Cette thèse porte sur l’aide à la décision dans un contexte décisionnel très complexe. Classiquement, pour résoudre de telles situations, on utilise des méthodes de structuration de problèmes. Cependant ces méthodes bien qu’appliquées dans le cadre multi acteur ou dans les décisions de groupe, n’aboutissent pas toujours à des résultats directement exploitables dans un modèle d’évaluation. Ou, lorsque c’est le cas, les données obtenues par structuration sont utilisées comme si elles provenaient d’un seul décideur, tendant à réduire par conséquent l'efficacité de la décision prise et son adhésion publique. Dans cette thèse nous nous sommes attelés à concevoir un modèle d’intégration d’outils conciliant le choix approprié d’outils de structuration pour les décisions de groupe et son exploitation efficace dans un modèle d’évaluation multicritère. En particulier nous nous sommes focalisés sur les modalités du passage des cartes cognitives aux arbres de valeurs. Ensuite nous avons appliqué notre démarche sur le cas pratique du projet ’’AIde à la Décision pour l'identification et l'accompagnement aux transformations sociétales induites par les nouvelles technologies de l'Hydrogène’’ (AIDHY). Enfin, la dernière partie de notre thèse est axée sur l’apport d’une modélisation multicritère pour appréhender formellement le problème d’évaluation des scénarios, formulé comme un problème de tri multicritère. Par conséquent, nous avons construit une méthode permettant d’observer et de paramétrer le comportement des invariants d’une acceptabilité sociale en général, par le biais d’une d’analyse de sensibilité à partir du cas de l’hydrogène énergie. / This thesis focuses on decision support in a very complex decision-making context. Typically, to solve such situations, methods of problem structuring are used. However, these methods although applied in the multi-stakeholder framework or group decisions do not always lead to results directly used in a valuation model. Even when this is the case, the data obtained by problem structuring are used as if they came from a single decision maker, thus tending to reduce the effectiveness of the decision and its popular support. In this thesis we attempted to develop a model that incorporates tools that reconcile the appropriate choice of tools for structuring group decision choice and its effective operation in a model of multi-criteria evaluation. In particular, we focused on how processing cognitive maps into value trees. Then we have applied our approach to the practical case of the ‘‘AIDHY” project. Finally, the last part of the thesis is focused on providing a multi-criteria modeling to formally approach the problem of evaluating scenarios, formulated as a multi-criteria sorting problem. Therefore, we constructed a method to observe and configure the behavior of invariants of social acceptability in general, through a sensitivity analysis based on the case of hydrogen energy.

The inability to navigate independently is one of the most significant handicaps due by blindness. Many difficulties can be faced when visually impaired people (VIP) need to visit new and unknown places. Our approach is to use locomotion simulation interface to allow a natural exploration way of virtual environment (VE) to assist VIP to create cognitive maps efficiently, and thereby to enhance their mobility skill. A computer system, called SACEC Assist System Spatial Cognition in the Blind, was developed using virtual reality techniques and natural interaction. The system allows the modeling of two-dimensional or three-dimensional virtual environments, and it is possible to reproduce actual environments such as schools, universities and others points of interest. The interaction with the virtual environment is based on the recognition of poses, which are interpreted as actions such as walking, turning, or front and side touch, and feedback sounds are sent to user. In order to review and validation of the developed system and the proposed interface tests are performed with actual VIPs, men and women aged from 18 to 60 years. A result shows the effectiveness of the SACEC in developing cognitive map of the simulated environment, as well the positive feedback shows that the system can be entertaining and pleasant to the participants. Therefore, SACEC has a great potential to be used in aid to Orientation and Mobility (OM) trainings, and thus contribute to greater social inclusion of those people. / A impossibilidade de navegar de forma independente é uma das desvantagens mais significativas que podem ser causadas pela cegueira. Muitas dificuldades são encontradas quando pessoas com deficiência