655900.

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46

Murrall, S., and I. Bailey. "Policy learning in UK renewable energy policy: a methodology." Proceedings of the Institution of Civil Engineers - Energy 161, no. 3 (August 2008): 133–40. http://dx.doi.org/10.1680/ener.2008.161.3.133.

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47

CHAKROUN, BORHENE. "National Qualification Frameworks: from policy borrowing to policy learning." European Journal of Education 45, no. 2 (June 2010): 199–216. http://dx.doi.org/10.1111/j.1465-3435.2010.01425.x.

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48

Lingard, Bob. "Policy borrowing, policy learning: testing times in Australian schooling." Critical Studies in Education 51, no. 2 (May 7, 2010): 129–47. http://dx.doi.org/10.1080/17508481003731026.

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49

Bray, David J., Michael A. P. Taylor, and Derek Scrafton. "Transport policy in Australia—Evolution, learning and policy transfer." Transport Policy 18, no. 3 (May 2011): 522–32. http://dx.doi.org/10.1016/j.tranpol.2010.10.005.

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50

Dunlop, Claire A. "Policy learning and policy failure: definitions, dimensions and intersections." Policy & Politics 45, no. 1 (January 17, 2017): 3–18. http://dx.doi.org/10.1332/030557316x14824871742750.

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