Project management exercises

Exercises and solutions in LaTex

Project management exercises pdf

Exercises and solutions in PDF

MA230exam99

Exam questions and solutions in LaTex

MA230exam98pdf

Exam questions and solutions in PDF

MA230exam01

Exam questions and solutions in LaTex

Risk, ambiguity, and diversification

Attitudes toward risk influence the decision to diversify among uncertain options. Yet, because in most situations the options are ambiguous, attitudes toward ambiguity may also play an important role. I conduct a laboratory experiment to investigate the effect of ambiguity on the decision to diversify. I find that diversification is more prevalent and more persistent under ambiguity than under risk. Moreover, excess diversification under ambiguity is driven by participants who stick with a status quo gamble when diversification among gambles is not feasible. This behavioral pattern cannot be accommodated by major theories of choice under ambiguity.

El risc din��mic: concepte, mesura i determinants econ��mics

Aquesta tesi t�� la intenci�� de realitzar una contribuci�� metodol��gica en el camp de la direcci�� estrat��gica, per mitj�� de tres objectius: la revisi�� del concepte de risc ex post o realitzat per l'��mbit de la direcci�� estrat��gica; la concreci�� d'aquest concepte en una mesura de risc v��lida; i l'exploraci�� de les possibilitats i l'inter��s de la descomposici�� del risc en diferents determinants que puguin explicar-ne la seva naturalesa. El primer objectiu es du a terme prenent com a base el concepte intu��tiu de risc i revisant la literatura en els camps m��s afins, especialment en la teoria comportamental de la decisi�� i la direcci�� estrat��gica. L'an��lisi porta a formular el risc ex post d'una activitat com el grau en qu�� no s'han assolit els objectius per a aquesta activitat. La concreci�� d'aquesta definici�� al camp de la direcci�� estrat��gica implica que els objectius han de portar a l'obtenci�� de l'avantatge competitiu sostenible, el que descobreix l'inter��s de realitzar la mesura del risc a curt termini, ��s a dir, est��ticament, i a llarg termini, ��s a dir, din��micament, pel que es defineix una mesura de Risc Est��tic i una altra de Risc din��mic, respectivament. En l'an��lisi apareixen quatre dimensions conceptuals b��siques a incorporar en les mesures: sign dependence, relativa, longitudinal i path dependence. Addicionalment, la consideraci�� de que els resultats puguin ser cardinals o ordinals justifica que es formulin les dues mesures anteriors per a resultats cardinals i, en segon lloc, per a resultats ordinals. Les mesures de risc que es proposen sintetitzen els resultats ex post obtinguts en una mesura de centralitat relativa dels resultats, el Risc Est��tic, i una mesura de la tend��ncia temporal dels resultats, el Risc Din��mic. Aquesta proposta contrasta amb el plantejament tradicional dels models esperan��a-vari��ncia. Les mesures desenvolupades s'avaluen amb un sistema de propietats conceptuals i t��cniques que s'elaboren expressament en la tesi i que permeten demostrar el seu gra de validesa i el de les mesures existents en la literatura, destacant els problemes de validesa d'aquestes darreres. Tamb�� es proporciona un exemple te��ric il��lustratiu de les mesures proposades que d��na suport a l'avaluaci�� realitzada amb el sistema de propietats. Una contribuci�� destacada d'aquesta tesi ��s la demostraci�� de que les mesures de risc proposades permeten la descomposici�� additiva del risc si els resultats o diferencials de resultats es descomponen additivament. Finalment, la tesi inclou una aplicaci�� de les mesures de Risc Est��tic i Din��mic cardinals, aix�� com de la seva descomposici��, a l'an��lisi de la rendibilitat del sector bancari espanyol, en el per��ode 1987-1999. L'aplicaci�� il��lustra la capacitat de les mesures proposades per a analitzar la manifestaci�� de l'avantatge competitiu, la seva evoluci�� i naturalesa econ��mica. En les conclusions es formulen possibles l��nees d'investigaci�� futures.