visual (PDVs) precisam visitar lugares novos e desconhecidos. Este trabalho propõe o uso de uma interface de simulação de locomoção que permita uma forma natural de exploração de Ambientes Virtuais (AV), para propiciar aos deficientes visuais a criação de mapas cognitivos de forma eficiente e, assim, auxiliar a sua mobilidade. Foi desenvolvido um sistema computacional, denominado SACEC - Sistema de Auxílio da Cognição Espacial em Cegos, que utiliza técnicas de Realidade Virtual e Interação Natural. O sistema permite a modelagem de Ambientes Virtuais bidimensionais ou tridimensionais. É possível reproduzir ambientes reais como escolas, universidades e outros espaços de interesse. A interação com o Ambiente Virtual ocorre por meio do reconhecimento de poses, que são interpretadas como ações de andar, girar ou tatear, e com respostas sonoras. Foram realizados testes com 20 PDVs, homens e mulheres com idades entre 18 e 60 anos, para avaliação e validação do sistema desenvolvido, bem como da interface proposta. Os resultados obtidos demonstraram a efetividade do SACEC, assim como avaliação positiva de todos os participantes que conseguiram criar o mapa cognitivo do ambiente navegado e reproduzí-lo verbalmente e em blocos montáveis. Desta forma, pode ser indicado no auxílio do trabalho de Orientação e Mobilidade (OM), e consequentemente contribuir com maior inclusão social dessas pessoas. / Doutor em Ciências

Αντικείµενο της διατριβής είναι η ανάπτυξη νέων µεθοδολογιών εκµάθησης και σύγκλισης των Ασαφών Γνωστικών ∆ικτύων που προτείνονται για τη βελτίωση και προσαρµογή της συµπεριφοράς τους, καθώς και για την αύξηση της απόδοσής τους, αναδεικνύοντάς τα σε αποτελεσµατικά δυναµικά συστήµατα µοντελοποίησης. Τα νέα βελτιωµένα Ασαφή Γνωστικά ∆ίκτυα, µέσω της εκµάθησης και προσαρµογής των βαρών τους, έχουν χρησιµοποιηθεί στην ιατρική σε θέµατα διάγνωσης και υποστήριξης στη λήψη απόφασης, καθώς και σε µοντέλα βιοµηχανικών συστηµάτων που αφορούν τον έλεγχο διαδικασιών, µε πολύ ικανοποιητικά αποτελέσµατα. Στη διατριβή αυτή παρουσιάζονται, αξιολογούνται και εφαρµόζονται δύο νέοι αλγόριθµοι εκµάθησης χωρίς επίβλεψη των Ασαφών Γνωστικών ∆ικτύων, οι αλγόριθµοι Active Hebbian Learning (AHL) και Nonlinear Hebbian Learning (NHL), βασισµένοι στον κλασσικό αλγόριθµό εκµάθησης χωρίς επίβλεψη τύπου Hebb των νευρωνικών δικτύων, καθώς και µια νέα προσέγγιση εκµάθησης των Ασαφών Γνωστικών ∆ικτύων βασισµένη στους εξελικτικούς αλγορίθµους και πιο συγκεκριµένα στον αλγόριθµο Βελτιστοποίησης µε Σµήνος Σωµατιδίων και στον ∆ιαφοροεξελικτικό αλγόριθµο. Οι προτεινόµενοι αλγόριθµοι AHL και NHL στηρίζουν νέες µεθοδολογίες εκµάθησης για τα ΑΓ∆ που βελτιώνουν τη λειτουργία, και την αξιοπιστία τους, και που παρέχουν στους εµπειρογνώµονες του εκάστοτε προβλήµατος που αναπτύσσουν το ΑΓ∆, την εκµάθηση των παραµέτρων για τη ρύθµιση των αιτιατών διασυνδέσεων µεταξύ των κόµβων. Αυτοί οι τύποι εκµάθησης που συνοδεύονται από την σωστή γνώση του εκάστοτε προβλήµατος-συστήµατος, συµβάλλουν στην αύξηση της απόδοσης των ΑΓ∆ και διευρύνουν τη χρήση τους. Επιπρόσθετα µε τους αλγορίθµους εκµάθησης χωρίς επίβλεψη τύπου Hebb για τα ΑΓ∆, αναπτύσσονται και προτείνονται νέες τεχνικές εκµάθησης των ΑΓ∆ βασισµένες στους εξελικτικούς αλγορίθµους. Πιο συγκεκριµένα, προτείνεται µια νέα µεθοδολογία για την εφαρµογή του εξελικτικού αλγορίθµου Βελτιστοποίησης µε Σµήνος Σωµατιδίων στην εκµάθηση των Ασαφών Γνωστικών ∆ικτύων και πιο συγκεκριµένα στον καθορισµό των βέλτιστων περιοχών τιµών των βαρών των Ασαφών Γνωστικών ∆ικτύων. Με τη µεθοδο αυτή λαµβάνεται υπόψη η γνώση των εµπειρογνωµόνων για τον σχεδιασµό του µοντέλου µε τη µορφή περιορισµών στους κόµβους που µας ενδιαφέρουν οι τιµές των καταστάσεών τους, που έχουν οριστοί ως κόµβοι έξοδοι του συστήµατος, και για τα βάρη λαµβάνονται υπόψη οι περιοχές των ασαφών συνόλων που έχουν συµφωνήσει όλοι οι εµπειρογνώµονες. Έτσι θέτoντας περιορισµούς σε όλα τα βάρη και στους κόµβους εξόδου και καθορίζοντας µια κατάλληλη αντικειµενική συνάρτηση για το εκάστοτε πρόβληµα, προκύπτουν κατάλληλοι πίνακες βαρών (appropriate weight matrices) που µπορούν να οδηγήσουν το σύστηµα σε επιθυµητές περιοχές λειτουργίας και ταυτόχρονα να ικανοποιούν τις ειδικές συνθήκες- περιορισµούς του προβλήµατος. Οι δύο νέες µέθοδοι εκµάθησης χωρίς επίβλεψη που έχουν προταθεί για τα ΑΓ∆ χρησιµοποιούνται και εφαρµόζονται µε επιτυχία σε δυο πολύπλοκα προβλήµατα από το χώρο της ιατρικής, στο πρόβληµα λήψης απόφασης στην ακτινοθεραπεία και στο πρόβληµα κατηγοριοποίησης των καρκινικών όγκων της ουροδόχου κύστης σε πραγµατικές κλινικές περιπτώσεις. Επίσης όλοι οι προτεινόµενοι αλγόριθµοι εφαρµόζονται σε µοντέλα βιοµηχανικών συστηµάτων που αφορούν τον έλεγχο