In diesem Beitrag wird die Berechnung von Differenzen zwischen Prozessmodellen betrachtet. Hierzu werden verschiedene Ansätze undWerkzeuge zur Berechnung von Differenzen beschrieben. Ausgehend von einem konkreten Anwendungsfall – einer EPK-zu-BPEL-Transformation – werden verschiedene Testkriterien aufgestellt, die anschließend zur Evaluierung von zwei Differenzbildungswerkzeugen dienen. Abschließend werden die Ergebnisse der Untersuchung entsprechend dargestellt.

A detailed and comprehensive description of the state of the art in the field of flood forecasting opens this work. Advantages and shortcomings of currently available methods are identified and discussed. Amongst others, one important aspect considers the most exigent weak point of today’s forecasting systems: The representation of all the fundamentally different event specific patterns of flood formation with one single set of model parameters. The study exemplarily proposes an alternative for overcoming this restriction by taking into account the different process characteristics of flood events via a dynamic parameterisation strategy. Other fundamental shortcomings in current approaches especially restrict the potential for real time flash flood forecasting, namely the considerable computational requirements together with the rather cumbersome operation of reliable physically based hydrologic models. The new PAI-OFF methodology (Process Modelling and Artificial Intelligence for Online Flood Forecasting) considers these problems and offers a way out of the general dilemma. It combines the reliability and predictive power of physically based, hydrologic models with the operational advantages of artificial intelligence. These operational advantages feature extremely low computation times, absolute robustness and straightforward operation. Such qualities easily allow for predicting flash floods in small catchments taking into account precipitation forecasts, whilst extremely basic computational requirements open the way for online Monte Carlo analysis of the forecast uncertainty. The study encompasses a detailed analysis of hydrological modeling and a problem specific artificial intelligence approach in the form of artificial neural networks, which build the PAI-OFF methodology. Herein, the synthesis of process modelling and artificial neural networks is achieved by a special training procedure. It optimizes the network according to the patterns of possible catchment reaction to rainstorms. This information is provided by means of a physically based catchment model, thus freeing the artificial neural network from its constriction to the range of observed data – the classical reason for unsatisfactory predictive power of netbased approaches. Instead, the PAI-OFF-net learns to portray the dominant process controls of flood formation in the considered catchment, allowing for a reliable predictive performance. The work ends with an exemplary forecasting of the 2002 flood in a 1700 km² East German watershed.

Diese Dissertation umfasst drei voneinander unabhängige Artikel. In diesen werden neue Prozess-modelle vorgestellt, die von der entscheidungspsychologischen Forschung inspiriert wurden. Im ersten Artikel werden Entscheidungsprozesse mit mehreren Entscheidungsmerkmalen als gesteuerte Suchprozesse modelliert. Zunächst wird ein theoretischer Rahmen vorgestellt, in dem ökonomische Modelle Entscheidungen mit Suche mit Modellen des subjektiven Nutzens aus dem Bereich der psychologischen Forschung integriert wird. In den so modellierten Entscheidungsprozessen wird angenommen, dass Individuen ihre Entscheidungsalternativen nach deren abnehmenden Nutzen ordnen und dann so lange durchsuchen, bis die erwarteten Suchkosten höher als die entsprechenden Gewinne sind. Anschliessend wird die Güte dreier Entscheidungsmodelle an zwölf realen Datensätzen überprüft. Im zweiten Artikel werden die Ergebnisse zweier Experimente vorgestellt, in denen untersucht wurde, wie Personen ihre Urteile verändern, wenn sie den Urteilen und dem der Konfidenzniveau anderer Personen ausgesetzt sind. Ein Baummodell wird eingeführt, welches abbildet, wie Urteile aufgrund solcher Informationen revidiert werden. Dieses Modell basiert auf den Ergebnissen der beiden Experimente: Indem soziale Informationen berücksichtigt werden, kann es zeigen, wie Urteile in einer Gruppe interagierender Personen zusammenlaufen oder polarisieren. Im dritten Artikel wird kollektives Verhalten in Märkten für kulturelle Produkte untersucht. Personen ordnen die Optionen entsprechend ihrer Popularität an und entscheiden sich dann für diejenige, die einen Nutzen hat, der über einer bestimmten ausreichend guten Schwelle liegt. Nach jeder individuellen Entscheidung wird die Rangfolge revidiert. Innerhalb dieses einfachen Rahmens wird demonstriert, dass solche Märkte durch eine sogenannte rich get richer-Dynamik charakterisiert sind. Diese führt zu Ungleichheiten in den Marktanteilen und ungewissen finanziellen Erlösen. / This dissertation comprises of three independent essays which introduce novel psychologically inspired process models and examine their implications for individual, collective or market behavior. The first essay studies multi-attribute choice as a guided process of search. It puts forward a theoretical framework which integrates work on search and stopping with partial information from economics with psychological subjective utility models from the field of judgment and decision making. The alternatives are searched in order of decreasing estimated utility, until the expected cost of search exceeds the relevant benefits; The essay presents the results of a performance comparison of three well-studied multi-attribute choice models.The second essay reports the results of two experiments designed to understand how people revise their judgments of factual questions after being exposed to the opinion and confidence levels of others. It introduces a tree model of judgment revision which is directly derived from the empirical observations. The model demonstrates how opinions in a group of interacting people can converge or polarize over repeated interactions. The third essay, studies collective behavior in markets for search products. The decision makers consider the alternatives in order of decreasing popularity and choose the first alternative with utility higher than a certain satisficing threshold. The popularity order is updated after each individual choice. The presented framework illustrates that such markets are characterized by rich-get-richer dynamics which lead to inequality in the market-share distribution and unpredictability in regard to the final outcome.

A detailed and comprehensive description of the state of the art in the field of flood forecasting opens this work. Advantages and shortcomings of currently available methods are identified and discussed. Amongst others, one important aspect considers the most exigent weak point of today’s forecasting systems: The representation of all the fundamentally different event specific patterns of flood formation with one single set of model parameters. The study exemplarily proposes an alternative for overcoming this restriction by taking into account the different process characteristics of flood events via a dynamic parameterisation strategy. Other fundamental shortcomings in current approaches especially restrict the potential for real time flash flood forecasting, namely the considerable computational requirements together with the rather cumbersome operation of reliable physically based hydrologic models. The new PAI-OFF methodology (Process Modelling and Artificial Intelligence for Online Flood Forecasting) considers these problems and offers a way out of the general dilemma. It combines the reliability and predictive power of physically based, hydrologic models with the operational advantages of artificial intelligence. These operational advantages feature extremely low computation times, absolute robustness and straightforward operation. Such qualities easily allow for predicting flash floods in small catchments taking into account precipitation forecasts, whilst extremely basic computational requirements open the way for online Monte Carlo analysis of the forecast uncertainty. The study encompasses a detailed analysis of hydrological modeling and a problem specific artificial intelligence approach in the form of artificial neural networks, which build the PAI-OFF methodology. Herein, the synthesis of process modelling and artificial neural networks is achieved by a special training procedure. It optimizes the network according to the patterns of possible catchment reaction to rainstorms. This information is provided by means of a physically based catchment model, thus freeing the artificial neural network from its constriction to the range of observed data – the classical reason for unsatisfactory predictive power of netbased approaches. Instead, the PAI-OFF-net learns to portray the dominant process controls of flood formation in the considered catchment, allowing for a reliable predictive performance. The work ends with an exemplary forecasting of the 2002 flood in a 1700 km² East German watershed.