A Lei de ZIPF ou Regra 80/20 da Internet

As Leis de Potência, LP, (Power Laws, em inglês), Leis de Pareto ou Leis de Zipf são distribuições estatísticas, com inúmeras aplicações práticas, em sistemas naturais e artificiais. Alguns exemplos são a variação dos rendimentos pessoais ou de empresas, a ocorrência de palavras em textos, as repetições de sons ou conjuntos de sons em composições musicais, o número de vítimas em guerras ou outros cataclismos, a magnitude de tremores de terra, o número de vendas de livros ou CD’s na internet, o número de sítios mais acedidos na Internet, entre muitos outros. Vilfredo Pareto (1897-1906) afirma, no manual de economia política “Cours d’Economie Politique”, que grande parte da economia mundial segue uma determinada distribuição, em que 20% da população reúne 80% da riqueza total do país, estando, assim uma pequena fração da sociedade a controlar a maior fatia do dinheiro. Isto resume o comportamento de uma variável que segue uma distribuição de Pareto (ou Lei de Potência). Neste trabalho pretende-se estudar em pormenor a aplicação das leis de potência a fenómenos da internet, como sendo o número de sítios mais visitados, o número de links existentes em determinado sítio, a distribuição de nós numa rede da internet, o número livros vendidos e as vendas em leilões online. Os resultados obtidos permitem-nos concluir que todos os dados estudados são bem aproximados, numa escala logarítmica, por uma reta com declive negativo, seguindo, assim, uma distribuição de Pareto. O desenvolvimento e crescimento da Web, tem proporcionado um aumento do número dos utilizadores, conteúdos e dos sítios. Grande parte dos exemplos presentes neste trabalho serão alvo de novos estudos e de novas conclusões. O fato da internet ter um papel preponderante nas sociedades modernas, faz com que esteja em constante evolução e cada vez mais seja possível apresentar fenómenos na internet associados Lei de Potência.

Share GFS – Games, Films & Series: sistema de recomendação

Esta dissertação incide sobre o estudo e análise de uma solução para a criação de um sistema de recomendação para uma comunidade de consumidores de media e no consequente desenvolvimento da mesma cujo âmbito inicial engloba consumidores de jogos, filmes e/ou séries, com o intuito de lhes proporcionar a oportunidade de partilharem experiências, bem como manterem um registo das mesmas. Com a informação adquirida, o sistema reúne condições para proceder a sugestões direcionadas a cada membro da comunidade. O sistema atualiza a sua informação mediante as ações e os dados fornecidos pelos membros, bem como pelo seu feedback às sugestões. Esta aprendizagem ao longo do tempo permite que as sugestões do sistema evoluam juntamente com a mudança de preferência dos membros ou se autocorrijam. O sistema toma iniciativa de sugerir mediante determinadas ações, mas também pode ser invocada uma sugestão diretamente pelo utilizador, na medida em que este não precisa de esperar por sugestões, podendo pedir ao sistema que as forneça num determinado momento. Nos testes realizados foi possível apurar que o sistema de recomendação desenvolvido forneceu sugestões adequadas a cada utilizador específico, tomando em linha de conta as suas ações prévias. Para além deste facto, o sistema não forneceu qualquer sugestão quando o histórico destas tinha provado incomodar o utilizador.

Self-scheduling and bidding strategies of thermal units with stochastic emission constraints

This paper is on the self-scheduling problem for a thermal power producer taking part in a pool-based electricity market as a price-taker, having bilateral contracts and emission-constrained. An approach based on stochastic mixed-integer linear programming approach is proposed for solving the self-scheduling problem. Uncertainty regarding electricity price is considered through a set of scenarios computed by simulation and scenario-reduction. Thermal units are modelled by variable costs, start-up costs and technical operating constraints, such as: forbidden operating zones, ramp up/down limits and minimum up/down time limits. A requirement on emission allowances to mitigate carbon footprint is modelled by a stochastic constraint. Supply functions for different emission allowance levels are accessed in order to establish the optimal bidding strategy. A case study is presented to illustrate the usefulness and the proficiency of the proposed approach in supporting biding strategies. (C) 2014 Elsevier Ltd. All rights reserved.

