By Marijn Janssen, Maria A. Wimmer, Ameneh Deljoo

The explosive progress in info, computational energy, and social media creates new possibilities for innovating the procedures and recommendations of data and communications know-how (ICT) established policy-making and learn. to use those advancements within the electronic international, new techniques, ideas, tools and strategies are had to navigate the societal and computational complexity. This calls for vast interdisciplinary wisdom of public management, coverage analyses, details platforms, complicated platforms and machine technological know-how. This publication offers the root for this new interdisciplinary box, within which a number of conventional disciplines are mixing. either coverage makers, executors and people accountable for coverage implementations recognize that ICT is turning into extra very important and is altering the policy-making approach, leading to a subsequent iteration policy-making in line with ICT help. net 2.0 or even internet 3.0 element to the explicit functions of social networks, semantically enriched and associated information, while policy-making has additionally to do with using the titanic quantity of knowledge, predictions and forecasts, and bettering the results of policy-making, that is faced with an expanding complexity and uncertainty of the results. the sphere of policy-making is altering and pushed through advancements like open facts, computational equipment for processing info, opining mining, simulation and visualization of wealthy facts units, all mixed with public engagement, social media and participatory tools.

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2003; Weisbuch 2004). The latter starts with some simple assumptions about how agents’ opinions affect the opinions of other agents, and shows under which circumstances the result is a consensus, polarisation, or fragmentation. However, how could these results be validated against observations without involving again a considerable amount of theory? 40 P. Ahrweiler and N. Gilbert Important features of the target might not be observable at all. We cannot, for example, observe learning. We can just use some indicators to measure the consequences of learning and assume that learning has taken place.

In the case of Caffè Nero, we cannot simply go to Venice, drink our coffee, be happy, and return. It is too expensive and 3 The Quality of Social Simulation: An Example from Research Policy Modelling 37 time-consuming. We have to use the simulation. In the case of a science simulation, we cannot get data from the real system to learn about its behaviour. We have to use the simulation. The question, whether one or the other is a good simulation, can therefore be reformulated as: Do we get from the simulation what we constructed it for?

This process includes not only the design and construction of the simulation model itself but also the whole interaction between stakeholders, study team, model, and findings. P. de N. Gilbert University of Surrey, Guildford, UK © Springer International Publishing Switzerland 2015 M. Janssen et al. 1007/978-3-319-12784-2_3 35 36 P. Ahrweiler and N. e. do we get from it what we want? e. do we get from it what we want? This chapter deals with the assessment of the quality of a simulation. After discussing this issue on a general level, we apply and test the assessment mechanisms using an example from policy modelling.

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