διαδικασιών µε πολύ ικανοποιητικά αποτελέσµατα. Οι αλγόριθµοι αυτοί, όπως προκύπτει από την εφαρµογή τους σε συγκεκριµένα προβλήµατα, βελτιώνουν το µοντέλο του ΑΓ∆, συµβάλλουν σε ευφυέστερα συστήµατα και διευρύνουν τη δυνατότητα εφαρµογής τους σε πραγµατικά και πολύπλοκα προβλήµατα. Η κύρια συνεισφορά αυτής της διατριβής είναι η ανάπτυξη νέων µεθοδολογιών εκµάθησης και σύγκλισης των Ασαφών Γνωστικών ∆ικτύων προτείνοντας δυο νέους αλγορίθµους µη επιβλεπόµενης µάθησης τύπου Hebb, τον αλγόριθµο Active Hebbian Learning και τον αλγόριθµο Nonlinear Hebbian Learning για την προσαρµογή των βαρών των διασυνδέσεων µεταξύ των κόµβων των Ασαφών Γνωστικών ∆ικτύων, καθώς και εξελικτικούς αλγορίθµους βελτιστοποιώντας συγκεκριµένες αντικειµενικές συναρτήσεις για κάθε εξεταζόµενο πρόβληµα. Τα νέα βελτιωµένα Ασαφή Γνωστικά ∆ίκτυα µέσω των αλγορίθµων προσαρµογής των βαρών τους έχουν χρησιµοποιηθεί για την ανάπτυξη ενός ∆ιεπίπεδου Ιεραρχικού Συστήµατος για την υποστήριξη λήψης απόφασης στην ακτινοθεραπεία, για την ανάπτυξη ενός διαγνωστικού εργαλείου για την κατηγοριοποίηση του βαθµού κακοήθειας των καρκινικών όγκων της ουροδόχου κύστης, καθώς και για την επίλυση βιοµηχανικών προβληµάτων για τον έλεγχο διαδικασιών. / The main contribution of this Dissertation is the development of new learning and convergence methodologies for Fuzzy Cognitive Maps that are proposed for the improvement and adaptation of their behaviour, as well as for the increase of their performance, electing them in effective dynamic systems of modelling. The new improved Fuzzy Cognitive Maps, via the learning and adaptation of their weights, have been used in medicine for diagnosis and decision-making, as well as to alleviate the problem of the potential uncontrollable convergence to undesired states in models of industrial process control systems, with very satisfactory results. In this Dissertation are presented, validated and implemented two new learning algorithms without supervision for Fuzzy Cognitive Maps, the algorithms Active Hebbian Learning (AHL) and Nonlinear Hebbian Learning (NHL), based on the classic unsupervised Hebb-type learning algorithm of neural networks, as well as a new approach of learning for Fuzzy Cognitive Maps based on the evolutionary algorithms and more specifically on the algorithm of Particles Swarm Optimization and on the Differential Evolution algorithm. The proposed algorithms AHL and NHL support new learning methodologies for FCMs that improve their operation, efficiency and reliability, and that provide in the experts of each problem that develop the FCM, the learning of parameters for the regulation (fine-tuning) of cause-effect relationships (weights) between the concepts. These types of learning that are accompanied with the right knowledge of each problem-system, contribute in the increase of performance of FCMs and extend their use. Additionally to the unsupervised learning algorithms of Hebb-type for the FCMs, are developed and proposed new learning techniques of FCMs based on the evolutionary algorithms. More specifically, it is proposed a new learning methodology for the application of evolutionary algorithm of Particle Swarm Optimisation in the adaptation of FCMs and more concretely in the determination of the optimal regions of weight values of FCMs. With this method it is taken into consideration the experts’ knowledge for the modelling with the form of restrictions in the concepts that interest us their values, and are defined as output concepts, and for weights are received the arithmetic values of the fuzzy regions that have agreed all the experts. Thus considering restrictions in all weights and in the output concepts and determining a suitable objective function for each problem, result appropriate weight matrices that can lead the system to desirable regions of operation and simultaneously satisfy specific conditions of problem. The first two proposed methods of unsupervised learning that have been suggested for the FCMs are used and applied with success in two complicated problems in medicine, in the problem of decision-making in the radiotherapy process and in the problem of tumor characterization for urinary bladder in real clinical cases. Also all the proposed algorithms are applied in models of industrial systems that concern the control of processes with very satisfactory results. These algorithms, as it results from their application in concrete problems, improve the model of FCMs, they contribute in more intelligent systems and they extend their possibility of application in real and complex problems. The main contribution of the present Dissertation is to develop new learning and