Multi-agent management system for electric vehicle charging

This paper proposes an implementation, based on a multi-agent system, of a management system for automated negotiation of electricity allocation for charging electric vehicles (EVs) and simulates its performance. The widespread existence of charging infrastructures capable of autonomous operation is recognised as a major driver towards the mass adoption of EVs by mobility consumers. Eventually, conflicting requirements from both power grid and EV owners require automated middleman aggregator agents to intermediate all operations, for example, bidding and negotiation, between these parts. Multi-agent systems are designed to provide distributed, modular, coordinated and collaborative management systems; therefore, they seem suitable to address the management of such complex charging infrastructures. Our solution consists in the implementation of virtual agents to be integrated into the management software of a charging infrastructure. We start by modelling the multi-agent architecture using a federated, hierarchical layers setup and as well as the agents' behaviours and interactions. Each of these layers comprises several components, for example, data bases, decision-making and auction mechanisms. The implementation of multi-agent platform and auctions rules, and of models for battery dynamics, is also addressed. Four scenarios were predefined to assess the management system performance under real usage conditions, considering different types of profiles for EVs owners', different infrastructure configurations and usage and different loads on the utility grid (where real data from the concession holder of the Portuguese electricity transmission grid is used). Simulations carried with the four scenarios validate the performance of the modelled system while complying with all the requirements. Although all of these have been performed for one charging station alone, a multi-agent design may in the future be used for the higher level problem of distributing energy among charging stations. Copyright (c) 2014 John Wiley & Sons, Ltd.

Strategic Bidding for Electricity Markets Negotiation Using Support Vector Machines

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which simulates the electricity markets environment. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated by being included in ALBidS and then compared with the application of an Artificial Neural Network, originating promising results. The proposed approach is tested and validated using real electricity markets data from MIBEL - Iberian market operator.

Strategic Bidding in Electricity Markets: An agent-based simulator with game theory for scenario analysis

Electricity markets are complex environments, involving a large number of different entities, with specific characteristics and objectives, making their decisions and interacting in a dynamic scene. Game-theory has been widely used to support decisions in competitive environments; therefore its application in electricity markets can prove to be a high potential tool. This paper proposes a new scenario analysis algorithm, which includes the application of game-theory, to evaluate and preview different scenarios and provide players with the ability to strategically react in order to exhibit the behavior that better fits their objectives. This model includes forecasts of competitor players’ actions, to build models of their behavior, in order to define the most probable expected scenarios. Once the scenarios are defined, game theory is applied to support the choice of the action to be performed. Our use of game theory is intended for supporting one specific agent and not for achieving the equilibrium in the market. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. The scenario analysis algorithm has been tested within MASCEM and our experimental findings with a case study based on real data from the Iberian Electricity Market are presented and discussed.

Metalearner based on Dynamic Neural Network for Strategic Bidding in electricity Markets

The restructuring of electricity markets, conducted to increase the competition in this sector, and decrease the electricity prices, brought with it an enormous increase in the complexity of the considered mechanisms. The electricity market became a complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. Software tools became, therefore, essential to provide simulation and decision support capabilities, in order to potentiate the involved players’ actions. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotiation entities. The proposed metalearner executes a dynamic artificial neural network to create its own output, taking advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that provides decision support to electricity markets’ players. The proposed metalearner considers different weights for each strategy, depending on its individual quality of performance. The results of the proposed method are studied and analyzed in scenarios based on real electricity markets’ data, using MASCEM - a multi-agent electricity market simulator that simulates market players’ operation in the market.

