Policy transfer as learning: capturing variation in what decision-makers learn from epistemic communities

Abstract

Almost two decades ago, Peter M. Haas formulated the epistemic community framework as a method for investigating the influence of knowledge-based experts in international policy transfer. Specifically, the approach was designed to address decision-making instances characterized by technical complexity and uncertainty. Control over the production of knowledge and information enables epistemic communities to articulate cause and effect relationships and so frame issues for collective debate and export their policy projects globally. Remarkably, however, we still know very little about the variety of ways in which decision-makers actually learn from epistemic communities. This article argues that variety is best captured by differentiating the control enjoyed by decision-makers and epistemic communities over the production of substantive knowledge (or means) that informs policy from the policy objectives (or ends) to which that knowledge is directed. The implications of this distinction for the types of epistemic community decision-maker learning exchanges that prevail are elaborated using a typology of adult learning from the education literature which delineates four possible learning situations. This typology is then applied to a comparative study of US and EU decision-makers’ interaction with the epistemic community that formed around the regulation of the biotech milk yield enhancer bovine somatotrophin (rbST) to illustrate how the learning types identified in the model play out in practice.