Environmental Engineering Reference
In-Depth Information
want to improve the quality of their policies and obtain a broader support for and
understanding of the proposed policy measures. In this new governance concept,
the policy making process is the product of complex interactions between
governmental and non-governmental organizations, each seeking to influence the
collectively binding decisions that have consequences for their interest. Policy
making is more and more a process of cooperation and participation in which the
policy maker becomes a facilitator of the process.
To account for this new governance, policy measures need to be assessed in an
integrated context. Rotmans and van Asselt
(1996)
defined Integrated Assessment
(IA) as “an interdisciplinary and participatory process combining, interpreting and
communicating knowledge from diverse scientific disciplines to allow a better
understanding of complex phenomena”. In this interdisciplinary and participatory
process, information needs to be accessible in the way that all different types of
stakeholders achieve a mutual understanding of the problems, objectives and
solutions. But this mutual understanding across disciplines is often hindered
by jargon, language, past experiences and presumptions of what constitutes
persuasive argument, and different outlooks across disciplines or experts of what
makes knowledge or information salient for policy makers or policy assessments
(Cash et al.
2003)
. This mutual understanding is essential in integrated assessment
studies in which modelling frameworks are built from multiple models, data
sources and indicators, which have to be coupled meaningfully. The software
architecture of modelling frameworks should be such that it reflects and enables
the mutual understanding of the group of researchers.
This chapter addresses problems and offers some solutions concerning knowledge
integration in integrated assessment studies, first by analysing the theoretical
perspective and then discussing the key role played by ontologies to support model
integration in the context of the SEAMLESS project. Finally, a web-based software
architecture for implementing modelling frameworks in integrated assessment studies
is presented and we demonstrate its use to improve mutual understanding within the
researchers' team. Along the way, the paper details the challenges and the processes
used to manage and tame the complexity of a large European integrated project
(e.g. SEAMLESS) and the use of an ontology to support (linking of) projects, models,
indicators and raw data. This chapter concludes this paper by describing the major
functionalities offered by SEAMLESS-IF and its expected future developments.
Challenges in Integrated Modelling
Interoperability from a Theoretical Perspective
Integrated modelling requires interoperability, which is the ability of two or more
systems or components to exchange information and to use the information
that has been exchanged (IEE
1990)
. Sølvberg (1998) distinguished between
syntactic, structural and semantic interoperability. Syntactic interoperability is defined
