The Science-Policy Ecosystem: Managing Variabilities, Complexities, and Trade-offs

It is often believed that scientific information and knowledge travels through a linear pipeline that leads straight from researchers to policy makers to use in decision-making and planning. It isn’t so simple–values, political considerations, and available resources are all part of the policy-making process. Peter Gluckman, the Chief Science Advisor to the New Zealand Prime Minister, asserts on the topic of science advice, that ensuring that science interfaces with societal needs have never been more urgent than now (Gluckman, 2017). Besides the ever-increasing complex problems facing the world today, Gluckman talked about the challenges of a society immersed in what he calls “post-truth” and “post-trust” culture that has higher expectations for evidence but where it is common to see facts distorted and manipulated and less trust in the institutions of science and policy. While science and policy intersect on multiple fronts through outputs, processes, actors, and contexts (Van den Hove, 2007), a recognized disconnect between the two has led to ineffective implementations of science in policy and decision-making.

Connecting science with policy through science-policy interfaces (SPIs) is recognized as essential for effective governance. Formally defined, scholars such as Van den Hove (2007) and Sarrki et al. (2014) describe SPIs as “social processes which encompass relations between scientists and other policy actors, and which allow for exchanges, co-evolution and joint construction of knowledge with the aim of enriching decision-making or research” (p.194). A SPI implies two-way exchange–not just of data or information but also of values, priorities, and goals (Sarkki et al., 2014). SPIs can take the form of complex, highly formalized institutions like the Intergovernmental Platform on Biodiversity and Ecosystem Services or can be as simple as a conversation between two people.

Van den Hove (2007) asserts the main purpose of SPIs is to address the multitude of challenges that exist at the intersection of science and policy. Thus, ensuring effective science for policy is not so simple. SPIs are characterized by a diversity of actors, scales, information, and contexts. Both policy makers and scientists must understand and manage these factors to improve the effectiveness of SPIs and encourage the two-way communication and co-production of knowledge necessary for their success.

Multiple actors play different roles within SPIs; these actors can range from researchers, science advisors, policy analysts and decision-makers, NGOs, the media, and interested public (MacDonald et al., 2016). However, no actor exists without values, motivations, and worldviews–even scientists who claim their objectiveness and neutrality. Crouzat et al. (2018) reason that varying biases create scientists with different “roles” within policy work. Acknowledging those biases and how they inform a scientist’s communication is one way to develop trust with policy-makers and enhance the effectiveness of SPIs. MacDonald et al. (2016) further emphasize the importance of boundary organizations that operate at the interface to bridge what Gluckman (2017) recognizes as “two inherently different cultures.” These “honest brokers” (as per Crouzat et al., 2018) act directly at the interface by providing added or alternate policy options and can focus information to highlight what is necessary for decision-making.

Just as SPIs involve a multitude of actors, they similarly rarely deal with a single issue or body of information (MacDonald et al., 2016). Challenges emerge as effective science-policy interfaces aim to integrate different information from different disciplines; this suggests the potential need for further translation even among scientists to understand the information being shared. Furthermore, the lack of available data and inherent uncertainties around information is a consistent challenge that SPIs must navigate. Van den Hove (2007) suggests that this challenge requires SPIs to foster communication about assumptions and uncertainties and state the boundaries of what is known compared to what is being asked. How policy uses information is substantially impacted by both how information is framed and presented (MacDonald et al., 2016). Effective communication thus relies on addressing challenges posed by creating information products that translate sometimes complex, uncertain information.

The science-policy interface is fundamentally influenced by the external context in which it is imbedded. MacDonald et al. (2016) refer to the “politicizing of science”–this reflects what Van den Hove (2007) describes as a “constant intermingling between facts and values”. Science is intrinsically value-driven; social and political contexts often guide what problems are important, what types of research is funded, and even how scientists are educated (Van den Hove, 2007). Van den Hove (2007) also argues for science as an “open system” not isolated from society and the environment. However, policy issues and the surrounding social and environmental contexts can change over time (Sarkki et al., 2014). Effective SPIs should thus promote adaptive management that considers the dynamic nature of diverse contexts to increase the usability of information (MacDonald et al., 2016).

