Relevant bibliographies by topics / Policy learning

Academic literature on the topic 'Policy learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 April 2022

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Journal articles on the topic "Policy learning"

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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Policy learning"

1

Molnár, Krisztina. "Essays on monetary policy and learning." Doctoral thesis, Universitat Pompeu Fabra, 2006. http://hdl.handle.net/10803/7341.

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Mi tesis se basa en los resultados de least squares learning, que modela agentes individuales como econometricos: los agentes funcionan como regresiones, usan datos disponibles para formar sus expectativas. En el primer capítulo de mi tesis demuestro que la presencia de principiantes de learning en una economía se puede racionalizar incluso en coexistencia con los agentes racionales. En el segundo capítulo, examino cuál es la implicación en la política monetaria óptima cuando los agentes privados siguen aprendiendo con least squares learning. Este capítulo demuestra que la política monetaria óptima bajo learning introduce unas nuevas características del comportamiento de la política que no son presentes cuando los agentes privados tienen expectativas racionales.
My thesis builds on the results of the least squares learning literature, which models individual agents as econometricians: agents are running least squares regressions using available data in order to form their expectations. I the ¯first chapter of my thesis I show that the presence of learners in an economy can be rationalized even in coexistence with rational agents. In the second chapter, I examine what is the implication on optimal policy when private agents follow learning. This chapter shows that optimal monetary policy under learning introduces new features of policy behavior that are not present under rational expectations.
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Locarno, Alberto. "Learning, monetary policy and asset prices." Thesis, London School of Economics and Political Science (University of London), 2012. http://etheses.lse.ac.uk/341/.

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The dissertation examines several policy-related implications of relaxing the assumption that economic agents are guided by rational expectations. A first, introductory chapter presents the main technical issues related to adaptive learning. The second chapter studies the implications for monetary policy of positing that both the private sector and the central bank form their expectations through adaptive learning and that the central bank has private information on shocks to the economy but cannot credibly commit. The main finding of this chapter is that when agents learn adaptively a bias against activist policy arises. The following chapter focuses on large, non-linear models, where no unambiguous linear approximation eligible as perceived law of motion exists. Accordingly, there are heterogeneous expectations and the system converges to a misspecification equilibrium, affected by the communication strategies of the central bank. The main results are: (1) the heterogeneity of expectations persists even when a large number of observations are available; (2) the monetary policymaker has no incentive to be an inflation hawk; (3) partial transparency enhances welfare somewhat but full transparency does not. The final chapter adopts a model in which agents are fully informed and use Bayesian techniques to estimate the hidden states of the economy. The monetary policy stance is unobservable and state-independent, generating uncertainty among agents, who try to gauge it from inflation: a change in consumer prices that confirms beliefs reduces stock risk premia, while a change that contradicts beliefs drives the risk premia upward. This may generate a negative correlation between returns and inflation that explains the Fisher puzzle. The model is tested on US data. The econometric evidence suggests: (1) that a mimickingportfolio proxying for monetary policy uncertainty is a risk factor priced by financial markets; and (2) that conditioning on monetary uncertainty and fundamentals eliminates the Fisher puzzle.
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Burke, Patrick Joseph. "Policy learning and policy change : advocacy groups and key moments in Irish homelessness policy." Thesis, Queen's University Belfast, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.554345.

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This dissertation identifies three key moments of policy change in the area of homelessness in Ireland. The key change moments identified point to specific moments when official government policy in relation to homelessness explicitly changed. The first moment was the enactment of the 1988 Housing Act in which for the first time the state defined and recognised that homelessness. Second, the publication in 2000 of Homelessness - An Integrated Strategy - the first national integrated strategy on homelessness. At this moment the state formally recognised the multi- faceted nature of homelessness and the reality that a co-ordinated response was needed. Third, the publication of The Way home: A Strategy to Address Adult Homelessness in Ireland 2008-2013, which sets out 2010 as the target date for the elimination of rough sleeping and long term homelessness. I use Sabatier's Advocacy Coalition Framework as the theoretical framework for my analysis. Having identified the key moments of policy change in the area of homelessness in Ireland the critical questions of my dissertation emerge. First, what drives these moments? I identify a number of societal factors which drive the moments of policy change. First, the increased numbers and visibility of homelessness, second, the growing heterogeneity of the homelessness, third, the growing consensus on the need for a definition of homelessness, fourth, the growing influence of the non-profit sector as advocates and fifth, the existence of a culture of policy- orientated learning. The second critical question asks what characterizes the development of non-profit sector thinking and advocacy on homelessness. I note first that non-profits are united at the level of a core belief that everybody ought to have access to adequate housing, second, non-profits successfully garner support from the public through their campaigning work, third, there is a movement from the individualistic versus structuralist debate on the causes of homelessness, fourth, the sector has a deep knowledge of the many nexus between homelessness and other social issues, fifth, the sector constructs itself as 'the expert' on homelessness and a necessary partner to government. The third critical question explores the nature of the policy orientated learning in the lead up to these critical moments. I note that first, policy orientated learning is incremental, second, policy orientated learning is evidence based, third, policy orientated learning exhibits a deep commitment to a core belief and fourth, that is non conflictual at the level of this core belief. In conclusion the dissertation presents a number of recommendations which arise from the reached.
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Söderberg, Charlotta. "Environmental policy integration in bioenergy : policy learning across sectors and levels?" Doctoral thesis, Umeå universitet, Statsvetenskapliga institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-42810.

