Brazilian foreign policy under President Lula (2003-2010): an overview

Foreign Minister of Brazil since 2003, Ambassador Celso Amorim outlines the main guidelines and accomplishments of Brazil's foreign policy under President Luiz Inácio Lula da Silva. The article provides a full-fledged, although not exhaustive, narrative of a number of diplomatic initiatives championed by Brazil over the last eight years: from the gathering of the group of developing countries in a World Trade Organization (WTO) meeting in Cancun to the negotiations that led to the Declaration of Tehran, as well as the challenges the country has been facing as its international weight grows.

Fighting climate change in the air: lessons from the EU directive on global aviation

The European Union's (EU) decision to include aviation into the Emissions Trade Scheme was heatedly contested. Countries around the world, but mainly the Brazil, Russia, India, China and South Africa group (BRICS) and the US, denounced the EU's initiate as illegal and unilateral. Following a decade of frustrated negotiations at the International Civil Aviation Organization (ICAO), this paper interrogates why such measure, in principle climate-friendly, inspired so much global resentment. I argue that concerns with competitiveness and risks of legal inconsistency are important, but insufficient elements to explain the core of the conflict. The paper suggests that the EU was strongly criticized because third countries perceived this action as an imposed solution, which fostered an environment of distrust. Therefore, I claim that the problem has more to do with a normative divide than with a substantive divergence on what should be done regarding aviation emissions. My analysis is informed by the present literature on the links between trade and climate change, but gives particular weight to first-hand information through interviews with key stakeholders. The paper is divided in three parts. First, it presents the scope of the EU directive in historical perspective. Second, it explores the EU's measure through three different angles: legal, economical and political. The final part explores some possible solutions to overcome these divergences.

Chinese engagement for Global Governance: aiming for a better room at the table?

The text defends the thesis that China is aware that its process of modernization is still recent and needs more growth, maturation and consolidation to assume the role and responsibilities of a regional or even a global leader. This text pursues an understanding about the Chinese stance in relation to the negotiations for the redefinitions of the International Order and the International System (and, hence, Global Governance) and, secondly, seeks to perceive the reactions of non-developed and developed countries in relation to Chinese strategies.

EU Commission participation in the Troika mission: is there a European Union price to pay?

The article is intended to debate two questions regarding the involvement of the Commission in the Troika's action: firstly, considering the nature of financial assistance programs, it aims to discuss the effect of the Commission's participation in Troika negotiations on the balance of power of the EU institutions; and secondly, the article raises the issue of the Commission's liability for the results achieved by the financial assistance program, taking into account the extent of the conditions imposed, as well as the intensity of scrutiny by the Troika.

Configuração e interrogação com sistemas federados

Apesar da existência de produtos comerciais e da investigação na área, a construção de sistemas de informação com diversos componentes distribuídos, heterogéneos e autónomos - conhecidos como sistemas de informação federados - é ainda um desafio. Estes sistemas de informação oferecem uma visão global unificada sobre os vários modelos de dados (parciais). No entanto, a modelação destes sistemas é um desafio, já que modelos de dados como o relacional não incluem informação sobre a distribuição e tratamento de heterogeneidade. É também necessário interagir com estes sistemas de informação, através de interrogações sobre os diversos componentes dos sistemas, sem ser necessário conhecer os detalhes dos mesmos. Este trabalho propõe uma abordagem a estes desafios, através da utilização de modelos para descrição semântica, e.g. linguagem OWL (Ontology Web Language), para construir uma descrição unificada dos seus diversos modelos parciais. O modelo criado para dar suporte a esta descrição é, em parte, baseado em ontologias existentes, que foram alteradas e extendidas para resolver diversos desafios de modelação. Sobre este modelo, é criado um componente de software que permite a execução de interrogações SQL (Structured Query Language) sobre o sistema federado, resolvendo os problemas de distribuição e heterogeneidade existentes.

Rede social académica

O processo de Bolonha apresenta directivas para a construção de um espaço europeu de ensino superior. A adopção destas directivas requer uma abordagem que favoreça, na prática, a mobilidade dos estudantes que têm dificuldades em compreenderem as oportunidades que lhes são oferecidas. Neste contexto, esta dissertação explora a hipótese de utilização de uma rede social para apoiar a mobilidade de estudantes no espaço europeu. No âmbito desta dissertação propõe-se um modelo de conhecimento para representar os membros de uma rede social vocacionada para apoiar cenários de mobilidade, designada por rede social académica. Este modelo foi obtido pela fusão da ontologia Academic Ontology to Support the Bologna Mobility Process com a ontologia Friend of a Friend Ontology. Para efeitos de avaliação experimental, foi criado um demonstrador numa rede social disponível publicamente na Internet que utiliza uma versão simplificada do modelo proposto. Os cenários usados nas experiências representam situações reais às quais foi aplicado um processo rudimentar de descoberta de conhecimento

Learning business strategies for competitive electronic marketplaces

This paper presents a Multi-Agent Market simulator designed for developing new agent market strategies based on a complete understanding of buyer and seller behaviors, preference models and pricing algorithms, considering user risk preferences and game theory for scenario analysis. This tool studies negotiations based on different market mechanisms and, time and behavior dependent strategies. The results of the negotiations between agents are analyzed by data mining algorithms in order to extract rules that give agents feedback to improve their strategies. The system also includes agents that are capable of improving their performance with their own experience, by adapting to the market conditions, and capable of considering other agent reactions.

