http://casl.ucd.ie/iru/index.php/eccs-2011-satellite
Summary of the ECCS 2011 Satellite
Workshop “Policy Modelling”
This
satellite workshop was about policy modelling with a focus on innovation
policy. Policy modelling means to identify
areas, which need intervention, to specify the desired state of the target
system, to find the regulating mechanisms, policy formation and implementation,
and to control and evaluate the robustness of interventions. The methodological difficulty hereby is to bridge the gap
between policy practice often expressed in qualitative and narrative terms and the
scientific realm of formal models. Furthermore, policymaking in complex social systems is
no clear-cut cause-effect process but characterised by contingency and
uncertainty. To take into account
technological, social, economic, political, cultural, ecological and other
relevant parameters, policy modelling has to be enhanced and supported by new
ICT-oriented research initiatives. Reviewing the current state-of-the-art of
policy context analysis such as forecasting, foresight, backcasting, impact
assessment, scenarios, early warning systems, and technology roadmapping, the
need for policy intelligence dealing with complexity becomes more and more
obvious.
Both
days were opened by invited keynote speakers. On the first day, John Casti,
showed in his keynote “Computational modelling and the complexity of policy”
how what he termed complexity overload acts
as the root cause of extreme events in all social environments, how social mood
theory and agent-based modelling can help us develop tools to anticipate such
events, and how this approach worked in case studies for the Finnish and
Scottish policy sponsors within the Game Changers project of the IIASA Xevents
activity.
This opened the floor for a day on the relation of complexity-based
policy domains and ICT. We had presentations reporting concrete experiences
with computational requirements and tools for policy modelling (José Javier
Alba Sánchez, Luca Minghini and Gianluca Misuraca), and more exploratory
studies about the possibility to create a general computational
framework which uses evidence-based policy making to encapsulate socio-economic
principles for creating enduring institutions for smart infrastructure
management (Jeremy Pitt). Existing simulation models of technological
evolution, knowledge dynamics and the emergence of innovation networks were
introduced and discussed for validation potential to be relevant or
stakeholders (Christopher Watts). There has been methodological progress in
interpreting models’ processes and results, but relevance is still largely
theoretical and reliability of validation procedures is incipient (Pablo Lucas). Case studies using specific models for
innovation policy modelling were then presented such as two studies adapting
and developing the SKIN model (Simulating
Knowledge Dynamics in Innovation Networks; http://cress.soc.surrey.ac.uk/SKIN/), a study
for DG INFSO on ex-ante evaluation of the FP7 successor programme Horizon 2020 (Petra Ahrweiler, Michel Schilperoord, Nigel Gilbert and
Andreas Pyka), and a simulation of the Vienna biotech cluster and its socio-economic
dynamics, with a special focus on the influence of public research funding on
its innovation performance (Manuela Korber and Manfred Paier). To assist
policymakers with simulation results (e.g. Ozge Kalkan on waste resources
markets), specific attention was drawn to modelling decision making processes
(Diane Payne).
In
Policy Modelling there is an uneasy relationship between two different worlds:
the "hard" "scientific" world of measurement, data, and
analytic/computer modelling, mostly occupied by academics who see themselves as
aiming for the truth behind complex socio-political systems and the
"soft" world of "policy practice", where human
understanding, communication and pragmatic decision making predominate, mostly
occupied by pressure groups, advisers, politicians and consultants whose aim is
to make acceptably good decisions. This
depth of this divide seems to be due to the complexity of human socio-political
phenomena. It results in a distinction between two communities who are thinking
about the same problems of policy making, and makes effective combination of
these approaches difficult. Those in the field have sometimes downplayed this
divide, but it makes for a real and substantial obstacle. From the "scientific" point of view
the policy side can seem to deliberately ignore the complexity of the
phenomena, to not care about the truth and with a tendency to look for
justifications for decisions that have already been made. From the
"policy" side the scientific approach can seem to be politically
naive, remote, inarticulate, and useless in the sense that they almost never
will give straight recommendations but rather just abstract explanation in ways
that are often difficult to understand.
However both sides are often motivated to try and bridge this divide,
sensing that better decisions might be facilitated as a result.
The
second day of the workshop focussed on the issues, ways and approaches for
bridging this gap. This is timely since complexity science could be a bridging
stone in this exercise, for the first time allowing the possibility of building
such bridges in a well-founded manner. The papers and discussion at the
workshop showed that the topics are very relevant and topical in today’s world,
but still largely unresolved. Overcoming these difficulties calls for a deeper
attention to human specific features, such as: immediacy, cognition, language,
understanding, and interpretation, but it also showed that complexity science
might be able to help and thus accommodate these features and, ultimately, to
support effecting improvements in everyday policy practices.
The papers
presented also showed that the dramatic progress of ICT can help with this
project, by facilitating the acquisition and immediate use of stakeholder
narratives, allowing their “situated experiences” to directly feedback into the
policy process. Dave Snowden’s keynote
presentation showed how implicit ground-level feedback techniques, by-passing
the traditional slow modelling process, can be used to improve the guidance of
policy by feeing the flow of information from those involved to the policy
makes in a more direct manner. This was
echoed in Bruce Edmonds’ analogy between how the control problem in AI/robotics
mirrors that of active policy formation. Sylvie Occelli pointed out that
modelling might be used as both an activity as well as an artefact, that
modelling could be a mediator engaging a wider selection of experts and
stakeholders in the policy making process.
As an activity it can aid the integration between the humanistic world
of understanding, and that of the scientific world of data. As an artefact it can be an element in the
policy debate alongside other means of expression.
Two of the speakers: Giovanni
Rabino, and also Magdalena Bielenia-Grajewska chose to examine the important
role of language in the policy making process, whilst John
Sutcliffe-Braithwaite argued for a meta-modelling language for specifying and
talking about policy and social issues. However, the debate also showed that
this potential is still underexploited in current policy practice. In this
respect, much further research was indicated, in particular how policy
modelling might be used as a way to build different mixes of argumentative and
syntactic approaches and so improve the production of policies over the long
run.
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