| Preface | |
| Modelling and Software as Instruments for Advancing Sustainability | |
| Summary | |
| Introduction | |
| Aims of the Summit | |
| The role of modelling and software | |
| Common problems in modelling | |
| Current state of the art and future challenges in modelling | |
| Generic issues | |
| Sectoral issues | |
| Conclusions References | |
| Good Modelling Practice | |
| Summary | |
| Introduction | |
| Key components of good modelling practice | |
| Model purpose | |
| Model evaluation | |
| Performance measures | |
| Stating and testing model assumptions | |
| Ongoing model testing and evaluation | |
| Model transparency and dissemination | |
| Terminology | |
| Reporting | |
| Model dissemination | |
| A definition of good modelling practice | |
| Progress towards good modelling practice | |
| Recommendations | |
| References. | |
| Bridging the Gaps between Design and Use: Developing Tools to Support Environmental Management and Policy | |
| Summary | |
| A gap between design and use? | |
| Decision and information support tool review | |
| Supporting organisational decision making | |
| Supporting participatory and collaborative decision making | |
| The nature and extent of the gap | |
| Good practice guidelines for involving users in development | |
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| Conclusions | |
| References | |
| Complexity and Uncertainty: Rethinking the Modelling Activity | |
| Summary | |
| Introduction | |
| Uncertainty: causes and manifestations | |
| Causes of uncertainty | |
| Manifestation of uncertainty | |
| A conceptual approach to deal with uncertainty and complexity in modelling | |
| Prediction | |
| Exploratory analysis | |
| Communication | |
| Learning | |
| Examples | |
| Prediction: model use in the development of the US clean air mercury rule | |
| Exploratory analysis: microeconomic modelling of land use change in a coastal zone area | |
| Communication: modelling water quality at different scales and different levels of complexity | |
| Learning: modelling for strategic river planning in the Maas, the Netherlands | |
| Conclusions | |
| Models for prediction purposes | |
| Models for exploratory purposes | |
| Models for communication purposes | |
| Models for learning purposes | |
| References | |
| Uncertainty in Environmental Decision Making: Issues, Challenges and Future Directions | |
| Summary | |
| Introduction | |
| Environmental Decision-Making Process | |
| Sources of Uncertainty | |
| Progress, Challenges and Future Directions | |
| Risk-based assessment criteria | |
| Uncertainty in human input | |
| Computational efficiency | |
| Integrated software frameworks for decision making under uncertainty | |
| Conclusions | |
| References | |
| Environmental Policy Aid under Uncertainty | |
| Summary | |
| Introduction | |
| Factors influencing perceptions of uncertainty | |
| Uncertainty in decision models | |
| Uncertainty in practical policy making | |
| Reducing uncertainty through innovative policy interventions | |
| Discussion and conclusions | |
| References | |
| Integrated Modelling Frameworks for Environmental Assessment and Decision Support | |
| Summary | |
| Introduction | |
| A first definition | |
| Why do we develop new frameworks? | |
| A more insightful definition | |
| A generic architecture for EIMFs | |
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| Knowledge representation and management | |
| Challenges for knowledge-based environmental modelling | |
| Model Engineering | |
| Component-based modelling | |
| Distributed modelling | |
| Driving and supporting the modelling process | |
| The experimental frame | |
| Conclusions | |
| References | |
| I | |
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