convergence methodologies for Fuzzy Cognitive Maps proposing two new unsupervised learning algorithms, the algorithm Active Hebbian Learning and the algorithm Nonlinear Hebbian Learning for the adaptation of weights of the interconnections between the concepts of Fuzzy Cognitive Maps, as well as Evolutionary Algorithms optimizing concrete objective functions for each examined problem. New improved Fuzzy Cognitive Maps via the algorithms of weight adaptation have been used for the development of an Integrated Two-level hierarchical System for the support of decision-making in the radiotherapy, for the development of a new diagnostic tool for tumour characterization of urinary bladder, as well as for the solution of industrial process control problems.

[pt] A compreensão dos atuais processos produtivos é essencial neste momento em que o conhecimento tornou-se um importante gerador de valor. Uma visão holística dos conhecimentos que estão disseminados, de forma dispersa, entre profissionais, consultores e acadêmicos é necessária para a síntese de novas teorias da produção. Pesquisadores de gerência de operações freqüentemente usam mapeamento causal como um mecanismo para construir e comunicar teorias, particularmente em suporte à pesquisa empírica. As abordagens mais usuais para capturar dados cognitivos para um mapa causal são brainstorming e entrevistas, os quais exigem muito tempo e apresentam um significativo custo em sua implementação. Esta tese visa gerar uma metodologia (Metodologia Evocativa para Mapeamento Causal - ECMM) voltada para aplicação em pesquisa sobre gerência de operações para coletar e estruturar dados disseminados de forma desagregada, como conhecimento e experiência profissional e acadêmica, contidos nas opiniões de um grande número de especialistas dispersos demograficamente e geograficamente. Isto é alcançado evocando opiniões, codificando-as em variáveis e reduzindo o grupo em conceitos e relações. Tem-se uma especial preocupação em conseguir este objetivo em tempo factível e com baixo custo. A coleta de dados é assíncrona, via Internet, possui dois ou três turnos (à semelhança do método Delfos). A análise de dados usa codificação, técnica de grupamento hierárquica e escalamento multidimensional para identificar conceitos na forma de mapas cognitivos. A ECMM foi ilustrada com aplicações que demonstram sua viabilidade. Aplicou-se nas áreas de gestão da cadeia de suprimento (SCM) e administração de serviços (SM) com a participação de aproximadamente 1.300 respondentes de empresas e universidades de quase 100 países. Dentre os desdobramentos para pesquisas futuras propõe-se aplicar nas áreas de ECMM em SCM e SM visando a uni-las em um tema: gestão da cadeia de suprimento de serviços. / [en] The understanding of the present productive processes is essential at this moment when knowledge became an important value creator. A holistic vision of the pieces of knowledge that are spread out and dispersed among practitioners, consultants and academics is necessary for the synthesis of new theories of production. Operations management researchers often use causal mapping as a key tool for building and communicating theory, particularly in support of empirical research. The widely accepted approaches for capturing cognitive data for a causal map are informal brainstorming and interviews, which require a time- consuming and significant cost of implementation. This dissertation aims at creating a methodology (Evocative Causal Mapping Methodology - ECMM) intended for use in operations management research for collecting and structuring dispersed data spread out as practical and research knowledge, and experience contained in the opinions of a large number of specialists demographically and geographically scattered. This is accomplished by evoking opinions, encoding them into variables and reducing the resulting set to concepts and relationships. A special concern is to achieve this goal in a feasible time and cost- efficient way. ECMM consists of two or three round, Delphi- like, Internet-based asynchronous data collection, and a data analysis that uses a coding panel of experts, hierarchical cluster analysis and multidimensional scaling for identifying concepts on cognitive map formats. Applications illustrate ECMM and demonstrate its feasibility. They were developed on supply chain management (SCM) and service management (SM) involving about 1,300 respondents of companies and universities of about 100 countries. Among possible unfolding future studies, this dissertation proposes to apply ECMM in SCM and SM aiming at unifying them into a single topic: service supply chain management.

