Foresight and policy-making – Addressing the need for theoretical frameworks

As I’ve mentioned in previous posts, technology foresight started out as a method for facilitating long-term planning and policy primarily in relation to research & development (R&D) and innovation policy. In the last decade there has been and increasing focus on the usefulness of the methods used in R&D planning for other policy areas, especially social policy, i.e. education, labor, welfare, etc. The qualifier “technology” is often dropped from the term, referring to the exercises only as “foresight” because technology is not always a defining characteristic of the exercise although it could be argued that technology is an unavoidable factor in any long-term planning. This can cause some conceptual confusion since the generic term “foresight” has also often been used as a catch-all term for any sort of future-oriented policy or decision-making activity. Yet, I am going to venture into the conceptual quagmire and use the term “foresight” rather than “technology foresight” because I want to discuss the use of foresight activities in areas of policy-making that may not be directly related to technology, especially educational policy, and I think the use of the qualifier “technology” may cause confusion. Thus, what I mean by “foresight” is a deliberate and well-defined exercise intended to apply futures methodologies to social policy issues in order to inform long-term policy-making. The specific purpose of foresight activities is to identify and address elements of significant uncertainty that affect social policy and institutions. Foresight activities differ from specific futures methods, such as scenario construction, delphi studies and projections, in that they use a mix of the aforementioned methods, involve a broad range of stakeholder groups, including the general public, subject experts, policymakers, etc., and are specifically intended to facilitate long-term policy making in a range of areas by identifying possible future outcomes based on significant uncertainty factors.

As foresight exercises have transitioned from the somewhat confined realm of R&D policy, which, in the past, was often limited to specific industries or even specific organizations, to the broad realm of social policy, a gap has emerged in the foresight literature concerning the interface between foresight and policy-making. Most of the literature that I have discussed or mentioned in previous posts builds on Irvine & Martin’s seminal work focusing primarily on the structure and implementation of foresight exercises themselves with little attention to how the outcomes from such activities are translated into policy. Some recent research has sought to address this gap by examining closer how foresight functions as a policy-making instrument. Although this recent research has produced valuable results, what is still lacking is framing foresight as a policy-making instrument in terms of existing theoretical frameworks on policy-making processes. In this article I want to briefly address that gap and consider foresight from the perspective of a couple of existing theoretical frameworks, primarily Kingdon’s “multiple streams” model and Mazzoni’s “arenas” model.

Defining foresight in policy terms
Before I dive into the theoretical aspects we need to clarify how foresight functions as a policy-making instrument. Around 2005, a project was launched by the Institute for Prospective Technological Studies at the European Commission’s Joint Research Centre (JRC-IPTS) to develop knowledge and capacity for foresight in Europe. Among other things, the project examined the connection between foresight exercises and policy-making. The results of this aspect of the project are detailed in an article published in 2008 (Da Costa, Warnke, Cagnin & Scapolo, 2008). The article provides a rare systematic examination of foresight activities as they relate directly to the transfer of knowledge developed in foresight exercises to policy-making processes.
The authors’ purpose is to define different functions of foresight activities as they relate to policy-making and to determine relationships and tensions between the functions that can affect policy-making processes. The authors describe six unique functions of foresight activities. The first two are core functions that describe two fundamentally different purposes of foresight activities:

1. Informing policy:

• supplements traditional policy-making models
• informs policy-making
• may provide policy recommendations
• related to agenda-setting

2. Facilitating policy implementation:

• aims to change the policy-making system
• challenges traditional linear model of policy-making
• defines policy-making as “continuous reflexive learning process”
• involves a broad range of stakeholders in policy-making processes

The other four functions relate in different ways to the core functions:

3. Embedding participation in policy-making
4. Supporting policy definition
5. Reconfiguring the policy system
6. Symbolic function

Da Costa, et al’s, point is that the two core functions should be kept separate, i.e. that organized foresight activities should not attempt to fulfill both of these functions simultaneously because they are very different in their nature and purpose. Thus it is easy to see how any attempt to fulfill both simultaneously minimizes the effectiveness of each respectively. I will focus specifically on the latter of the two, i.e. ‘facilitating policy implementation’ for two reasons. First, it is the more ambitious and interesting of the two because it seeks to alter the policy-making system to facilitate long-term planning rather than merely introducing a future-oriented component into existing systems. Second, I think that the ‘informing policy’ function as defined by Da Costa et al, is generally not likely to be as effective, in terms of influencing policy-making, as the second function, primarily because current policy-making systems seldom have the capacity to address the needs for long-term planning. In other words, I think that the problem related to future-oriented policy-making is not a lack of information, it is the nature of the policy-making system itself which is ill-suited to make use of information for long-term policy consideration.

I am going to consider two well-known theoretical frameworks which, to me, seem at first glance to be relevant to foresight’s relation to policy-making. The first is Kingdon’s “multiple streams” model and the second is Mazzoni’s “arenas” model.

Kingdon’s multiple streams model
Kingdon’s multiple streams model is a very likely candidate because it defines the policy-making process as a non-linear process, much like Da Costa, et al, claim for foresight exercises as they relate to policy-making. The multiple streams model is primarily concerned with agenda setting, i.e. how issues are brought to the attention of policymakers. Kingdon describes policy-making as being influenced by three distinct streams:

• The problem stream – specific issues vie for policymakers’ attention
• The solution stream – policy ideas for addressing specific problems are considered
• The political stream – key decision makers debate policy initiatives

In Kingdon’s model ideas and debates are floated in their relevant stream where they compete for attention quite isolated from what happens in other streams. What eventually prompts policy action is when the three streams converge in relation to a particular issue in what Kingdon refers to as a “window of opportunity”. The “window of opportunity” is typically brought about by events that define a condition as problematic and coincide with an event in the political stream that prompts policymakers to action.

A considerable strength of Kingdon’s multiple streams model is that it seems to be generic enough that it can be applied to a number of different types of policy-making contexts. For example, the model is equally applicable whether we are considering a top-down decision-making system or a bottom-up decision-making system. However, it is hard to see how exactly loosely defined policy-related activities, such as foresight exercises often are, fit into the model because they tend to reach across streams and are by definition intended to address issues that are not likely to be perceived as critical in the current political atmosphere. Thus, research on foresight in relation to Kingdon’s multiple streams model could involve an examination of how future-oriented issues, i.e. foresight activities’ output, come to be recognized as critical issues in the policy streams. This is clearly related to Da Costa, et al’s, first function of foresight activities, informing policy. Formulating a research problem that would relate to the second function, facilitating policy implementation, is more problematic because Kingdon’s model doesn’t really allow for much reconfiguration. Consequently, Kingdon’s multiple streams model is unlikely to reveal significant insights into how foresight activities fulfill Da Costa, et al’s, second function.

Mazzoni’s arenas model
Mazzoni developed his “arenas” model to analyze policy innovation related to education in the United States as opposed to incremental policy change. Mazzoni defines arena as, “… referring to the political interactions characterizing particular decision sites through which power is exercised to initiate, formulate, and enact public policy.” (Mazzoni, 1991, p. 116). Like Kingdon’s streams, Mazzoni’s arenas are spaces where policy ideas compete for attention. However, whereas Kingdon’s streams are defined by process, Mazzoni’s arenas are dynamic and defined by the key players in the relevant stage of policy discourse. Thus, arenas are more than just sites for debate and decision-making. They can also be created to empower specific groups by extending to them political legitimacy on specific issues. Shifting an issue from one arena to another can significantly affect policy outcomes because it changes the key players in the policy discourse. In his empirical research on educational policy in Minnesota, Mazzoni found that such arena shifts affected policy outcomes, especially in that it made it possible for major policy changes, as opposed to incremental changes, to be accomplished.

Mazzoni first defined two arenas but later revised the theory to include four arenas:

• Subsystem arena – Formal political institutions (ex. legislature and especially education committees within the legislature), Department of Education, and special interest groups.
• Commission arena – Temporary groups of area experts and other prominent individuals.
• Leadership arena – Top-level government executives.
• Macro arena – General public, mass media, and opinion leaders.

Mazzoni found that the subsystem arena was not open to major policy innovation. In the subsystem change was more likely to be based on broad consensus and incremental in nature. When the issue was shifted to another arena the liklihood of more extensive policy change increased. Especially significant were the commission arena, which provided opportunities to generate discussion on innovative measures, and the leadership arena, which was able to exert influence on the subsystem arena.

Mazzoni’s model reflects the educational policy-making system in the U.S., which differs in important ways from other policy-making systems due to the highly decentralized nature of the U.S. educational system. However, the model can be easily adapted to other democratic types of policy-making systems (I doubt the model would have much explanatory value for non-democratic systems). To adapt the model we would have to determine what the relevant arenas are. For most democratic systems Mazzoni’s four arenas probably hold although they might be defined somewhat differently. In many European countries, especially the Nordic countries, it might be reasonable to add a “social partners” arena (i.e. labor and trade unions and employer organizations) because of the influence that they are able to exert on public policy. Other political contexts may need to add other arenas depending on the distribution of political power. Extending and redefining the relevant arenas does not seem problematic to the overall model itself.

Once we have developed a context-relevant arena landscape, we can start to consider how foresight activities fit into the model. It might be suggested that foresight activities would fall under the commission arena since they share certain qualities, especially that they are temporary and that they tend to involve subject experts in their activities. Yet, there are differences that suggest that the commission arena might not be an adequate classification for foresight activities, especially if we consider Da Costa, et al’s, second function of foresight activities. Participants in those kinds of foresight activities are not expected to issue recommendations or even necessarily to reflect on current critical issues. Their purpose is to identify emerging sources of uncertainty for the future and to consider how they might affect society in the long-term. So, there may very well be a case for foresight activities to be defined as a special type of arena. To do so, we would have to demonstrate, based on empirical evidence, the defining characteristics of foresight activities as policy-making arenas. The characteristics that would need to be defined include the arena’s scope, process, visibility and outcome (Mazzoni, 1991, p. 129). Other researchers that have used Mazzoni’s model may have other relevant characteristics that could inform research design.

References

Da Costa, O., Warnke, P., Cagnin, C. & Scapolo, F. (2008). The impact of foresight on policy-making: Insights from the FORLEARN mutual learning process. Technology Analysis and Strategic Management, 20(3), 369-387.

Fowler, F. C. (2006). Struggling with theory: A beginning scholar’s experience with Mazzoni’s arena models. In V. A. Anfara, Jr. & N. T. Mertz (Eds.), Theoretical frameworks in qualitative research (pp. 39-58). Thousand Oaks, CA: Sage Publications.

Kingdon, J. W. (1995). Agenda setting. In S. Z. Theodoulou & M. A. Cahn (Eds.). Public policy: The essential readings (pp.105-113). Upper Saddle River, NJ: Prentice Hall.

Mazzoni, T. L. (1991). Analyzing state school policymaking: An arena model. Educational Evaluation and Policy Analysis, 13(2), 115-138.