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In 1951, Harold Lasswell defined the ability to clarify value goals as integral to a policy analyst’s job. But graduate education in public policy analysis has paid insufficient attention to the skills needed to investigate and clarify value disputes. In turn, practicing policy analysts don’t have ready access to a set of methods for normative analysis that serves Lasswell’s vision of a contextualized, holistic, and interdisciplinary policy science. I start by describing calls for more emphasis on social equity in policy analysis and explore the complementary relationship of empirical, fact-based analysis and normative, value-driven analysis. I then propose seven competencies that policy analysts should be expected to master. They need to understand how normative issues arise in and adjacent to the classical model of policy analysis. They need to master a vocabulary for normative analysis and understand how humans make moral judgments, recognizing the distinction between moral rationalism and moral intuitionism. To engage in moral rationalism, practitioners need to be able to use the tools of analytic political philosophy. When it comes to moral intuitionism, they need to recognize the emotion-driven foundations of moral judgement and personal values. Finally, policy analysts also need to know where to find the values that are relevant to their analysis. Mastery of these competencies will allow analysts to better serve what Laswell describes as the intelligence needs of policymakers.
In 1951, Harold Lasswell defined the ability to clarify value goals as integral to a policy analyst’s job. But graduate education in public policy analysis has paid insufficient attention to the skills needed to investigate and clarify value disputes. In turn, practicing policy analysts don’t have ready access to a set of methods for normative analysis that serves Lasswell’s vision of a contextualized, holistic, and interdisciplinary policy science. I start by describing calls for more emphasis on social equity in policy analysis and explore the complementary relationship of empirical, fact-based analysis and normative, value-driven analysis. I then propose seven competencies that policy analysts should be expected to master. They need to understand how normative issues arise in and adjacent to the classical model of policy analysis. They need to master a vocabulary for normative analysis and understand how humans make moral judgments, recognizing the distinction between moral rationalism and moral intuitionism. To engage in moral rationalism, practitioners need to be able to use the tools of analytic political philosophy. When it comes to moral intuitionism, they need to recognize the emotion-driven foundations of moral judgement and personal values. Finally, policy analysts also need to know where to find the values that are relevant to their analysis. Mastery of these competencies will allow analysts to better serve what Laswell describes as the intelligence needs of policymakers.
This article examines the interplay between uncertainty, emotions, and scientific discourse in shaping COVID-19 policies in Quebec, Canada. Through the application of natural language processing (NLP) techniques, indices were developped to measure sentiments of uncertainty among policymakers, their negative sentiments, and the prevalence of scientific statements. The study reveals that while sentiments of uncertainty led to the adoption of stringent policies, scientific statements and the evidence they conveyed were associated with a relaxation of such policies, as they offered reassurance and mitigated negative sentiments. Furthermore, the findings suggest that scientific statements encouraged stricter policies only in contexts of high uncertainty. This research contributes to the theoretical understanding of the interplay between emotional and cognitive dynamics in health crisis policymaking. It emphasizes the need for a nuanced understanding of how science may be used in the face of uncertainty, especially when democratic processes are set aside. Methodologically, it demonstrates the potential of NLP in policy analysis.
Does anxiety affect how public officials process policy information? It is often argued that the increasing number of policy failures can be explained by a lack of policy learning by decision makers. While previous studies show that socioeconomic and partisan variables are related to the perception of policy information, little attention has been paid to the role of emotions, such as anxiety, in the policymaking process. In this paper, we investigate the impact of anxiety on the policy learning of local office holders at the individual level in Switzerland. We introduce the Marcus' Affective Intelligence Model—which examines how emotions affect individuals' information processing—to the policy learning literature. To test the expectations of the model, we draw on novel experimental data collected among local elected officials from the 26 Swiss cantons. In the experiment, we randomly display anxiety‐inducing images along with policy information. We provide evidence that anxiety has a positive causal effect on learning. Considering potential moderators of this effect, we show that the relationship is not conditioned by the strength of priors or the perceived complexity of public policies. However, these variables are substantially correlated with policy learning. Our findings have important implications for better understanding how information influences policymaking.
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