Evidence with insight : what models contribute to EU research
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Will formal models of policy-making like those in this book ultimately become reliable aids to policy-makers? We do not share the pessimism of Rubinstein (2000: 74 75) who attributes no predictive power to game theory: I am not convinced that Game Theory is more valuable than a detective novel, a romantic poem, or a game of chess in improving the strategic capabilities of practitioners. In particular, he points to the artificiality of some concepts, like mixed strategies, and notes that the usage of mathematical symbols creates an illusion of preciseness which does not have any basis in reality . To the contrary, we all believe that our models can enlighten political debates, even when, as now, they have achieved only modest levels of predictive success.
Translating between the academic world and the policy world is never easy, however. Complicated models require explanation in plain language, with their limitations and imprecisions conveyed clearly to nonspecialists. Commercial opinion surveys have made much progress in this regard, and the best of them include in their reports information on their sample size, margin of error, and the response rates. Similarly, many evaluation associations impose a set of standards on their members who do practical research.We have tried to meet those standards throughout this book and particularly in Chapter 10, where our forecasting successes and limitations are described in detail.
The present volume has shown that a critical dialogue between different modelling traditions applied to a single data set forces researchers to make their modelling choices transparent, as honest science requires. It also makes the models vulnerable to empirical findings, a critical step in intellectual progress. Then, when models reach a certain stage of maturity and empirical success, reporting the results of such dialogues to policy-makers in accessible form seems to us part of the scholarly mission.
In short, we feel that this book itself proposes a model beyond those included in the individual chapters a working model for collaborative social scientific research that aims at addressing issues of broad social interest and concern. In this project, we have engaged in a double dialogue in which different theoretical claims encountered each other while each model faced a common data set. This kind of work is not easy, it is not cheap, and it imposes substantial administrative and managerial burdens on its practitioners. But when it is possible, both science and policy benefit. We are glad we did it. But of course, the present book is just a beginning. We are confident that subsequent double dialogues will build on our findings, correct our errors, and continue to improve our common understanding of EU decision-making.
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SCHNEIDER, Gerald, Bernard STEUNENBERG, Mika WIDGRÉN, 2006. Evidence with insight : what models contribute to EU research. In: THOMSON, Robert, ed. and others. The European Union Decides. Cambridge: Cambridge Univ. Press, 2006, pp. 299-316BibTex
@incollection{Schneider2006Evide-4301, year={2006}, title={Evidence with insight : what models contribute to EU research}, publisher={Cambridge Univ. Press}, address={Cambridge}, booktitle={The European Union Decides}, pages={299--316}, editor={Thomson, Robert}, author={Schneider, Gerald and Steunenberg, Bernard and Widgrén, Mika} }
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Commercial opinion surveys have made much progress in this regard, and the best of them include in their reports information on their sample size, margin of error, and the response rates. Similarly, many evaluation associations impose a set of standards on their members who do practical research.We have tried to meet those standards throughout this book and particularly in Chapter 10, where our forecasting successes and limitations are described in detail.<br />The present volume has shown that a critical dialogue between different modelling traditions applied to a single data set forces researchers to make their modelling choices transparent, as honest science requires. It also makes the models vulnerable to empirical findings, a critical step in intellectual progress. 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