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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

O processo decisório estratégico de adaptação de competências organizacionais

Figueiredo, Viviane Marchioni 13 April 2010 (has links)
Made available in DSpace on 2016-03-15T19:26:51Z (GMT). No. of bitstreams: 1 Viviane Marchioni Figueiredo.pdf: 1273367 bytes, checksum: 2725a17c82654baeade687b477e1e239 (MD5) Previous issue date: 2010-04-13 / Fundo Mackenzie de Pesquisa / Competitive advantage may be reached in response to environment movements through constant adjustments and adaptations of the organizations in competitive environments, demanding an update capability that raises the flexibility necessary to the organization. In these conditions it is possible to note that organizations adopt certain tactics of alternatives choice, which relate to the need to adapt those skills that keep them competitive, showing complexity of decision making in organizations. Then, appears the question of how complexity of strategic decision making for adaptation of organizational competencies is related to alternatives choice tactics in the Brazilian pharmaceutical industry, segment of human health. To answer this question, we chose to study the decision-making process of adaptation of organizational skills in the Brazilian pharmaceutical industry. The methodology of multiple case studies is used in this study, composed by three international pharmaceutical companies operating in Brazil with drug research and development. The relevance of the research is linked to the understanding and improved knowledge of the complexity of strategic decision making, specially the phase of evaluating alternatives. Also with raising standards of tactics of choice in the segment and build knowledge that can guide the conduct of decisions with less risk and less time. / Em ambientes competitivos a garantia de vantagem competitiva pode estar na resposta a movimentos do ambiente através de adaptações e ajustes constantes das organizações, demandando uma capacidade de atualização que eleva a flexibilidade necessária à organização. Nessas condições é possível notar que as organizações adotam determinadas táticas de escolhas de alternativas, que, por sua vez, relacionam-se a essa necessidade de adaptação de competências que as mantêm competitivas, denotando complexidade aos processos decisórios nas organizações. Coloca-se então a questão de como a complexidade do processo decisório estratégico de adaptação de competências organizacionais influencia as táticas de escolha de alternativas na indústria farmacêutica brasileira, segmento de saúde humana. Para responder a essa questão, optou-se por estudar o processo decisório de adaptação de competências organizacionais na indústria farmacêutica brasileira. Para a realização deste estudo foi utilizada a metodologia de estudos de caso múltiplos, composto por três laboratórios farmacêuticos internacionais atuantes no Brasil e que desenvolvem pesquisa e desenvolvimento de medicamentos. A relevância da pesquisa vincula-se à compreensão e ao aprofundamento do conhecimento sobre a complexidade do processo decisório estratégico, em especial da fase de avaliação de alternativas. Também com o levantamento de padrões de táticas de escolha no segmento e construir conhecimentos que possam direcionar a condução de decisões, com menos riscos e em menor tempo.
2

Complexity-aware Decision-making with Applications to Large-scale and Human-in-the-loop Systems

