Asset Pricing and Productivity Growth: The Role of Consumption Scenarios

The paper analyzes the performance of asset prices implied by an aggregate macroeconomic growth model under two different consumption hypotheses: overlapping generations of agents with two period lives versus the infinitely lived agent. The production side of the economy is described by a random growth model with a competitive labor market and an exogenously given random dividend payout ratio. For an isoelastic technology with multiplicative production shocks this implies a random dynamical system for the firm’s rate of profit with a unique asymptotically stable random fixed point for a large class of productivity growth and dividend payout ratio processes. Based on an extensive numerical study of stationary solutions we show that the two consumption scenarios imply a limited number of diverse effects regarding equity and bond returns and equity premia.     

Automatic Construction and Verification of Isotopy Invariants

We extend our previous study of the automatic construction of isomorphic classification theorems for algebraic domains by considering the isotopy equivalence relation. Isotopism is an important generalisation of isomorphism, and is studied by mathematicians in domains such as loop theory. This extension was not straightforward, and we had to solve two major technical problems, namely, generating and verifying isotopy invariants. Concentrating on the domain of loop theory, we have developed three novel techniques for generating isotopic invariants, by using the notion of universal identities and by using constructions based on subblocks. In addition, given the complexity of the theorems that verify that a conjunction of the invariants form an isotopy class, we have developed ways of simplifying the problem of proving these theorems. Our techniques employ an interplay of computer algebra, model generation, theorem proving, and satisfiability-solving methods. To demonstrate the power of the approach, we generate isotopic classification theorems for loops of size 6 and 7, which extend the previously known enumeration results. This work was previously beyond the capabilities of automated reasoning techniques. The author’s work was supported by EPSRC MathFIT grant GR/S31099.     

Stability-based validation of clustering solutions

Data clustering describes a set of frequently employed techniques in exploratory data analysis to extract "natural" group structure in data. Such groupings need to be validated to separate the signal in the data from spurious structure. In this context, finding an appropriate number of clusters is a particularly important model selection question. We introduce a measure of cluster stability to assess the validity of a cluster model. This stability measure quantifies the reproducibility of clustering solutions on a second sample, and it can be interpreted as a classification risk with regard to class labels produced by a clustering algorithm. The preferred number of clusters is determined by minimizing this classification risk as a function of the number of clusters. Convincing results are achieved on simulated as well as gene expression data sets. Comparisons to other methods demonstrate the competitive performance of our method and its suitability as a general validation tool for clustering solutions in real-world problems.     

Data collection in global software engineering research: learning from past experience

Global Software Engineering has become a standard in today’s software industry. Research in distributed software development poses severe challenges that are due to the spatial and temporal distribution of the actors, as well as to language, intercultural and organizational aspects. These challenges occur in addition to “traditional” challenges of the domain itself in large-scale software projects, like coordination and communication issues, requirements volatily, lack of domain knowledge, among others. While several authors have reported empirical studies of global software development projects, the methodological difficulties and challenges of this type of studies have not been sufficiently discussed. In this paper, we share our experiences of collecting and analysing qualitative data in the context of Global Software Engineering projects. We discuss strategies for gaining access to field sites, building trust and documenting distributed and complex work practices in the context of several research projects we have conducted in the past 9 years. The experiences described in this paper illustrate the need to deal with fundamental problems, such as understanding local languages and different cultures, observing synchronous interaction, or dealing with barriers imposed by political conflicts between the sites. Based on our findings, we discuss some practical implications and strategies that can be used by other researchers and provide some recommendations for future research in methodological aspects of Global Software Engineering.     

Scaling Kernel-Based Systems to Large Data Sets

In the form of the support vector machine and Gaussian processes, kernel-based systems are currently very popular approaches to supervised learning. Unfortunately, the computational load for training kernel-based systems increases drastically with the size of the training data set, such that these systems are not ideal candidates for applications with large data sets. Nevertheless, research in this direction is very active. In this paper, I review some of the current approaches toward scaling kernel-based systems to large data sets.     

Dynamic Complexity Theory Revisited

Dynamic complexity investigates the required effort to maintain knowledge about a property of a structure under changing operations. This article introduces a refined notion of dynamic problems which takes the initial structure into account. It develops the basic structural complexity notions accordingly. It also shows that the dynamic version of the LOGCFL-complete problem D₂LREACH(acyclic) can be maintained with first-order updates.     

Keine Mautdaten für Ermittlungsverfahren

Das Autobahnmautgesetz zeichnet sich durch eine strenge Zweckbindung für die Mautdaten aus. Diese dürfen ausschlie?lich für die Zwecke des Mautgesetzes erhoben und verarbeitet werden. Diese Vorgabe hat das LG Magdeburg in einem Beschluss richtungsweisend auch für die Verbindungsdaten der GSM-Kommunikation best?tigt.     

Service level management with agent technology

This paper outlines a generic evaluation methodology for multimedia and real time applications. It concentrates on the application layer and the service aspect. Principles of software evaluation for quality assessment and measurement for networked services and distributed applications are used to present a specific method for measuring important characteristics. The method and the measurement procedure allow comparison of the actual characteristics of service quality with the required characteristics and thresholds. The proof of concepts will be made by the implementation of quality measurement agents following the approach outlined in this paper. Our approach of performing service level management (SLM) with agent technology is distributed, slim, minimizable to the maximum, independent in its methodology and offers comparable, objective results.     

Statistical relational learning of trust

The learning of trust and distrust is a crucial aspect of social interaction among autonomous, mentally-opaque agents. In this work, we address the learning of trust based on past observations and context information. We argue that from the truster’s point of view trust is best expressed as one of several relations that exist between the agent to be trusted (trustee) and the state of the environment. Besides attributes expressing trustworthiness, additional relations might describe commitments made by the trustee with regard to the current situation, like: a seller offers a certain price for a specific product. We show how to implement and learn context-sensitive trust using statistical relational learning in form of a Dirichlet process mixture model called Infinite Hidden Relational Trust Model (IHRTM). The practicability and effectiveness of our approach is evaluated empirically on user-ratings gathered from eBay. Our results suggest that (i) the inherent clustering achieved in the algorithm allows the truster to characterize the structure of a trust-situation and provides meaningful trust assessments; (ii) utilizing the collaborative filtering effect associated with relational data does improve trust assessment performance; (iii) by learning faster and transferring knowledge more effectively we improve cold start performance and can cope better with dynamic behavior in open multiagent systems. The later is demonstrated with interactions recorded from a strategic two-player negotiation scenario.