Judgments, forecasts and decisions: an analysis of fund managers over time

Decision theory is the study of models of judgement involved in, and leading to, deliberate and (usually) rational choice. In real estate investment there are normative models for the allocation of assets. These asset allocation models suggest an optimum allocation between the respective asset classes based on the investors��� judgements of performance and risk. Real estate is selected, as other assets, on the basis of some criteria, e.g. commonly its marginal contribution to the production of a mean variance efficient multi asset portfolio, subject to the investor���s objectives and capital rationing constraints. However, decisions are made relative to current expectations and current business constraints. Whilst a decision maker may believe in the required optimum exposure levels as dictated by an asset allocation model, the final decision may/will be influenced by factors outside the parameters of the mathematical model. This paper discusses investors' perceptions and attitudes toward real estate and highlights the important difference between theoretical exposure levels and pragmatic business considerations. It develops a model to identify ���soft��� parameters in decision making which will influence the optimal allocation for that asset class. This ���soft��� information may relate to behavioural issues such as the tendency to mirror competitors; a desire to meet weight of money objectives; a desire to retain the status quo and many other non-financial considerations. The paper aims to establish the place of property in multi asset portfolios in the UK and examine the asset allocation process in practice, with a view to understanding the decision making process and to look at investors��� perceptions based on an historic analysis of market expectation; a comparison with historic data and an analysis of actual performance.

Modeling intelligent agents for web-based information gathering

The recent emergence of intelligent agent technology and advances in information gathering have been the important steps forward in efficiently managing and using the vast amount of information now available on the Web to make informed decisions. There are, however, still many problems that need to be overcome in the information gathering research arena to enable the delivery of relevant information required by end users. Good decisions cannot be made without sufficient, timely, and correct information. Traditionally it is said that knowledge is power, however, nowadays sufficient, timely, and correct information is power. So gathering relevant information to meet user information needs is the crucial step for making good decisions. The ideal goal of information gathering is to obtain only the information that users need (no more and no less). However, the volume of information available, diversity formats of information, uncertainties of information, and distributed locations of information (e.g. World Wide Web) hinder the process of gathering the right information to meet the user needs. Specifically, two fundamental issues in regard to efficiency of information gathering are mismatch and overload. The mismatch means some information that meets user needs has not been gathered (or missed out), whereas, the overload means some gathered information is not what users need. Traditional information retrieval has been developed well in the past twenty years. The introduction of the Web has changed people's perceptions of information retrieval. Usually, the task of information retrieval is considered to have the function of leading the user to those documents that are relevant to his/her information needs. The similar function in information retrieval is to filter out the irrelevant documents (or called information filtering). Research into traditional information retrieval has provided many retrieval models and techniques to represent documents and queries. Nowadays, information is becoming highly distributed, and increasingly difficult to gather. On the other hand, people have found a lot of uncertainties that are contained in the user information needs. These motivate the need for research in agent-based information gathering. Agent-based information systems arise at this moment. In these kinds of systems, intelligent agents will get commitments from their users and act on the users behalf to gather the required information. They can easily retrieve the relevant information from highly distributed uncertain environments because of their merits of intelligent, autonomy and distribution. The current research for agent-based information gathering systems is divided into single agent gathering systems, and multi-agent gathering systems. In both research areas, there are still open problems to be solved so that agent-based information gathering systems can retrieve the uncertain information more effectively from the highly distributed environments. The aim of this thesis is to research the theoretical framework for intelligent agents to gather information from the Web. This research integrates the areas of information retrieval and intelligent agents. The specific research areas in this thesis are the development of an information filtering model for single agent systems, and the development of a dynamic belief model for information fusion for multi-agent systems. The research results are also supported by the construction of real information gathering agents (e.g., Job Agent) for the Internet to help users to gather useful information stored in Web sites. In such a framework, information gathering agents have abilities to describe (or learn) the user information needs, and act like users to retrieve, filter, and/or fuse the information. A rough set based information filtering model is developed to address the problem of overload. The new approach allows users to describe their information needs on user concept spaces rather than on document spaces, and it views a user information need as a rough set over the document space. The rough set decision theory is used to classify new documents into three regions: positive region, boundary region, and negative region. Two experiments are presented to verify this model, and it shows that the rough set based model provides an efficient approach to the overload problem. In this research, a dynamic belief model for information fusion in multi-agent environments is also developed. This model has a polynomial time complexity, and it has been proven that the fusion results are belief (mass) functions. By using this model, a collection fusion algorithm for information gathering agents is presented. The difficult problem for this research is the case where collections may be used by more than one agent. This algorithm, however, uses the technique of cooperation between agents, and provides a solution for this difficult problem in distributed information retrieval systems. This thesis presents the solutions to the theoretical problems in agent-based information gathering systems, including information filtering models, agent belief modeling, and collection fusions. It also presents solutions to some of the technical problems in agent-based information systems, such as document classification, the architecture for agent-based information gathering systems, and the decision in multiple agent environments. Such kinds of information gathering agents will gather relevant information from highly distributed uncertain environments.

