Optimization for Policy Making: The Cornerstone for an Integrated Approach

  • Michela Milano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8124)


Policy making is a very complex task taking into account several aspects related to sustainability, namely impact on the environments, health of productive sectors, economic implications and social acceptance. Optimization methods could be extremely useful for analysing alternative policy scenarios, but should be complemented with several other techniques such as machine learning, agent-based simulation, opinion mining and visualization to come up with an integrated system able to support decision making in the overall policy design life cycle. I will discuss how these techniques could be merged with optimization and I will identity some open research directions.


Logic Program Pareto Optimal Solution Opinion Mining Sentiment Analysis Policy Making Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Gavanelli, M., Riguzzi, F., Milano, M., Cagnoli, P.: Logic-Based Decision Support for Strategic Environmental Assessment. In: Theory and Practice of Logic Programming, 26th Int’l. Conference on Logic Programming, ICLP 2010, vol. 10(4-6), pp. 643–658 (July 2010), Special IssueGoogle Scholar
  2. 2.
    Gavanelli, M., Riguzzi, F., Milano, M., Cagnoli, P.: Constraint and optimization techniques for supporting policy making. In: Yu, T., Chawla, N., Simoff, S. (eds.) Computational Intelligent Data Analysis for Sustainable Development, Routledge (2013)Google Scholar
  3. 3.
    Gilbert, N.: Agent based models. Sage Publications Inc. (2007)Google Scholar
  4. 4.
    Milano, M.: Sustainable energy policies: research challenges and opportunities. In: Design, Automation and Test in Europe, DATE, pp. 1143–1148 (2013)Google Scholar
  5. 5.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1-2), 1–135 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Michela Milano
    • 1
  1. 1.DISIUniversity of BolognaBolognaItaly

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