A Quantitative Model of Sovereign Debt, Bailouts and Conditionality

International Financial Institutions provide temporary balance-of-payment support contingent on the implementation of specific macroeconomic policies. While several emerging markets repeatedly used conditional assistance, sovereign defaults occurred. This paper develops a dynamic stochastic model of a small open economy with endogenous default risk and endogenous participation rates in bailout programs. Conditionality enters as a constraint on fiscal policy. In a quantitative application to Argentina the model mimics the empirical duration and frequency of bailout programs. In equilibrium, conditional bailouts generate high and volatile interest spreads. A Laffer-curve in conditionality reflects the trade-off between fostering fiscal reform and creating incentives for non-compliance.sovereign debt, sovereign default, interest rate spread, fiscal policy, bailouts, conditionality

Expolring Architectures for CNN-Based Word Spotting

The goal in word spotting is to retrieve parts of document images which are relevant with respect to a certain user-defined query. The recent past has seen attribute-based Convolutional Neural Networks take over this field of research. As is common for other fields of computer vision, the CNNs used for this task are already considerably deep. The question that arises, however, is: How complex does a CNN have to be for word spotting? Are increasingly deeper models giving increasingly bet- ter results or does performance behave asymptotically for these architectures? On the other hand, can similar results be obtained with a much smaller CNN? The goal of this paper is to give an answer to these questions. Therefore, the recently successful TPP- PHOCNet will be compared to a Residual Network, a Densely Connected Convolutional Network and a LeNet architecture empirically. As will be seen in the evaluation, a complex model can be beneficial for word spotting on harder tasks such as the IAM Offline Database but gives no advantage for easier benchmarks such as the George Washington Database

ROSAT monitoring of persistent giant and rapid variability in the narrow-line Seyfert 1 galaxy IRAS 13224-3809

We report evidence for persistent giant and rapid X-ray variability in the radio-quiet, ultrasoft, strong Fe II, narrow-line Seyfert 1 galaxy IRAS 13224-3809. Within a 30 day ROSAT High Resolution Imager (HRI) monitoring observation at least five giant amplitude count rate variations are visible, with the maximum observed amplitude of variability being about a factor of 60. We detect a rise by a factor of about 57 in just two days. IRAS 13224-3809 appears to be the most X-ray variable Seyfert known, and its variability is probably nonlinear. We carefully check the identification of the highly variable X-ray source with the distant galaxy, and it appears to be secure. We examine possible explanations for the giant variability. Unusually strong relativistic effects and partial covering by occulting structures on an accretion disc can provide plausible explanations of the X-ray data, and we explore these two scenarios. Relativistic boosting effects may be relevant to understanding the strong X-ray variability of some steep spectrum Seyferts more generally.Comment: 14 pages, submitted to MNRA

Entanglement-enhanced optical gyroscope

Fiber optic gyroscopes (FOG) based on the Sagnac effect are a valuable tool in sensing and navigation and enable accurate measurements in applications ranging from spacecraft and aircraft to self-driving vehicles such as autonomous cars. As with any classical optical sensors, the ultimate performance of these devices is bounded by the standard quantum limit (SQL). Quantum-enhanced interferometry allows us to overcome this limit using non-classical states of light. Here, we report on an entangled-photon gyroscope that uses path-entangled NOON-states (N=2) to provide phase supersensitivity beyond the standard-quantum-limit

Automated Negotiations Under Uncertain Preferences

Automated Negotiation is an emerging field of electronic markets and multi-agent system research. Market engineers are faced in this connection with computational as well as economic issues, such as individual rationality and incentive compatibility. Most literature is focused on autonomous agents and negotiation protocols regarding these issues. However, common protocols show two deficiencies: (1) neglected consideration of agents’ incentives to strive for social welfare, (2) underemphasised acknowledgement that agents build their decision upon preference information delivered by human principals. Since human beings make use of heuristics for preference elicitation, their preferences are subject to informational uncertainty. The contribution of this paper is the proposition of a research agenda that aims at overcoming these research deficiencies. Our research agenda draws theoretically and methodologically on auctions, iterative bargaining, and fuzzy set theory. We complement our agenda with simulation-based preliminary results regarding differences in the application of auctions and iterative bargaining

Steuerung komplexer Systeme – Ergebnisse einer soziologischen Simulationsstudie

Die Frage, ob sich komplexe Systeme steuern lassen, beschäftigt die Sozialwissenschaften seit geraumer Zeit. Der folgende Beitrag bearbeitet das Thema „Steuerung komplexer Systeme“ mit Mitteln der experimentellen Soziologie, um auf diese Weise die Wirkung und die Leistungsfähigkeit unterschiedlicher Governance-Modi empirisch zu überprüfen. Zu diesem Zwecke wurde das Simulationsframework SUMO-S entwickelt, dessen soziologische Grundlagen das Modell soziologischer Erklärung (Esser) und das Modell der Frame-Selektion (Kroneberg) bilden. Die Performance von Governance wurde mittels dreier Makro-Indikatoren und zweier Mikro-Indikatoren vermessen. Überraschenderweise erreicht die zentrale Steuerung in der Regel bessere Werte als die dezentrale Koordination. Aber offenbar kommt es nicht allein auf den Governance- Modus an; denn es gibt einen – bislang wenig erforschten – Zusammenhang zwischen der Leistungsfähigkeit der Governance-Modi und der Zusammensetzung der Agentenpopulation

Steuerung komplexer Systeme - Ein Mehrebenen-Modell von Governance

Die Frage, ob sich komplexe Systeme steuern lassen, beschäftigt die Sozialwissenschaften seit geraumer Zeit. Der folgende Beitrag greift die steuerungstheoretische Debatte der 1980er Jahre wie auch den seit den 1990er Jahre andauernden Governance- Diskurs auf und entwickelt auf dieser Grundlage ein Mehrebenen Modell von Governance, das drei Dimensionen umfasst: die Abstimmungsprozesse in Verhandlungssystemen, die Regulierung funktioneller Teilsysteme sowie die operative Steuerung dieser Teilsysteme. In diesem Sinne steht der Begriff „Governance“ für eine spezifische Kombination der basalen Mechanismen Koordination und Steuerung in einem sozialen bzw. sozio-technischen System. Das Mehrebenen-Modell ermöglicht es, die unterschiedlichen Dimensionen von Governance getrennt in den Blick zu nehmen, aber auch die Interdependenzen zwischen den Ebenen zu analysieren. Bezogen auf komplexe Systeme, so die steuerungstheoretische Implikation unseres Governance-Begriffs, hat Governance immer das Kernproblem zu bewältigen, durch die Koordination von Akteuren sicherzustellen, dass gesteuert werden kann

Block Crossings in Storyline Visualizations

Storyline visualizations help visualize encounters of the characters in a story over time. Each character is represented by an x-monotone curve that goes from left to right. A meeting is represented by having the characters that participate in the meeting run close together for some time. In order to keep the visual complexity low, rather than just minimizing pairwise crossings of curves, we propose to count block crossings, that is, pairs of intersecting bundles of lines. Our main results are as follows. We show that minimizing the number of block crossings is NP-hard, and we develop, for meetings of bounded size, a constant-factor approximation. We also present two fixed-parameter algorithms and, for meetings of size 2, a greedy heuristic that we evaluate experimentally.Comment: Appears in the Proceedings of the 24th International Symposium on Graph Drawing and Network Visualization (GD 2016