In the literature on the role of agency in the policy process, relatively little attention has been devoted to how agents define policy problems. This article helps to address this gap by asking when and how policy entrepreneurs are successful in defining problems. The article rests on a framework that shows how policy entrepreneurs holding specific ideas and given a propitious socioeconomic context are able to define problems, translate those problems into new frames, and draw on those frames, while using their personal skills and political and institutional resources, to help build supportive coalitions in favor of policy change. Illustrated by a puzzling case in the field of European mobility policy, the article offers a new perspective on the role of ideas at the problem definition stage of the policy process, while providing a richer understanding of the policy entrepreneur as a driver of policy change.

Poor definition and uncertainty are primary characteristics of conceptual design processes. During the initial stages of these generally human-centric activities, little knowledge pertaining to the problem at hand may be available. The degree of problem definition will depend on information available in terms of appropriate variables, constraints, and both quantitative and qualitative objectives. Typically, the problem space develops with information gained in a dynamical process in which design optimization plays a secondary role, following the establishment of a sufficiently well-defined problem domain. This paper concentrates on background human–computer interaction relating to the machine-based generation of high-quality design information that, when presented in an appropriate manner to the designer, supports a better understanding of a problem domain. Knowledge gained from such information combined with the experiential knowledge of the designer can result in a reformulation of the problem, providing increased definition and greater confidence in the machine-based representation. Conceptual design domains related to gas turbine blade cooling systems and a preliminary air frame configuration are introduced. These are utilized to illustrate the integration of interactive evolutionary strategies that support the extraction of optimal design information, its presentation to the designer, and subsequent human-based modification of the design domain based on knowledge gained from the information received. An experimental iterative designer or evolutionary search process resulting in a better understanding of the problem and improved machine-based representation of the design domain is thus established.