@incollection{GavRigMilCag13-CIDASD13-BC,
editor = { Ting Yu and Nitesh Chawla and Simeon Simoff},
author = {Marco Gavanelli and Fabrizio Riguzzi and Michela Milano and Paolo Cagnoli},
title = {Constraint and Optimization techniques for supporting Policy Making},
booktitle = {Computational Intelligent Data Analysis for Sustainable Development},
year = {2013},
series = {Data Mining and Knowledge Discovery Series},
publisher = {Chapman \& Hall/CRC},
chapter = {12},
pages = {361-382},
abstract = {Public institutions develop policies and plans in order to achieve
economic and social development while preserving the environment. This
is a difficult task where computational intelligence data analysis
techniques can provide an important contribution. The policy maker has
to take decisions by optimizing a set of often conflicting objectives
and satisfying a set of constraints. The aim is to reduce negative
impacts and enhance positive impacts of plan decisions on the
environment, society and economy, exploiting all the data that is
available on the territory that is targeted.
Up to now, only agent-based simulation models have been proposed in
the literature for policy making. In these models, agents represent
the parties involved in the decision making and implementation process
and simulation is used in order to evaluate the impacts of the policy.
Agent-based simulation models provide ``individual level models'': we
claim that the policy planning activity needs also a global
perspective that faces the problem at a global level while tightly
interacting with the individual level model.
We thus propose a mathematical optimization model that can be applied to
regional planning. In the model, decision variables represent
political decisions (for instance the magnitude of a given activity in
the regional plan), potential outcomes are associated with each
decision by considering the available data, constraints limit possible
combination of assignments of decision variables, and objectives
can be used either to evaluate alternative solutions, or translated
into additional constraints. The model has been solved with Constraint
Programming techniques.
The model has been tested on the Emilia-Romagna regional energy plan.
The results have been validated with an expert in policy making and
impact assessment to evaluate the accuracy of the results.},
url = {http://www.crcnetbase.com/doi/abs/10.1201/b14799-18},
doi = {10.1201/b14799-18},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/GavRigMil-CIDASD13.pdf},
isbn = {978-1-43-989594-8},
isbn = {978-1-4398-9595-5},
address = {Abingdon, UK}
}
@article{AlbGavLam13-FI-IJ,
author = {Marco Alberti and
Marco Gavanelli and
Evelina Lamma},
title = {The CHR-based Implementation of the {SCIFF} Abductive System},
journal = {Fundam. Inform.},
volume = {124},
number = {4},
pages = {365--381},
year = {2013},
url = {http://dx.doi.org/10.3233/FI-2013-839},
doi = {10.3233/FI-2013-839},
timestamp = {Tue, 18 Jun 2013 15:03:39 +0200},
biburl = {http://dblp.uni-trier.de/rec/bib/journals/fuin/AlbertiGL13},
bibsource = {dblp computer science bibliography, http://dblp.org}
}
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