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A central principle within UN and EU policy is environmental policyintegration (EPI), aiming at integrating environmental aspirations, targetsand requirements into sector policy in order to promote sustainabledevelopment. The focus of this study is EPI in bioenergy policy. Bioenergy isa renewable energy source of increasing importance in the EU and Swedishenergy mix. At the same time, it is debated how environmentally friendlybioenergy really is. Furthermore, bioenergy can be considered both a multisectorand a multi-level case, since bioenergy is produced in many differentsectors and bioenergy policy is formulated and implemented on differentlevels. Therefore, EPI in bioenergy policy is here analysed over time in twosectors (energy and agriculture) and on three levels (EU, national, subnational).A cognitive, policy learning perspective on EPI is adopted, tracingEPI through looking for reframing of policy towards incorporatingenvironmental objectives in policy rhetoric and practice. Furthermore,institutional and political explanations for the development are discussed.Paper I analyses EPI in Swedish bioenergy policy within energy andagriculture. Paper II analyses institutional conditions for multi-sector EPI inSwedish bioenergy policy. Paper III analyses EPI in EU bioenergy policywithin energy and agriculture. Paper IV analyses sub-national EPI in thecase of the Biofuel Region in north Sweden. The material examined consistsof policy documents complemented by semi-structured interviews. Together, the four papers provide a more complex and holistic picture ofthe EPI process than in previous research, which mainly has focused onstudying EPI in single sectors and on single levels. The study shows thatpriorities are different on different levels; that EPI has varied over time; butthat EPI today is detectable within bioenergy policy in both studied sectorsand on all levels. Policy learning in bioenergy is found to be mainly a topdownprocess. Furthermore, policy coherence between sectors and levels;long-term goals; and concrete policy instruments are found to be importantboth for the EPI process as such and for the outcomes from this process.However, when attempting to marry different goals, such as growth, securityand sustainability, in line with the three-tiered (economic, social, ecologic)sustainable development concept, environmental aspects risks not to beprioritised when goal conflicts arise. The study proposes that future researchboth continues the analysis of multi-sector and multi-level EPI, and furtherexplores to what extent ecological sustainability is improved by EPI.
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Lamoureux, Marcel. "Policy learning theory derived from Russian power sector liberalisation policy experience." Thesis, Glasgow Caledonian University, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.726804.

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Lidström, Christian, and Hannes Leskelä. "Learning for RoboCup Soccer : Policy Gradient Reinforcement Learning inmulti-agent systems." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-157469.

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Robo Cup Soccer is a long-running yearly world wide robotics competition,in which teams of autonomous robot agents play soccer against each other.This report focuses on the 2D simulator variant, where no actual robots are needed and the agents instead communicate with a server which keeps trackof the game state. RoboCup Soccer 2D simulation has become a major topic of research for articial intelligence, cooperative behaviour in multi-agent systems, and the learning thereof. Some form of machine learning is mandatory if you want to compete at the highest level, as the problem is too complex for manualconguration of a teams decision making.This report nds that PGRL is a common method for machine learning in Robo Cup teams, it is utilized in some of the best teams in Robo Cup. The report also nds that PGRL is an effective form of machine learning interms of learning speed, but there are many factors which affects this. Most often a compromise have to made between speed of learning and precision.
Robo Cup Soccer är en årlig världsomspännande robotiktävling, i vilken lag av autonoma robotagenter spelar fotboll mot varandra. Denna rapport fokuserar på 2D-simulatorn, vilken är en variant där inga riktiga robotar behövs, utan där spelarklienterna istället kommunicerar med en server vilken håller reda på speltillståndet. RoboCup Soccer 2D simulation har blivit ett stort ämne för forskning inom articiell intelligens, samarbete och beteende i multi-agent-system, och lärandet därav. Någon form av maskininlärning är ett krav om man villkunna tävla på den högsta nivån, då problemet är för komplext för att beslutsfattandet ska kunna programmeras manuellt.Denna rapport finner att PGRL är en vanlig metod för maskininlärning i Robo Cup-lag, den används inom några av de bästa lagen i Robo Cup. Rapporten nner också att PGRL är en effektiv form av maskininlärningn är det gäller inlärningshastighet, men att det finns många faktorer som kan påverka detta. Oftast måste en avvägning ske mellan inlärningshastighet och precision.
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Tezel, Nezahat. "Policy Implications In The." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/12604944/index.pdf.

