Adaptive learning in agents behaviour: a framework for electricity markets simulation

Electricity markets are complex environments with very particular characteristics. A critical issue regarding these specific characteristics concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, which was performed so that the competitiveness could be increased, but it also had exponential implications in the increase of the complexity and unpredictability in those markets scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behaviour. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This dissertation presents ALBidS – Adaptive Learning strategic Bidding System, a multiagent system created to provide decision support to market negotiating players. This system is integrated with the MASCEM electricity market simulator, so that its advantage in supporting a market player can be tested using cases based on real markets’ data. ALBidS considers several different methodologies based on very distinct approaches, to provide alternative suggestions of which are the best actions for the supported player to perform. The approach chosen as the players’ actual action is selected by the employment of reinforcement learning algorithms, which for each different situation, simulation circumstances and context, decides which proposed action is the one with higher possibility of achieving the most success. Some of the considered approaches are supported by a mechanism that creates profiles of competitor players. These profiles are built accordingly to their observed past actions and reactions when faced with specific situations, such as success and failure. The system’s context awareness and simulation circumstances analysis, both in terms of results performance and execution time adaptation, are complementary mechanisms, which endow ALBidS with further adaptation and learning capabilities.

Representations of Portugal in Herman Hesse

The aim of my research is to answer the question: How is Portugal seen by non-Portuguese fictionists? The main reason why I chose this research line is the following: Portuguese essayists like Eduardo Lourenço and José Gil (2005) focus their attention on the image or representation of Portugal as conceived by the Portuguese; indeed there is a tendency in Portuguese cultural studies (and, to a certain extent, also in Portuguese philosophical studies) to focus on studying the so-called ‗portugalidade‘ (portugueseness), i.e., the essence of being Portuguese. In my view, the problem with the studies I have been referring to is that everything is self-referential, and if ‗portugueseness‘ is an issue, then it might be useful, when dealing with it, to separate subject from object of observation. That is the reason why we, in the CEI (Centro de Estudos Interculturais), decided to start this research line, which is an inversion in the current tendency of the studies about ‗portugueseness‘: instead of studying the image or representation of Portugal by the Portuguese, my task is to study the image or representation of Portugal by the non-Portuguese, in this case, in non-Portuguese fiction. For the present paper I selected three writers of the 20th century: the German Hermann Hesse and the North-Americans Philip Roth and Paul Auster