TugaTAC Broker: A Fuzzy Logic Adaptive Reasoning Agent for Energy Trading

Smart Grid technologies are changing the way energy is generated, distributed and consumed. With the increasing spread of renewable power sources, new market strategies are needed to guarantee a more sustainable participation and less dependency of bulk generation. In PowerTAC (Power Trading Agent Competition), different software agents compete in a simulated energy market, impersonating broker companies to create and manage attractive tariffs for customers while aiming to profit. In this paper, we present TugaTAC Broker, a PowerTAC agent that uses a fuzzy logic mechanism to compose tariffs based on its customers portfolio. Fuzzy sets allow adaptive configurations for brokers in different scenarios. To validate and compare the performance of TugaTAC, we have run a local version of the PowerTAC competition. The experiments comprise TugaTAC competing against other simple agents and a more realistic configuration, with instances of the winners of previous editions of the competition. Preliminary results show a promising dynamic: our approach was able to manage imbalances and win the competition in the simple case, but need refinements to compete with more sophisticated market. (c) Springer International Publishing Switzerland 2016

How to Decrease the Amortization Bias: Experience vs. Rules

We conduct an experimental study that tests the effectiveness of de-biasing a certain form of exponential growth bias found in household finance debt decisions, called the amortization bias. We provide 251 bachelor students at a German university with a short tutorial based on one of three learning methods: experiential learning, learning a simple “I Owe More” debt rule-of-thumb, as well as learning an extended, but more accurate version of the “I Owe More” debt rule. Immediately after completing these tutorials, we retest for the amortization bias and find a significant bias improvement in all three treatments. More importantly, after confronting the same participants with similar debt scenarios approximately three weeks later, we find that those who had previously received a debt tutorial maintain a significantly larger bias improvement over the control group. However, during this short period, most of the individuals who learned the simple and complex rules-of-thumb could no longer apply the rule and reverted back to their biased answers, while the experiential learning group best retained their improvement in bias. We find evidence in this experiment that experience-based learning may be better suited to produce long-lasting improvements for attenuating the amortization bias

A characteristics framework for Semantic Information Systems Standards

Semantic Information Systems (IS) Standards play a critical role in the development of the networked economy. While their importance is undoubted by all stakeholders—such as businesses, policy makers, researchers, developers—the current state of research leaves a number of questions unaddressed. Terminological confusion exists around the notions of “business semantics”, “business-to-business interoperability”, and “interoperability standards” amongst others. And, moreover, a comprehensive understanding about the characteristics of Semantic IS Standards is missing. The paper addresses this gap in literature by developing a characteristics framework for Semantic IS Standards. Two case studies are used to check the applicability of the framework in a “real-life” context. The framework lays the foundation for future research in an important field of the IS discipline and supports practitioners in their efforts to analyze, compare, and evaluate Semantic IS Standard