MASCEM improvement for agent based coalitions

This paper presents MASCEM - Multi-Agent Simulator for Electricity Markets improvement towards an enlarged model for Seller Agents coalitions. The simulator has been improved, both regarding its user interface and internal structure. The OOA, used as development platform, version was updated and the multi-agent model was adjusted for implementing and testing several negotiations regarding Seller agents’ coalitions. Seller coalitions are a very important subject regarding the increased relevance of Distributed Generation under liberalised electricity markets.

Multi-agent electricity market simulation with dynamic strategies & virtual power producers

Distributed energy resources will provide a significant amount of the electricity generation and will be a normal profitable business. In the new decentralized grid, customers will be among the many decentralized players and may even help to co-produce the required energy services such as demand-side management and load shedding. So, they will gain the opportunity to be more active market players. The aggregation of DG plants gives place to a new concept: the Virtual Power Producer (VPP). VPPs can reinforce the importance of these generation technologies making them valuable in electricity markets. In this paper we propose the improvement of MASCEM, a multi-agent simulation tool to study negotiations in electricity spot markets based on different market mechanisms and behavior strategies, in order to take account of decentralized players such as VPP.

Multiagent system for adaptive strategy formulation in electricity markets

Electricity markets are complex environments with very particular characteristics. MASCEM is a market simulator developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations. This paper presents a new proposal for the definition of MASCEM players’ strategies to negotiate in the market. The proposed methodology is multiagent based, using reinforcement learning algorithms to provide players with the capabilities to perceive the changes in the environment, while adapting their bids formulation according to their needs, using a set of different techniques that are at their disposal.

VPP´s multi-level negotiation in smart grids and competitive electricity markets

The increase of distributed generation (DG) has brought about new challenges in electrical networks electricity markets and in DG units operation and management. Several approaches are being developed to manage the emerging potential of DG, such as Virtual Power Players (VPPs), which aggregate DG plants; and Smart Grids, an approach that views generation and associated loads as a subsystem. This paper presents a multi-level negotiation mechanism for Smart Grids optimal operation and negotiation in the electricity markets, considering the advantages of VPPs’ management. The proposed methodology is implemented and tested in MASCEM – a multiagent electricity market simulator, developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations.

Bid definition method for electricity markets based on an adaptive multiagent system

Electricity markets are complex environments with very particular characteristics. MASCEM is a market simulator developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations. This paper presents a new proposal for the definition of MASCEM players’ strategies to negotiate in the market. The proposed methodology is multiagent based, using reinforcement learning algorithms to provide players with the capabilities to perceive the changes in the environment, while adapting their bids formulation according to their needs, using a set of different techniques that are at their disposal. Each agent has the knowledge about a different method for defining a strategy for playing in the market, the main agent chooses the best among all those, and provides it to the market player that requests, to be used in the market. This paper also presents a methodology to manage the efficiency/effectiveness balance of this method, to guarantee that the degradation of the simulator processing times takes the correct measure.

Strategic bidding methodology for electricity markets using adaptive learning

The very particular characteristics of electricity markets, require deep studies of the interactions between the involved players. MASCEM is a market simulator developed to allow studying electricity market negotiations. This paper presents a new proposal for the definition of MASCEM players’ strategies to negotiate in the market. The proposed methodology is implemented as a multiagent system, using reinforcement learning algorithms to provide players with the capabilities to perceive the changes in the environment, while adapting their bids formulation according to their needs, using a set of different techniques that are at their disposal. This paper also presents a methodology to define players’ models based on the historic of their past actions, interpreting how their choices are affected by past experience, and competition.

Ontologies for reusing data cleaning knowledge

The emergence of new business models, namely, the establishment of partnerships between organizations, the chance that companies have of adding existing data on the web, especially in the semantic web, to their information, led to the emphasis on some problems existing in databases, particularly related to data quality. Poor data can result in loss of competitiveness of the organizations holding these data, and may even lead to their disappearance, since many of their decision-making processes are based on these data. For this reason, data cleaning is essential. Current approaches to solve these problems are closely linked to database schemas and specific domains. In order that data cleaning can be used in different repositories, it is necessary for computer systems to understand these data, i.e., an associated semantic is needed. The solution presented in this paper includes the use of ontologies: (i) for the specification of data cleaning operations and, (ii) as a way of solving the semantic heterogeneity problems of data stored in different sources. With data cleaning operations defined at a conceptual level and existing mappings between domain ontologies and an ontology that results from a database, they may be instantiated and proposed to the expert/specialist to be executed over that database, thus enabling their interoperability.

ISEM: a multi-agent system that simulates competitive electronic marketplaces

With the increasing importance of large commerce across the Internet it is becoming increasingly evident that in a few years the Iternet will host a large number of interacting software agents. a vast number of them will be economically motivated, and will negociate a variety of goods and services. It is therefore important to consider the economic incentives and behaviours of economic software agents, and to use all available means to anticipate their collective interactions. This papers addresses this concern by presenting a multi-agent market simulator designed for analysing agent market strategies based on a complete understanding of buyer and seller behaviours, preference models and pricing algorithms, consideting risk preferences. The system includes agents that are capable of increasing their performance with their own experience, by adapting to the market conditions. The results of the negotiations between agents are analysed by data minig algorithms in order to extract rules that give agents feedback to imprive their strategies.