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This study explores the relatively new concept of &ldquo
The Learning Economy&rdquo
in the context of system of Innovation, which provides a basic understanding of all elements and their relations necessary to enhance the innovative capacity. This thesis aims to examine the structure and characteristics of ASELSAN (Electronic Industries Inc.) including i.e., firm-level technological activities. In the &lsquo
Learning Economy&rsquo
, rapid learning is the key factor for accelerating innovative capabilities and competitiveness for firms and nations. On the other hand, this concept is closely correlated with the &lsquo
New Economy&rsquo
, ICT (Information communication Technologies) that enhances the knowledge dissemination and learning. In this perspective, ASELSAN acquired high-level technological capabilities and rapid development such that it can be considered as a model for other firms in Turkey. Furthermore, this research aims to point out the &lsquo
Learning Process Model of ASELSAN&rsquo
comparing it with the catching-up firms in South Korea and emphasize transformation of technology and institutional structure in the period from 1980 to 2002. As an individual firm, &lsquo
ASELSAN&rsquo
is a leading firm in the defense industry as a system integrator
and the next step may be &lsquo
network-based&rsquo
learning process model. In summary, there could be policy lessons to be taken for other firms to become a &lsquo
learning organization and &lsquo
innovative firm&rsquo
.
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Caprioli, Francesco. "Optimal fiscal policy, limited commitment and learning." Doctoral thesis, Universitat Pompeu Fabra, 2009. http://hdl.handle.net/10803/7396.

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Esta tesis trata sobre cómo la autoridad fiscal debe fijar los impuestos distorsivos de manera óptima. El capítulo 1 analiza el problema de la política fiscal cuando el gobierno tiene un incentivo a hacer default con su deuda externa. El capítulo 2 trata sobre el problema de la política fiscal cuando los agentes no conocen cómo el gobierno fija las tasas impositivas. La principal conclusión que obtengo es que, en ambos contextos, el resultado de suavidad de las tasas, que es estándar en la literatura de imposición óptima, se rompe. Cuando los gobiernos no tienen una tecnología de compromiso, los impuestos responden a los incentivos de default; cuando los agentes poseen información parcial sobre el modelo subyacente de la economía, los impuestos dependen de sus expectativas sobre los mismos.
This thesis is about how fiscal authority should optimally set dissorting taxes. Chapter 1 deals with the optimal fiscal policy problem when the government has an incentive to default on external debt. Chapter 2 deals with the optimal fiscal policy problem when households do not know how government sets taxes. The main conclusion I get is that, in each of these two contexts, the tax smoothing result, which is the standars result in the optimal taxation literature, is broken. When governments do not have a commitment technology taxes respond to the incentives to default; when agents have partial information about the underlying economic model, taxes depend on their beliefs about it.
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Pau, Jason. "Global antidumping use retaliation or policy learning? /." CONNECT TO ELECTRONIC THESIS, 2007. http://dspace.wrlc.org/handle/1961/4179.

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McGough, Bruce. "Learning, oil price shocks, and monetary policy /." view abstract or download file of text, 2000. http://wwwlib.umi.com/cr/uoregon/fullcit?p9987239.

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Thesis (Ph. D.)--University of Oregon, 2000.
Typescript. Includes vita and abstract. Includes bibliographical references (leaves 143-145). Also available for download via the World Wide Web; free to University of Oregon users.
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Books on the topic "Policy learning"

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Ainley, Patrick. Learning Policy. London: Palgrave Macmillan UK, 1999. http://dx.doi.org/10.1057/9780230500891.

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Dunlop, Claire A., Claudio M. Radaelli, and Philipp Trein, eds. Learning in Public Policy. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76210-4.