The purpose of this dissertation is to understand the managerial "action" of public administrators in the management of their organizations within the Brazilian context. The research seeks to understand the relationships between managers and formal management mechanisms by exploring the complementary nature of the effective managerial action in the face of structural deficiencies and flaws, considering the possibility of overcoming the structuralism-subjectivism dichotomy present in the construction of the Theory of Organizations. Initially, the study provides a review of the literature on organizational design. It highlights the "goodness of fit" proposition on strategic choice issues concerning the main organizational variables design and organizational goal attainment. It also calls special attention to the emerging interest of designing theorists on interpretivist approaches to the matter, such that of Karl Weick. A review of the the administrative reforms in Brazil is made from the perspective of the main stream organizational design conceptual framework. It highlights the complex dynamics of a constant search for differentiation and flexibilization subject to patterns of advances and reversals, due to the centrality, strength and pervasiveness of the bureaucratic model. It is concluded that in no single given moment, a public manager and his team, may count on a formal organizational design which attends the"congruency" criteria, devised by organizational design conceptual frameworks, to explain organizational results in different environmental sets. Although this conclusion may explain failure at the public sector, it can not provide understanding on the many instances of significative success attained by government operations in spite of inadequate formal administrative structures. This point calls for a better understanding from the interpretivist approach, on how public administrators, strongly associated with good organizational results, engage into transformative action, in order to superate administrative structures flaws and dysfunctional cultural patterns of conduct, structurally present and constantly reproduced, in vigorous developing countries, such as Brazil. The dissertation transcribes the testimony of four outstanding public administrators, doing a deep incursion in the managerial real world of public administration, as subjectively defined by them and transformed by their engagement into action.Through the thematic version of the Oral History methodology, full segments of the complete interviews are categorized into the thirty two managerial strategies captured which are presented on a recategorized manner under eight main strategies: (1) Interchanging Frames of Reference; (2) Exploring the Formal Limits; (3) Playing the Bureaucracy Game; (4) Inducing the Inclusion of Others (5)Promoting Internal Cohesion; (6) Creating Shields against Transgressions; (7) Overcoming Internal Restrictions; (8) Letting the Structures Blossom. Each one of these eight blocks of strategies presented, deserves further reflexive interpretation by the author, on the light of the interpretivist approach to organizational design. A final effort is made, now on theory building, for improving understanding on the matter. In order to find a significant meaning underlining all the strategies extracted from the "practical consciousness" of the interviewers as revealed in their report, the author resort to a metaphor. This metaphor helps to: (1) better describe and understand a not adequately treated phenomenon, namely, good results under inadequate structural social and organizational conditions; (2) reveal the logic and the meaning underlining all the strategies adopted to generate results under these unfaithful conditions; (3) name, accordingly to the nature of the managerial transformative social action involved, an open ended class of managerial interventions of a pragmatic sort driven by an ethics of results much common to good managers, that is, the concept of "managerial equalization"; and (4) give back to public administrators, represented by the interviewees, to be incorporated in their "discursive consciousness", something the most effective and experienced public managers already have as tacit knowledge built in their "practical consciousness", and so, help the education and development of new talents. / Ph. D.