Evaluation of Bayesian belief networks for diagnosis of psychotic disorders

The thesis examined the inter-rater reliability and procedural validity of four computerised Bayesian belief networks (BBNs) which were developed to assist with the diagnosis of psychotic disorders. The results of this research indicated that BBNs can significantly improve diagnostic reliability and may represent an important advance over current diagnostic methods. The professional portfolio investigated, through the presentation of case studies and review of literature relevant to each case study, how comorbidity and context of depression may impact on cognitive behavioural therapy treatment.

Policy recognition in the Abstract Hidden Markov Model

In this paper, we present a method for recognising an agent's behaviour in dynamic, noisy, uncertain domains, and across multiple levels of abstraction. We term this problem on-line plan recognition under uncertainty and view it generally as probabilistic inference on the stochastic process representing the execution of the agent's plan. Our contributions in this paper are twofold. In terms of probabilistic inference, we introduce the Abstract Hidden Markov Model (AHMM), a novel type of stochastic processes, provide its dynamic Bayesian network (DBN) structure and analyse the properties of this network. We then describe an application of the Rao-Blackwellised Particle Filter to the AHMM which allows us to construct an efficient, hybrid inference method for this model. In terms of plan recognition, we propose a novel plan recognition framework based on the AHMM as the plan execution model. The Rao-Blackwellised hybrid inference for AHMM can take advantage of the independence properties inherent in a model of plan execution, leading to an algorithm for online probabilistic plan recognition that scales well with the number of levels in the plan hierarchy. This illustrates that while stochastic models for plan execution can be complex, they exhibit special structures which, if exploited, can lead to efficient plan recognition algorithms. We demonstrate the usefulness of the AHMM framework via a behaviour recognition system in a complex spatial environment using distributed video surveillance data.

O or��amento como instrumento de planejamento, execu����o e controle: estudo de caso do plano diretor da Marinha do Brasil

O objetivo deste estudo �� verificar o Plano Diretor, instrumento de planejamento, execu����o e controle das a����es desenvolvidas pela Marinha do Brasil, est�� adequado �� ambi��ncia vislumbrada para o s��culo XXI. De forma sist��mica, �� constru��do o referencial te��rico sobre assuntos pertinentes ao problema a ser investigado, tais como Teoria da decis��o, Teoria or��ament��ria, gest��o estrat��gica e modelos te��ricos de sistemas de planejamento e controle. O resultado da an��lise das caracter��sticas do sistema e do processo do Plano Diretor indica haver pontos pass��veis de serem aperfei��oados. Ao final, �� oferecido um resumo de sugest��es de medidas para tornar o Plano Diretor um instrumento up-to-date de gest��o or��ament��ria.