Stefansson, Elis January 2023 (has links)
This thesis considers control systems governed by autonomous decision-makers and humans. We formalise and compute low-complex control policies with applications to large-scale systems, and propose human interaction models for controllers to compute interaction-aware decisions. In the first part of the thesis, we consider complexity-aware decision-making, formalising the complexity of control policies and constructing algorithms that compute low-complexity control policies. More precisely, first, we consider large-scale control systems given by hierarchical finite state machines (HFSMs) and present a planning algorithm for such systems that exploits the hierarchy to compute optimal policies efficiently. The algorithm can also handle changes in the system with ease. We prove these properties and conduct simulations on HFSMs with up to 2 million states, including a robot application, where our algorithm outperforms both Dijkstra's algorithm and Contraction Hierarchies.  Second, we present a planning objective for control systems modelled as finite state machines yielding an explicit trade-off between a policy's performance and complexity. We consider Kolmogorov complexity since it captures the ultimate compression of an object on a universal Turing machine. We prove that this trade-off is hard to optimise in the sense that dynamic programming is infeasible. Nonetheless, we present two heuristic algorithms obtaining low-complexity policies and evaluate the algorithms on a simple navigation task for a mobile robot, where we obtain low-complexity policies that concur with intuition.  In the second part of the thesis, we consider human-in-the-loop systems and predict human decision-making in such systems. First, we look at how the interaction between a robot and a human in a control system can be predicted using game theory, focusing on an autonomous truck platoon interacting with a human-driven car. The interaction is modelled as a hierarchical dynamic game, where the hierarchical decomposition is temporal with a high-fidelity tactical horizon predicting immediate interactions and a low-fidelity strategic horizon estimating long-term behaviour. The game enables feasible computations validated through simulations yielding situation-aware behaviour with natural and safe interactions.  Second, we seek models to explain human decision-making, focusing on driver overtaking scenarios. The overtaking problem is formalised as a decision problem with perceptual uncertainty. We propose and numerically analyse risk-agnostic and risk-aware decision models, judging if an overtaking is desirable. We show how a driver's decision time and confidence level can be characterised through two model parameters, which collectively represent human risk-taking behaviour. We detail an experimental testbed for evaluating the decision-making process in the overtaking scenario and present some preliminary experimental results from two human drivers. / Denna avhandling studerar styrsystem med autonoma beslutsfattare och människor. Vi formaliserar och beräknar styrlagar av låg komplexitet med tillämpningar på storskaliga system samt föreslår modeller för mänsklig interaktion som kan användas av regulatorer för att beräkna interaktionsmedvetna beslut. I den första delen av denna avhandling studerar vi komplexitet-medveten beslutsfattning, där vi formaliserar styrlagars komplexitet samt konstruerar algoritmer som beräknar styrlagar med låg komplexitet. Mer precist, först studerar vi storskaliga system givna av hierarkiska finita tillståndsmaskiner (HFSMs) och presenterar en planeringsalgoritm för sådana system som utnyttjar hierarkin för att beräkna optimala styrlagar effektivt. Algoritmen kan också lätt hantera förändringar i systemet. Vi bevisar dessa egenskaper och utför simuleringar på HFSMs med upp till 2 miljoner tillstånd, inklusive en robot-applikation, där vår algorithm överträffar både Dijkstra's algoritm och så kallade Contraction Hierarchies. För det andra så presenterar vi ett planeringsobjektiv för finita tillståndsmaskiner som ger en explicit avvägning mellan ett styrlags prestanda och komplexitet. Vi använder Kolmogorovkomplexitet då den fångar den ultimata komprimeringen av ett objekt i en universell Turing-maskin. Vi bevisar att detta objektiv är icke-trivial att optimera över i avseendet att dynamisk programming är omöjligt att utföra. Vi presenterar två algoritmer som beräknar styrlagar med låg komplexitet och evaluerar våra algoritmer på ett enkelt navigationsproblem där vi erhåller styrlagar av låg komplexitet som instämmer med intuition. I den andra delen av denna avhandling behandlar vi reglersystem där en människa interagerar med systemet och studerar hur mänskligt beslutsfattande i sådana system kan förutspås. Först studerar vi hur interaktionen mellan en maskin och en människa i ett reglersystem can förutspås med hjälp av spelteori, med fokus på en självkörande lastbilskonvoj som interagerar med en mänskligt styrd bil. Interaktionen är modellerad som ett hierarkiskt dynamiskt spel, där den hierarkiska indelningen är tidsmässig med en högupplöst taktil horisont som förutspår omedelbara interaktioner samt en lågupplöst strategisk horisont som estimerar långtgående interaktioner. Indelning möjliggör beräkningar som vi validerar via simuleringar där vi får situations-medvetet beteende med naturliga och säkra interaktioner. För det andra söker vi en model med få parametrar som förklarar mänskligt beteende där vi fokuserar på omkörningar. Vi formaliserar omkörningsproblemet som ett beslutfattningsproblem med perceptuell osäkerhet. Vi presenterar och analyserar numeriskt risk-agnostiska och risk-medvetna beslutsmodeller som avväger om en omkörning är önskvärd. Vi visar hur en förares beslutstid och konfidensnivå kan karakteriserar via två modellparametrar som tillsammans representerar mänskligt risk-beteende. Vi beskriver en experimentell testbädd och presentar preliminära resultat från två mänskliga förare. / <p>QC 20230523</p>

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