Statistical Inferences for Polarity Identification in Natural Language

Information forms the basis for all human behavior, including the ubiquitous decision-making that people constantly perform in their every day lives. It is thus the mission of researchers to understand how humans process information to reach decisions. In order to facilitate this task, this work proposes a novel method of studying the reception of granular expressions in natural language. The approach utilizes LASSO regularization as a statistical tool to extract decisive words from textual content and draw statistical inferences based on the correspondence between the occurrences of words and an exogenous response variable. Accordingly, the method immediately suggests significant implications for social sciences and Information Systems research: everyone can now identify text segments and word choices that are statistically relevant to authors or readers and, based on this knowledge, test hypotheses from behavioral research. We demonstrate the contribution of our method by examining how authors communicate subjective information through narrative materials. This allows us to answer the question of which words to choose when communicating negative information. On the other hand, we show that investors trade not only upon facts in financial disclosures but are distracted by filler words and non-informative language. Practitioners - for example those in the fields of investor communications or marketing - can exploit our insights to enhance their writings based on the true perception of word choice

Tax-Transfer Systems in Europe: Between Efficiency, Redistribution and Stabilization

This thesis contains five empirical essays that aim at enriching the knowledge about European tax-transfer systems while also providing analyses concerned with concepts and developments that might become increasingly important for future policy design

Equality of Opportunity and Redistribution in Europe

The concept of equality of opportunity (EOp) goes back to Roemer (1993, 1998) who argues that a society shall guarantee its members equal access to advantage regardless of their circumstances, while holding them responsible for turning that access into actual advantage by the application of effort. Such arguments have been on the political agenda across the European Union, where the recent enlargements have brought together countries with rather different economic, social, and political backgrounds. This paper investigates how family background influences income acquisition in 15 European countries. It also scrutinizes how governments affect EOp through the design of their tax and transfer schemes. Our overall results suggest that the link between family background and economic success is usually tighter in relatively poor countries than in rich countries. Moreover, we find a clear country clustering for the Scandinavian, the Continental European, and the Anglo-Saxon countries. For Eastern Europe, our results are less definite. Looking at the impact of the tax and benefit schemes in the EU, it can be concluded that both taxes and transfers reduce inequality of opportunities, with social benefits typically playing the key role. Furthermore, the equalizing impacts of the tax benefit system on inequality of opportunity differ substantially from the ones observed when referring to the traditional notion of inequality of outcomes.equality of opportunity, inequality, redistribution

Dynamic Reactor Operation and High-Temperature Catalysis:Direct Oxidation of Methane in a Reverse-Flow-Reactor

Synthesis gas, a mixture of H2 and CO, is a key intermediate product in the petrochemical industry, where it is used e.g. for the production of methanol and liquid fuels (via Fischer-Tropsch synthesis), or as a source of hydrogen for ammonia synthesis and fuel cells. An interesting alternative for the production of syngas to the conventionally used steam-reforming of methane (SRM) is catalytic partial oxidation of methane (CPOM) [1]. Here, methane is converted in a one-step process with oxygen or air over noble metal catalysts to synthesis gas. The reaction is characterized by extremely short contact times (ƒä < 50 ms) and very high temperatures exceeding 1000¢XC. While thermodynamics allow for optimum syngas yields, a complex interaction between total and partial oxidation reactions limits these under autothermal operation. A way to overcome these autothermal limitations is by increasing catalyst temperatures, e.g. in a multifunctional reactor concept. A particularly efficient heat-integration is achieved in the dynamically operated reverse-flow reactor (RFR) [2] through periodic switching of the direction of the gas flow in the reactor. We built a computer-controlled, laboratory-scale RFR for CPOM to gain general insights into the reaction behavior in this multifunctional reactor configuration. Experimental results demonstrate that total oxidation of methane can be reduced effectively, resulting in strongly increased syngas yields compared to autothermal reactor operation without heat integration. Furthermore, maximum attainable syngas yields are shifted towards even shorter contact times compared to a conventional process, allowing for even higher space-time yields. In addition, experiments reveal that dynamic reactor operation intrinsically counteracts catalyst deactivation. Detailed numerical simulations using elementary step kinetics are performed to investigate the influence of dynamic reactor operation on surface kinetics and reaction mechanism. It is shown that the reaction mechanism is characterized by methane partial and total oxidation reactions at the catalyst front edge, followed by endothermic reforming reactions in the second half of the catalyst bed, which occur due to advantageous temperature profiles in dynamic reactor operation. Overall, the RFR is a promising configuration for efficient, small-scale production of syngas

A model of preference elicitation: The case of distributed resource allocation

Market mechanisms are deemed promising for distributed resource allocation settings by explicitly involving users into the allocation process. The market considers the users’ and providers’ valuations to generate efficient resource allocations and prices. In theory, valuations are assumed to be known to the user. In practice, however, this is not the case. It is a complex burden for both users and providers to assess their true valuation for a certain combination of resources and services and to efficiently communicate this valuation to the market. This paper contributes to the theory of designing distributed allocation models in that (i) we propose a model for preference elicitation, which allows users and providers to assess their valuations as a function of their resource requirements and strategic considerations, (ii) we show how this model can be encoded within so-called bidding agents which interact with the market on behalf of the user, and (iii) we evaluate our approach in a numerical experiment to illustrate how the bidding agent adapts to the dynamic market situation. As this evaluation shows, the model outperforms technical schedulers and can thus be used for decision support in electronic markets

Mitigating the Effects of Partial Resource Failures for Cloud Providers

Competition for users on a global market is fierce, forcing enterprises to provide for better, faster services while offering the same more cheaply. At the same time, users choose to remain oblivious of the infrastructure behind the service – only demanding that it works. Cloud service failures and inefficient management of such failures can result in significant financial cost, loss of reputation for providers, and drive key customers away. At the same time failure situations can never be completely avoided. To mitigate their effects we present a decision model for providers to help them decide which jobs to keep running and which to cancel in order to minimize loss of revenue and key customers during partial resource failures. The results of the evaluation of the model and its extension show its ability to significantly improve revenue. Furthermore the model can also help to reduce the number of cancelled jobs