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A decision support tool coupling a causal model and a multi-objective genetic algorithm

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Abstract

A significant class of decision making problems consists of choosing actions, to be carried out simultaneously, in order to achieve a trade-off between different objectives. When such decisions concern complex systems, decision support tools including formal methods of reasoning and probabilistic models are of noteworthy helpfulness. These models are often built through learning procedures, based on an available knowledge base. Nevertheless, in many fields of application (e.g. when dealing with complex political, economic and social systems), it is frequently not possible to determine the model automatically, and this must then largely be derived from the opinions and value judgements expressed by domain experts. The BayMODE decision support tool (Bayesian Multi Objective Decision Environment), which we describe in this paper, operates precisely in such contexts. The principal component of the program is a multi-objective Decision Network, where actions are executed simultaneously. If the noisy-OR assumptions are applicable, such a the model has a reasonably small number of parameters, even when actions are represented as non-binary variables. This makes the model building procedure accessible and easy. Moreover, BayMODE operates with a multi-objective approach, which provides the decision maker with a set of non-dominated solutions, computed using a multi-objective genetic algorithm.

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Authors and Affiliations

  1. Department of Architecture and Planning, University of Sassari, Palazzo del Pou Salit, Piazza Duomo,6, I07041, Alghero (SS), Italy

    Ivan Blecic, Arnaldo Cecchini & Giuseppe A. Trunfio

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  1. Ivan Blecic
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  2. Arnaldo Cecchini
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  3. Giuseppe A. Trunfio
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Correspondence to Giuseppe A. Trunfio.

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Ivan Blecic is Assistant Professor of Economic Appraisal and Evaluation at the Faculty of Architecture in Alghero (University of Sassari, Italy) and member of Interuniversity Laboratory of Analysis and Models for Planning (LAMP). He received a Ph.D. in Planning and Public Policies in 2005 from IUAV University of Venice where he has also been a research fellow at the Department of Planning. His current research interests include analysis and modelling for planning, evaluation techniques and modelling, decision support systems and methods for public participation.

Arnaldo Cecchini graduated cum laude in Physics at the University of Bologna in 1972. He is Professor of Analysis of Urban Systems at the Faculty of Architecture in Alghero (University of Sassari), Director of the Urban and Environmental Planning Course, Vice-Dean of the Faculty of Architecture in Alghero and Director of the Interuniversity Laboratory of Analysis and Models for Planning - LAMP. He is the author of more than 100 articles and papers published in books and refereed journals and is an expert in techniques of urban analysis and for public participation: simulation, gaming simulation, cellular automata, scenario techniques.

Giuseppe A. Trunfio gained a Ph.D. in Computational Mechanics in 1999 at the University of Calabria, Italy. He has been a research fellow at the Italian National Research Council where he has worked extensively on the application of parallel computing to the simulation of complex systems. He is Assistant Professor of Computer Engineering at the Department of Architecture and Planning of the University of Sassari and his current research interests include decision support, probabilistic models, neural networks, evolutionary computation and cellular automata.

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Blecic, I., Cecchini, A. & Trunfio, G.A. A decision support tool coupling a causal model and a multi-objective genetic algorithm. Appl Intell 26, 125–137 (2007). https://doi.org/10.1007/s10489-006-0009-z

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