The interface between land use systems research and policy: Multiple arrangements and leverages

Sterk, B.; Carberry, Peter; Leeuwis, C.; Ittersum, M.K. van; Howden, Mark; Meinke, Holger; Keulen, H. van; Rossing, W.A.H.

doi:10.1016/j.landusepol.2008.05.003

Cited by 39 publications

(18 citation statements)

References 39 publications

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“…In this case, the 'knowledge broker' role was mainly played by a social scientist (i.e. the author of this article), who acted as a broker or a 'stepping stone' (Sterk 2009b) between a group of computer modellers -the 'researchers' -and key officials in DG Agriculture and Rural Development (DG agriculture) within the European Commission -the 'policy makers'. The distinction between 'researchers' and 'policy makers' is an artificial one and in reality the boundary between science and politics is commonly rather fuzzy: Within DG Agriculture, policy officials are often technically trained with significant experience of using economic models before entering the Commission.…”

Section: 2) Selecting a Knowledge Brokering Strategymentioning

confidence: 99%

Contexualising the tool development process through a knowledge brokering approach: The case of climate change adaptation and agriculture

Adelle

2015

Environmental Science & Policy

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This article applies a 'knowledge brokering' approach to contextualise the development of an integrated computer modelling tool into the real world policy context of adaptation of agriculture to climate change at the EU level. In particular, the article tests a number of knowledge brokering strategies described and theorized in the literature, but seldom empirically tested. The article finds that while the policy context can be used to identify a theoretically informed knowledge brokering strategy, in practice a strategy's 'success' is more informed by practical considerations, such as whether the tool development process is knowledge or demand driven. In addition, in practice the knowledge brokering process is found to be dynamic and messy, which is not always apparent in the literature. The article goes on to question the perception that there is always a need (or a desire) to bridge the gap between researchers and policy makers in the tool development process. Rather than a problem of design and communication, the science policy interface may be characterised more by a high level of competition between researchers and research organisations to have their tool legitimised by its use in the policy making process.

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Section: 2) Selecting a Knowledge Brokering Strategymentioning

confidence: 99%

Contexualising the tool development process through a knowledge brokering approach: The case of climate change adaptation and agriculture

Adelle

2015

Environmental Science & Policy

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“…Decision makers are also subject to bounded rationality (Simon 1972), cognitive biases and constraints (Kahneman 2011), a reliance on often firmly entrenched heuristics (Gigerenzer and Todd 2001), and divergent but valid rationalities (Faucheux et al 1995, Kollock 1998, Größler 2004, Sen 2009) when confronted with unstructured problems and complex systems. Clay and Schaffer (1986), Funtowicz and Ravetz (1993), Hisschemöller and Hoppe (1996), Sterk et al (2009), andStirling (2010) argue that decision makers regularly deploy strategies to reduce the complexity of policy choice arenas, avoiding scrutiny of proposed initiatives and limiting the exploration of alternatives that correspond with stated objectives. Containment biases that either limit or omit the representation of contested values, or restrict knowledge and arguments to those that correspond with criteria acceptable to current political beliefs are common strategies.…”

Section: Introductionmentioning

confidence: 99%

Visions, beliefs, and transformation: exploring cross-sector and transboundary dynamics in the wider Mekong region

Smajgl¹,

Ward²,

Foran³

et al. 2015

E&S

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ABSTRACT. Policy and investment decisions in highly connected, developing regions can have implications that extend beyond their initial objectives of national development and poverty reduction. Local level decisions that aim to promote trajectories toward desirable futures are often transformative, unexpectedly altering factors that are determined at higher regional levels. The converse also applies. The ability to realize desirable local futures diminishes if decision-making processes are not coordinated with other influential governance and decision levels. Providing effective support across multiple levels of decision making in a connected, transformative environment requires (a) identification and articulation of desired outcomes at the relevant levels of decision making, (b) improved understanding of complex cross-scale interactions that link to potentially transforming decisions, and (c) learning among decision makers and decision influencers. Research implemented through multiple participatory modalities can facilitate such relevant system learning to contribute to sustainable adaptation pathways. We test application of a systematic policy engagement framework, the Challenge and Reconstruct Learning or ChaRL framework, on a set of interdependent development decisions in the Mekong region. The analysis presented here is focused on the implementations of the ChaRL process in the Nam Ngum River Basin, Lao People's Democratic Republic and the Tonle Sap Lake and environs, Cambodia to exemplify what cross-scale and cross-sectoral insights were generated to inform decision-making processes in the wider Mekong region. The participatory process described aligns the facilitated development of scenarios articulating shared future visions at local and regional levels with agent-based simulations and facilitates learning by contrasting desired outcomes with likely, potentially maladaptive outcomes.

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“…They characterize the institutional sciencepolicy space and help to explain experiences of interactions between science and policy. Building on the work of Hoppe (2005), Sterk et al (2009b) define four boundary arrangements based on two criteria: (1) who is perceived to initiate the research, that is, 'science' or 'policy', and (2) how logical and appropriate it is to integrate scientific knowledge and policy. Acknowledging the different existing boundary arrangements makes explicit the institutional space in which modellers function and the arrangements or facilitators that may assist in model introduction.…”

Section: When and How Are Computer Models Used In Practice?mentioning

confidence: 99%

Computerized models: tools for assessing the future of complex systems?

Ittersum¹,

Sterk²

2015

The Tools of Policy Formulation

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The interface between land use systems research and policy: Multiple arrangements and leverages

Cited by 39 publications

References 39 publications

Contexualising the tool development process through a knowledge brokering approach: The case of climate change adaptation and agriculture

Contexualising the tool development process through a knowledge brokering approach: The case of climate change adaptation and agriculture

Visions, beliefs, and transformation: exploring cross-sector and transboundary dynamics in the wider Mekong region

Computerized models: tools for assessing the future of complex systems?

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