The issues, actors, and contexts of SPIs can all act along various administrative, spatial, and time scales. SPIs can exist for decisions that affect cities, countries, or even the world, addressing widespread issues such as climate change; however, the same strategies may not apply at all levels. The different motivations and timescales at which science and policy operate can lead to a disconnect: policy makers often work on a political schedule that demands rapid response and certainty, while science may take years to produce meaningful results (MacDonald et al., 2016; Sarkki et al., 2014). Different policy stages also demand different information, and so scientists must be integrated within policy to solve these types of mismatches (Van den Hove, 2007). Recognizing these changing and variable scales demands iterativity (Sarkki et al., 2015), which requires SPIs be adaptive, flexible, and take a system’s approach.

Ultimately, science-policy interfaces aim to build relationships, increase communication and transparency, and build the capacity for meaningful science-policy work. Effective SPIs will lead to knowledge translation and eventually policy decisions that are credible, legitimate, relevant (known as CRELE attributes, from Cash et al., 2003), and iterative (Sarrki et al., 2015). Constraints such as resources, timelines, and funding all limit the ability to produce interdisciplinary, participatory, and thorough science-policy work. Sarrki et al. (2014) identify several trade-offs that science-policy work must balance, reflecting intrinsic conflicts between values, goals, and objectives. For example, Sarrki et al. (2014) describes the clarity-complexity trade-off, which reflects a choice between delivering clear, simple, and strong messages which are relevant, against complex, more ambiguous messages which can be credible and legitimate.

The science-policy interface is dynamic, not linear, and in many ways value-driven. It is not enough to simply promote more efficient use of scientific facts in decision-making. We must move away from the traditional “linear” model (Koetz, Farrell, & Bridgewater, 2011) where science and policy are separate and move towards a “collaborative model” that sees science and policy as an ecosystem and recognizes the complex and dynamic nature of the interface, influenced by the external society and environment. Resolving these challenges rests not only on the shoulders of boundary organizations, but also on the political institutions and scientists involved in science-policy work. Furthermore, addressing these challenges may require fundamental institutional changes to timelines, research priorities, and funding mechanisms present in both scientific and political institutions. The way forward thus requires acknowledging the variabilities, complexities, and trade-offs that characterize science-policy interfaces.



Cash, D. W., Clark, W. C., Alcock, F., Dickson, N. M., Eckley, N., Guston, D. H., … Mitchell, R. B. (2003). Knowledge systems for sustainable development. Proceedings of the National Academy of Sciences of the United States of America (PNAS), 100(14), 8086–8091.

Crouzat, E., Arpin, I., Brunet, L., Colloff, M. J., Turkelboom, F., & Lavorel, S. (2018). Researchers must be aware of their roles at the interface of ecosystem services science and policy. Ambio, 47(1), 97–105.

Gluckman, P. (2017, September). Science advice: A bastion against the post-truth / post-trust torrent? Keynote address to the Annual Conference of the Joint Research Centre of the European Commission. Brussels. Retrieved from

Koetz, T., Farrell, K. N., & Bridgewater, P. (2011). Building better science-policy interfaces for international environmental governance: Assessing potential within the Intergovernmental Platform for Biodiversity and Ecosystem Services. International Environmental Agreements: Politics, Law and Economics, 12(1), 1–21.

MacDonald, B. H., Soomai, S. S., De Santo, E. M., & Wells, P. G. (2016). Understanding the science-policy interface in integrated coastal and ocean management. In B. H. MacDonald, S. S. Soomai, E. M. De Santo, & P. G. Wells (Eds.). Science, information, and policy interface for effective coastal and ocean management (pp. 19-43). Boca Raton, FL: CRC Press.

Sarkki, S., Niemelä, J., Tinch, R., van den Hove, S., Watt, A., & Young, J. (2014). Balancing credibility, relevance and legitimacy: A critical assessment of trade-offs in science–policy interfaces. Science & Public Policy, 41(2), 194-206.

Sarkki, S., Tinch, R., Niemelä, J., Heink, U., Waylen, K., Timaeus, J., … van den Hove, S. (2015). Adding “iterativity” to the credibility, relevance, legitimacy: A novel scheme to highlight dynamic aspects of science-policy interfaces. Environmental Science and Policy, 54, 505–512.

Van den Hove, S. (2007). A rationale for science–policy interfaces. Futures, 39 (7), 807-826.


Authors: Calinda Brown and Jenny Weitzman

This blog post is part of a series of posts authored by students in the graduate course “The Role of Information in Public Policy and Decision Making” offered at Dalhousie University.


Please follow and like us: