Enhancing spammer detection in online social networks with trust-based metrics.

As online social networks acquire larger user bases, they also become more interesting targets for spammers. Spam can take very different forms on social Web sites and cannot always be detected by analyzing textual content. However, the platform\u27s social nature also offers new ways of approaching the spam problem. In this work the possibilities of analyzing a user\u27s direct neighbors in the social graph to improve spammer detection are explored. Special features of social Web sites and their implicit trust relations are utilized to create an enhanced attribute set that categorizes users on the Twitter microblogging platform as spammers or legitimate users

Financial and actuarial valuation of insurance derivatives.

This dissertation looks into the interplay of financial and insurance markets that is created by securitization of insurance related risks. It comprises four chapters on both the common ground and different nature of actuarial and financial risk valuation. The first chapter investigates the market for catastrophe insurance derivatives that has been established at the Chicago Board of Trade in 1992. Modeling the underlying index as a compound Poisson process the set of financial derivative prices that exclude arbitrage opportunities is characterized by the market prices of frequency and jump size risk. Fourier analysis leads to a representation of price processes that separates the underlying stochastic structure from the contract's payoff and allows derivation of the inverse Fourier transform of price processes in closed form. In a market with a representative investor, market prices of frequency and jump size risk are uniquely determined by the agent's coefficient of absolute risk aversion which consequently fixes the price process on the basis of excluding arbitrage strategies. The second chapter analyzes a model for a price index of insurance stocks that is based on the Cramer-Lundberg model used in classical risk theory. It is shown that price processes of basic securities and derivatives can be expressed in terms of the market prices of risk. This parameterization leads to formulae in closed form for the inverse Fourier transform of prices and the conditional probability distribution. Financial spreads are examined in more detail as their structure resembles the characteristics of stop loss reinsurance treaties. The equivalence between a representative agent approach and the Esscher transform is shown and the financial price process that is robust to these two selection criteria is determined. Finally, the analysis is generalized to allow for risk processes that are perturbed by diffusion. In the third chapter an integrated market is introduced containing both insurance and financial contracts. The calculation of insurance premia and financial derivative prices is presented assuming the absence of arbitrage opportunities. It is shown that in contrast to financial contracts, there exist infinitely many market prices of risk that lead to the same premium process. Thereafter a link between financial and actuarial prices is established based on the requirement that financial prices should be consistent with actuarial valuation. This connection is investigated in more detail under certain premium calculation principles. The starting point of the final chapter is the Fourier technique developed in Chapters 1 and 2. It is the aim of this chapter to generalize the analysis to underlying Levy processes. Expressions for the conditional moments and probabilities based on these processes are derived and their inverse Fourier transforms are obtained in closed form. The representation of conditional moments and probabilities separates the stochastic structure from the deterministic dependence on the underlying Levy processes

Modeling flexibility in energy systems : comparison of power sector models based on simplified test cases

Model-based scenario analyses of future energy systems often come to deviating results and conclusions when different models are used. This may be caused by heterogeneous input data and by inherent differences in model formulations. The representation of technologies for the conversion, storage, use, and transport of energy is usually stylized in comprehensive system models in order to limit the size of the mathematical problem, and may substantially differ between models. This paper presents a systematic comparison of nine power sector models with sector coupling. We analyze the impact of differences in the representation of technologies, optimization approaches, and further model features on model outcomes. The comparison uses fully harmonized input data and highly simplified system configurations to isolate and quantify model-specific effects. We identify structural differences in terms of the optimization approach between the models. Furthermore, we find substantial differences in technology modeling primarily for battery electric vehicles, reservoir hydro power, power transmission, and demand response. These depend largely on the specific focus of the models. In model analyses where these technologies are a relevant factor, it is therefore important to be aware of potential effects of the chosen modeling approach. For the detailed analysis of the effect of individual differences in technology modeling and model features, the chosen approach of highly simplified test cases is suitable, as it allows to isolate the effects of model-specific differences on results. However, it strongly limits the model's degrees of freedom, which reduces its suitability for the evaluation of fundamentally different modeling approaches

Impacts of power sector model features on optimal capacity expansion: a comparative study

The transition towards decarbonized energy systems requires the expansion of renewable and flexibility technologies in power sectors. Many powerful tools exist to find optimal capacity expansion. In a stylized comparison of six models, we evaluate the capacity expansion results of basic power sector technologies. The technologies under investigation include base- and peak-load power plants, electricity storage, and transmission. We define four highly simplified and harmonized test cases that focus on the expansion of only one or two specific technologies to isolate their effects on model results. We find that deviating assumptions on limited availability factors of technologies cause technology-specific deviations between optimal capacity expansion in models in almost all test cases. Fixed energy-to-power-ratios of storage can entirely change model optimal expansion outcomes, especially shifting the ratio between short- and long-duration storage. Fixed initial and end storage levels can impact the seasonal use of long-duration storage. Models with a pre-ordered dispatch structure substantially deviate from linear optimization models, as missing foresight and limited flexibility can lead to higher capacity investments. A simplified net transfer capacity approach underestimates the need for grid infrastructure compared to a more detailed direct current load flow approach. We further find deviations in model results of optimal storage and transmission capacity expansion between regions and link them to variable renewable energy generation and demand characteristics. We expect that the general effects identified in our stylized setting also hold in more detailed model applications, although they may be less visible there

Model-related outcome differences in power system models with sector coupling - quantification and drivers

This paper presents the results of a multi-model comparison to determine outcome deviations resulting from differences in power system models. We apply eight temporally and spatially resolved models to 16 stylized test cases. These test cases differ in their renewable energy supply share, technology scope, and optimization scope. We focus on technologies for balancing the variability of power generation, such as controllable power plants, energy storage, power transmission, and flexibility related to sector coupling. We use harmonized input data in all models to separate model-related from data-related outcome deviations. We find that our approach allows for isolating and quantifying model-related outcome deviations and robust effects concerning system operation and investment decisions. Furthermore, we can attribute these deviations to the identified model differences. Our results show that trends in the use of individual flexibility options are robust across most models. Moreover, our analysis reveals that differences in the general modeling approach and the modeling of specific technologies lead to comparatively small deviations. In contrast, a heterogeneous model scope can cause substantially larger deviations. Due to a large number of models and scenarios, our analysis can provide important information on which investment and operation decisions are robust to the model choice, and which modeling approaches have an exceptionally high impact on results. Our findings may guide both modelers and decision-makers in properly evaluating the results of similarly designed power system models

Verbundvorhaben FlexMex: Modellexperiment zur zeitlich und räumlich hoch aufgelösten Untersuchung des zukünftigen Lastausgleichs im Stromsystem

Das Projekt FlexMex konzentrierte sich auf einen Modellvergleich zur Untersuchung der Nutzung von Flexibilitätsoptionen zum Ausgleich der Stromerzeugung aus variablen erneuerbaren Energien. Die zentrale Frage war, wie unterschiedliche Optimierungs- und Technologiemodellie-rungsansätze den Anlageneinsatz in stündlich aufgelösten Stromsektormodellen beeinflussen. Darüber hinaus wurde der Einfluss unterschied-licher Modellumfänge auf den Einsatz von Flexibilitätsoptionen untersucht. Um datenbedingte von modellbedingten Unterschieden in den Ergebnissen konsequent zu trennen, wurden die Eingangsdaten der neun beteiligten Modelle vollständig harmonisiert. Die Anwendung der Modelle wurde dann in zwei Hauptexperimente unterteilt. Im ersten Experiment wurden auf der Grundlage einer umfassenden qualitativen Analyse der Modelle und ihrer Unterschiede einzelne Flexibilitätsoptionen untersucht. Anhand stark reduzierter Testfälle konnten modellspe-zifische Effekte isoliert und quantifiziert werden. Ergänzende Analysen befassten sich mit dem modellendogenen Ausbau von Stromspeichern, Stromnetzen und regelbaren Kraftwerken. Aufbauend auf den technologiespezifischen Analysen wurden im zweiten Modellexperiment komplexere Szenarien betrachtet. Dort wurden sechzehn stilisierte Szenarien betrachtet, die sich in Versorgungsanteil erneuerbarer Ener-gien, Technologieumfang und Optimierungsumfang unterscheiden. Trotz der hohen Anzahl der Modelle und der interagierenden Modellun-terschiede können die Ergebnisabweichungen auf die Modelleigenschaften zurückgeführt werden. Das Experiment zeigt, dass Unterschiede im Modellierungsansatz und der Technologieabbildung zu vergleichsweise geringen Abweichungen führen, während ein heterogener Modell-umfang einen deutlich größeren Einfluss haben kann. Zusammenfassend können die Ergebnisse des FlexMex-Projekts ein besseres Verständ-nis für die Wirkung unterschiedlicher Modellierungsansätze liefern und damit zur Interpretation von Modellergebnissen beitragen