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- Definitions of integrate modelling and assessment and key terms in
the field. This will include the importance of the inclusion of areas
such as the social sciences, as well as the concept of integrated assessment
without the modelling component.
- Model complexity
- how to reduce real world complexity to a tractable level while still
retaining some sense of 'realism'.
- model disaggregation and 'modularisation' as a way forward for increased
model flexibility and limiting model complexity.
- Model validation versus justification
- difficulties involved for IAM and how to communicate IAM results
and validity with those used to 'single discipline' rigorous approaches.
- methods for tracing why different processes are included and excluded
from the modelling.
- How to communicate the issues, tools, outcomes and suggestions of
IAM projects to nonspecialists. This links to the questions about the
role of IAM users in the modelling process and the need for approaches
to integrate the knowledge of scientists, managers and others for IAM.
- Institutional frameworks and policy environment - problems with
these supporting the time and effort required to convey the IAM message.
- The need to better understand the potential role of networks of
decision makers and outcomes of planned policy - understanding the
divergence of modelled and real world outcomes.
- Tools available for integrated 'real world' problems involved in
IAM and
their maturity of development.

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