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.