Sistema de Recomendação: Share GFS Games, Films & Series

Esta dissertação incide sobre o estudo e análise de uma solução para a criação de um sistema de recomendação para uma comunidade de consumidores de media e no consequente desenvolvimento da mesma cujo âmbito inicial engloba consumidores de jogos, filmes e/ou séries, com o intuito de lhes proporcionar a oportunidade de partilharem experiências, bem como manterem um registo das mesmas. Com a informação adquirida, o sistema reúne condições para proceder a sugestões direccionadas a cada membro da comunidade. O sistema actualiza a sua informação mediante as acções e os dados fornecidos pelos membros, bem como pelo seu feedback às sugestões. Esta aprendizagem ao longo do tempo permite que as sugestões do sistema evoluam juntamente com a mudança de preferência dos membros ou se autocorrijam. O sistema toma iniciativa de sugerir mediante determinadas acções, mas também pode ser invocada uma sugestão directamente pelo utilizador, na medida em que este não precisa de esperar por sugestões, podendo pedir ao sistema que as forneça num determinado momento. Nos testes realizados foi possível apurar que o sistema de recomendação desenvolvido forneceu sugestões adequadas a cada utilizador específico, tomando em linha de conta as suas acções prévias. Para além deste facto, o sistema não forneceu qualquer sugestão quando o histórico destas tinha provado incomodar o utilizador.

Reporting earnings per share: Evidence from Portuguese annual reports

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

Share the fame or share the blame? The reputational implications of partnerships

We use an adverse selection model to study the dynamics of firms' reputations when firms implement joint projects. We show that in contrast with projects implemented by a single firm, in the case of joint projects a firm's reputation does not necessarily increase following a success and does not necessarily decrease following a failure. We also study how reputation considerations affect firms ' decisions to participate in joint projects. We show that a high quality partner may not be preferable to a low quality partner, and that a high reputation partner is not necessarily preferable to a low reputation partner.

High or low initial setting price?: Initial setting price as prediction of intention to bid in on line auctions

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

How to optimize Lipton’s business model in order to stimulate tea consumption in Portugal and increase Lipton Leaf tea market share?: The importance of distribution and sales channels management in a B2B2C market

Field Lab Entrepreneurial Innovative Ventures

Mental health and the crisis: Evidence from share

Mental health constitutes a significant share of the global burden of disease. It is shaped to a great extent by socioeconomic factors and is vulnerable to external shocks. The recent financial crisis brought about stressors prone to trigger and aggravate mental illnesses. This project presents a micro analysis of the effect of the economic crisis on mental health in eleven European countries, through the estimation of individual health production functions accounting for socioeconomic controls and macroeconomic indicators. We find that mental health has deteriorated since 2007, even though the development of depression episodes is unchanged. Additionally, his variation can be partially attributed to economic recession and budgetary cuts in health spending.

Espírito Santo Saúde: The bidding war

This case study describes the current situation of Espírito Santo Saúde, which involved an eventful takeover process. The company initially went public on February 2014 and later that year, due to the financial situation of its holdings it had to be sold. The bidding war began in August 2014, after Ángeles announced the first offer. Other participants who also pitched bids include José de Mello Saúde, UnitedHealth and Fosun. Furthermore, the potential projects Espírito Santo Saúde was considering implementing prior to the sale and the current situation of the healthcare industry in Portugal, will also be analysed.

Share and Strife - The Strait of Melaka and the Portuguese (16th and 17th centuries)

The Strait of Melaka is the longest strait in the world, stretching for about 800 km from the northern tip of Sumatra to Singapore. It exhibits a dual character like no other, being simultaneously a privileged linking passage of two seas and two knots of human civilization – India and China – and a »bottleneck« that constrains the maritime connections between them. Today, the latter aspect is globally dominant. The strait is considered and analysed mostly as an obstacle rather than a linking point: how to reach China from the West or elsewhere is no longer an issue, but securing the vital flows that pass into the strait on a daily basis undoubtedly is. Accidents, natural catastrophes, political local crises or terrorist attacks are permanent dangers that could cut this umbilical cord of world trade and jeopardize a particularly sensitive and vulnerable area; piracy and pollution are the most common local threats and vulnerabilities.