Policy Learning and Information Processing.
Policy learning is an important concept in the study of policymaking, yet it is difficult to model and empirically estimate. Additionally, work on policy learning has not fully drawn from the work on information processing in the policy process. In this paper, I propose a model of policy learning th...
| Publicado en: | Policy Studies Journal Vol. 49; no. 4; pp. 1019 - 1040 |
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| Formato: | Artículo |
| Publicado: |
Wiley-Blackwell
Nov2021
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=153385754&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 153385754 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0190292X PSJ jtl: Policy Studies Journal issn: 0190292X maglogo: Y pubinfo: dt: Nov2021 vid: 49 iid: 4 pid: 480 pub: Wiley-Blackwell artinfo: ui: 153385754 10.1111/psj.12397 ppf: 1019 ppct: 21 formats: fmt: @attributes: type: P size: 215KB tig: atl: Policy Learning and Information Processing. aug: su: Yucca Mountain (Nev.) Policy sciences Public opinion Advocacy coalition framework Radioactive waste repositories sug: subj: Policy sciences Public opinion Yucca Mountain (Nev.) Advocacy coalition framework Radioactive waste repositories keyword: advocacy coalition framework Bayesian updating beliefs information processing policy learning Actualización bayesiana aprendizaje de políticas creencias marco de coalición de defensa procesamiento de información 信念 信息处理 倡导联盟框架 政策学习 贝叶斯更新 advocacy coalition framework Bayesian updating beliefs information processing policy learning Actualización bayesiana aprendizaje de políticas creencias marco de coalición de defensa procesamiento de información 信念 信息处理 倡导联盟框架 政策学习 贝叶斯更新 ab: Policy learning is an important concept in the study of policymaking, yet it is difficult to model and empirically estimate. Additionally, work on policy learning has not fully drawn from the work on information processing in the policy process. In this paper, I propose a model of policy learning that incorporates the Advocacy Coalition Framework's notion of policy‐oriented learning and the theory of disproportionate information processing within a Bayesian learning framework. Policy learning through Bayesian updating occurs as individuals adjust their prior beliefs in light of new information, and in the approach posited here, learning is a function of the strength of prior beliefs and the weight given to new information. Additionally, learning is thought to occur only when subsequent beliefs move in the direction of the information. Then, I demonstrate the policy learning model using public opinion data about Yucca Mountain, a proposed repository site for nuclear waste. Finally, I conclude with suggesting ways in which the policy learning model can be incorporated into current policy learning theories and frameworks. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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