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Sanderson, Ian. Evaluation, policy learning and evidence-based policy making. Oxford: Blackwell, 2002.

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Goff, Philip. Learning for life.: Policy decisions. [Wellington, New Zealand]: Government Printing Office, 1989.

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Namibia. National policy on adult learning. Windhoek]: Republic of Namibia, Ministry of Basic Education, Sport, and Culture, 2003.

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Williams, Val. Learning Disability Policy and Practice. London: Macmillan Education UK, 2013. http://dx.doi.org/10.1007/978-1-137-29669-6.

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Learning, policy making, and market reforms. New York: Cambridge University Press, 2009.

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Richard, Edwards. Supporting lifelong learning: Making policy work. London: RoutledgeFalmer, 2004.

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Forde, Christine, and Margery McMahon. Teacher Quality, Professional Learning and Policy. London: Palgrave Macmillan UK, 2019. http://dx.doi.org/10.1057/978-1-137-53654-9.

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Fox, Dennis. A policy for teaching and learning. Nottingham: Trent Polytechnic, 1987.

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Book chapters on the topic "Policy learning"

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Dunlop, Claire A., and Claudio M. Radaelli. "Policy Learning." In The SAGE Handbook of Political Science, 1121–33. 1 Oliver's Yard, 55 City Road London EC1Y 1SP: SAGE Publications Ltd, 2020. http://dx.doi.org/10.4135/9781529714333.n70.

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Ainley, Patrick. "Introduction: the Emergence of Learning Policy." In Learning Policy, 1–26. London: Palgrave Macmillan UK, 1999. http://dx.doi.org/10.1057/9780230500891_1.

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Ainley, Patrick. "Tripartite Schooling, 1944–63." In Learning Policy, 27–58. London: Palgrave Macmillan UK, 1999. http://dx.doi.org/10.1057/9780230500891_2.

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Ainley, Patrick. "Comprehensive Schooling, 1963–76." In Learning Policy, 59–87. London: Palgrave Macmillan UK, 1999. http://dx.doi.org/10.1057/9780230500891_3.

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Ainley, Patrick. "Training without Jobs, 1976–87." In Learning Policy, 88–117. London: Palgrave Macmillan UK, 1999. http://dx.doi.org/10.1057/9780230500891_4.

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Ainley, Patrick. "Education without Jobs, 1987–97." In Learning Policy, 118–56. London: Palgrave Macmillan UK, 1999. http://dx.doi.org/10.1057/9780230500891_5.

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Ainley, Patrick. "New Learning under New Labour?" In Learning Policy, 157–96. London: Palgrave Macmillan UK, 1999. http://dx.doi.org/10.1057/9780230500891_6.

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Ainley, Patrick. "Conclusion: Towards a New Alternative." In Learning Policy, 197–213. London: Palgrave Macmillan UK, 1999. http://dx.doi.org/10.1057/9780230500891_7.

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Shell, Duane F., David W. Brooks, Guy Trainin, Kathleen M. Wilson, Douglas F. Kauffman, and Lynne M. Herr. "Policy." In The Unified Learning Model, 187–95. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-3215-7_15.

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Huang, Ruitong, Tianyang Yu, Zihan Ding, and Shanghang Zhang. "Policy Gradient." In Deep Reinforcement Learning, 161–212. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4095-0_5.

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Conference papers on the topic "Policy learning"

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Tsai, Yi-Shan, Dragan Gasevic, Pedro J. Muñoz-Merino, and Shane Dawson. "LA policy." In LAK '17: 7th International Learning Analytics and Knowledge Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3027385.3029424.

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Lim, Yow Tzu, Pau-Chen Cheng, Pankaj Rohatgi, and John A. Clark. "Dynamic security policy learning." In the first ACM workshop. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1655168.1655177.

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van de Wolfshaar, Jos, Marco Wiering, and Lambert Schomaker. "Deep Learning Policy Quantization." In 10th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006592901220130.

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Ulumudin, Ikhya, Siska Lismayanti, and Sisca Fujianita. "Utilizing the Assessment of Learning Outcome to Improve Learning Quality." In International Conference on Educational Assessment and Policy. Badan Pengembangan dan Pembinaan Bahasa, 2018. http://dx.doi.org/10.26499/iceap.v1i1.68.

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Tsai, Yi-Shan, Pedro Manuel Moreno-Marcos, Kairit Tammets, Kaire Kollom, and Dragan Gašević. "SHEILA policy framework." In LAK '18: International Conference on Learning Analytics and Knowledge. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3170358.3170367.

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Padmakumar, Aishwarya, Peter Stone, and Raymond Mooney. "Learning a Policy for Opportunistic Active Learning." In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/d18-1165.

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Sundararaman, Dhanasekar, Henry Tsai, Kuang-Huei Lee, Iulia Turc, and Lawrence Carin. "Learning Task Sampling Policy for Multitask Learning." In Findings of the Association for Computational Linguistics: EMNLP 2021. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.findings-emnlp.375.

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Migliavacca, Martino, Alessio Pecorino, Matteo Pirotta, Marcello Restelli, and Andrea Bonarini. "Fitted policy search." In 2011 Ieee Symposium On Adaptive Dynamic Programming And Reinforcement Learning. IEEE, 2011. http://dx.doi.org/10.1109/adprl.2011.5967368.

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Bandara, Arosha K., Alessandra Russo, and Emil C. Lupu. "Towards Learning Privacy Policies." In Eighth IEEE International Workshop on Policies for Distributed Systems and Networks (POLICY'07). IEEE, 2007. http://dx.doi.org/10.1109/policy.2007.45.

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Bowe, Megan, Weiqin Chen, Dai Griffiths, Tore Hoel, Jaeho Lee, Hiroaki Ogata, Griff Richards, Li Yuan, and Jingjing Zhang. "Learning analytics and policy (LAP)." In LAK '17: 7th International Learning Analytics and Knowledge Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3027385.3029436.

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Reports on the topic "Policy learning"

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Bullard, James, and Kaushik Mitra. Learning About Monetary Policy Rules. Federal Reserve Bank of St. Louis, 2000. http://dx.doi.org/10.20955/wp.2000.001.

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Arifovic, Jasmina, James Bullard, and Olena Kostyshyna. Social Learning and Monetary Policy Rules. Federal Reserve Bank of St. Louis, 2007. http://dx.doi.org/10.20955/wp.2007.007.

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Wang, Shaoda, and David Yang. Policy Experimentation in China: the Political Economy of Policy Learning. Cambridge, MA: National Bureau of Economic Research, October 2021. http://dx.doi.org/10.3386/w29402.

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Hu, Vincent C. Machine Learning for Access Control Policy Verification. National Institute of Standards and Technology, September 2021. http://dx.doi.org/10.6028/nist.ir.8360.

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Hantrais, Linda. Policy learning from COVID-19 in Europe. Emerald, February 2021. http://dx.doi.org/10.35241/emeraldopenres.1114904.1.

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6

Meeker, Jessica. Mutual Learning for Policy Impact: Insights from CORE. Sharing Experience and Learning on Approaches to Influence Policy and Practice. Institute of Development Studies (IDS), August 2021. http://dx.doi.org/10.19088/core.2021.005.

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Abstract:
On 23 June 2021, Southern Voice and the Institute of Development Studies co-hosted an online dialogue which aimed to enhance efforts to inform and influence policy by sharing learning between CORE projects, at different stages in their policy engagement activities, on their approaches and experiences at sub-national, national, and regional levels. The event was attended by over 70 participants from across the CORE cohort and highlighted the experiences of CORE partners, Partnership for Economic Policy (PEP), International Centre for Research on Women (ICRW), and Group for the Analysis of Development (GRADE). This learning guide captures the practical insights and advice from the event to help inform the practice of both participants and other projects across the portfolio. The guide is structured around the key challenges identified in influencing policy, particularly within the changing parameters of the current pandemic, highlighting key messages and examples from the three partners.
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7

Stanley, Kenneth O. Scalable Heterogeneous Multiagent Teams Through Learning Policy Geometry. Fort Belvoir, VA: Defense Technical Information Center, October 2011. http://dx.doi.org/10.21236/ada551086.

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8

Chernozhukov, Victor, Greg Lewis, Vasilis Syrgkanis, and Mert Demirer. Semi-Parametric Efficient Policy Learning with Continuous Actions. The IFS, June 2019. http://dx.doi.org/10.1920/wp.cem.2019.3419.

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9

Mark, Nelson. Changing Monetary Policy Rules, Learning, and Real Exchange Rate Dynamics. Cambridge, MA: National Bureau of Economic Research, January 2005. http://dx.doi.org/10.3386/w11061.

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10

Gilchrist, Simon, and Masashi Saito. Expectations, Asset Prices, and Monetary Policy: The Role of Learning. Cambridge, MA: National Bureau of Economic Research, August 2006. http://dx.doi.org/10.3386/w12442.

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