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International audience ; Understanding complex problems such as climate change is difficult for most non‐scientists, with serious implications for decision making and policy support. Scientists generate complex computational models of climate systems to describe and understand those systems and to predict the future states of the systems. Non-scientists generate mental models of climate systems, perhaps with the same aims and perhaps with other aims too. Often, the predictions of computational models and of mental models do not correspond with important implications for human decision making, policy support, and behaviour change. Recent research has suggested non-scientists' poor appreciation of the simple foundations of system dynamics is at the root of the lack of correspondence between computational and mental models. We report here a study that uses a simple computational model to 'run' mental models to assess whether a system will evolve according to our aspirations when considering policy choices. We provide novel evidence of a dual-process model: how we believe the system works today is a function of ideology and worldviews; how we believe the system will look in the future is related to other, more general, expectations about the future. The mismatch between these different aspects of cognition may prevent establishing a coherent link between a mental model's assumptions and consequences, between the present and the future, thus potentially limiting decision making, policy support, and other behaviour changes.

International audience ; Understanding complex problems such as climate change is difficult for most non‐scientists, with serious implications for decision making and policy support. Scientists generate complex computational models of climate systems to describe and understand those systems and to predict the future states of the systems. Non-scientists generate mental models of climate systems, perhaps with the same aims and perhaps with other aims too. Often, the predictions of computational models and of mental models do not correspond with important implications for human decision making, policy support, and behaviour change. Recent research has suggested non-scientists' poor appreciation of the simple foundations of system dynamics is at the root of the lack of correspondence between computational and mental models. We report here a study that uses a simple computational model to 'run' mental models to assess whether a system will evolve according to our aspirations when considering policy choices. We provide novel evidence of a dual-process model: how we believe the system works today is a function of ideology and worldviews; how we believe the system will look in the future is related to other, more general, expectations about the future. The mismatch between these different aspects of cognition may prevent establishing a coherent link between a mental model's assumptions and consequences, between the present and the future, thus potentially limiting decision making, policy support, and other behaviour changes.

International audience Understanding complex problems such as climate change is difficult for most non‐scientists, with serious implications for decision making and policy support. Scientists generate complex computational models of climate systems to describe and understand those systems and to predict the future states of the systems. Non-scientists generate mental models of climate systems, perhaps with the same aims and perhaps with other aims too. Often, the predictions of computational models and of mental models do not correspond with important implications for human decision making, policy support, and behaviour change. Recent research has suggested non-scientists' poor appreciation of the simple foundations of system dynamics is at the root of the lack of correspondence between computational and mental models. We report here a study that uses a simple computational model to 'run' mental models to assess whether a system will evolve according to our aspirations when considering policy choices. We provide novel evidence of a dual-process model: how we believe the system works today is a function of ideology and worldviews; how we believe the system will look in the future is related to other, more general, expectations about the future. The mismatch between these different aspects of cognition may prevent establishing a coherent link between a mental model's assumptions and consequences, between the present and the future, thus potentially limiting decision making, policy support, and other behaviour changes.

International audience ; Understanding complex problems such as climate change is difficult for most non‐scientists, with serious implications for decision making and policy support. Scientists generate complex computational models of climate systems to describe and understand those systems and to predict the future states of the systems. Non-scientists generate mental models of climate systems, perhaps with the same aims and perhaps with other aims too. Often, the predictions of computational models and of mental models do not correspond with important implications for human decision making, policy support, and behaviour change. Recent research has suggested non-scientists' poor appreciation of the simple foundations of system dynamics is at the root of the lack of correspondence between computational and mental models. We report here a study that uses a simple computational model to 'run' mental models to assess whether a system will evolve according to our aspirations when considering policy choices. We provide novel evidence of a dual-process model: how we believe the system works today is a function of ideology and worldviews; how we believe the system will look in the future is related to other, more general, expectations about the future. The mismatch between these different aspects of cognition may prevent establishing a coherent link between a mental model's assumptions and consequences, between the present and the future, thus potentially limiting decision making, policy support, and other behaviour changes.

Expertise in research integration and implementation is an essential but often overlooked component of tackling complex societal and environmental problems. We focus on expertise relevant to any complex problem, especially contributory expertise, divided into 'knowing-that' and 'knowing-how.' We also deal with interactional expertise and the fact that much expertise is tacit. We explore three questions. First, in examining 'when is expertise in research integration and implementation required?,' we review tasks essential (a) to developing more comprehensive understandings of complex problems, plus possible ways to address them, and (b) for supporting implementation of those understandings into government policy, community practice, business and social innovation, or other initiatives. Second, in considering 'where can expertise in research integration and implementation currently be found?,' we describe three realms: (a) specific approaches, including interdisciplinarity, transdisciplinarity, systems thinking and sustainability science; (b) case-based experience that is independent of these specific approaches; and (c) research examining elements of integration and implementation, specifically considering unknowns and fostering innovation. We highlight examples of expertise in each realm and demonstrate how fragmentation currently precludes clear identification of research integration and implementation expertise. Third, in exploring 'what is required to strengthen expertise in research integration and implementation?,' we propose building a knowledge bank. We delve into three key challenges: compiling existing expertise, indexing and organising the expertise to make it widely accessible, and understanding and overcoming the core reasons for the existing fragmentation. A growing knowledge bank of expertise in research integration and implementation on the one hand, and accumulating success in addressing complex societal and environmental problems on the other, will form a virtuous cycle so that each strengthens the other. Building a coalition of researchers and institutions will ensure this expertise and its application are valued and sustained.