The Dixit-Pindyck and the Arrow-Fisher-Hanemann-Henry option values are not equivalent

Fisher [2000, this journal] offers a unifying framework for two concepts of (quasi-) option value suggested by Arrow, Fisher, Hanemann, and Henry (AFHH) on the one hand, and by Dixit and Pindyck (DP) on the other, and claims these two concepts to be equivalent. We show that this claim is not correct and point out the flaws in Fisher's proof. We further suggest a decomposition of the DP option value into two components, one of which corresponds exactly to the AFHH option value which captures the value of obtaining new information, and a second one which captures the postponement value irrespective of uncertainty. --option value,quasi option value,decision under uncertainty,irreversible investment

Transport efficiency and dynamics of hydraulic fracture networks

Acknowledgments This study is carried out within the framework of DGMK (German Society for Petroleum and Coal Science and Technology) research project 718 “Mineral Vein Dynamics Modeling,” which is funded by the companies ExxonMobil Production Deutschland GmbH, GDF SUEZ E&P Deutschland GmbH, RWE Dea AG and Wintershall Holding GmbH, within the basic research programme of the WEG Wirtschaftsverband Erdöl- und Erdgasgewinnung e.V. We thank the companies for their financial support and their permission to publish our results. We further acknowledge support by Deutsche Forschungsgemeinschaft and Open Access Publishing Fund of University of Tübingen.Peer reviewedPublisher PD

Residence- and source-based capital taxation in open economies with infinitely-lived consumers

In this paper we investigate tax competition in a neoclassical growth model where each country may use both residence- and source-based capital taxes. We show that both types of capital taxes are zero at any interior steady state, just as in a closed economy. For symmetric countries, and even for countries that differ only with respect to size and productivity, we prove analytically and verify numerically that the open-economy policies coincide exactly with the closed-economy policies in all time periods. For countries that are asymmetric in other dimensions, we find that source-based taxes are used to manipulate the intertemporal terms of trade in the short run. Either way, the fiscal externalities of source-based taxes vanish once residence-based taxes are allowed

Spatial Interpolation of Air Quality Data with Multidimensional Gaussian Processes

The central question of this paper is whether interpolation techniques applied to a distributed sensor network can indeed provide more information than using the constant background of an urban reference station to measure air pollution. We compare different interpolation techniques based on temporal-spatial machine learning in terms of their applicability for correctly predicting personal exposure. Using a dataset of stationary low-cost sensors, we estimate exposure on a route through the city and compare it to mobile measurements. The results show that while different machine learning-based interpolation methods yield quite different results, validation of machine learning-based approaches is still challenging

Towards a Generation of Artificially Intelligent Strategy Tools: The SWOT Bot

Strategy tools are widely used to inform the complex and unstructured decision-making of firms. Although software has evolved to support strategy analysis, such digital strategy tools still require heavy manual work especially on the data input and processing levels, making their use time-intensive, costly, and susceptible to biases. This design research presents the ‘SWOT Bot’, a digital strategy tool that exploits recent advances in natural language processing (NLP) to perform a SWOT (strengths, weaknesses, opportunities, threats) analysis. Our artifact uses a feed reader, an NLP pipeline, and a visual interface to automatically extract information from a text corpus (e.g., analyst reports) and present it to the user. We argue that the SWOT Bot reduces time and adds objectivity to strategy analyses while allowing the human-in-the-loop to focus on value-adding tasks. Besides providing a functioning prototype, our work provides three general design principles for the development of next-generation digital strategy tools

Understanding Silent Failures in Medical Image Classification

To ensure the reliable use of classification systems in medical applications, it is crucial to prevent silent failures. This can be achieved by either designing classifiers that are robust enough to avoid failures in the first place, or by detecting remaining failures using confidence scoring functions (CSFs). A predominant source of failures in image classification is distribution shifts between training data and deployment data. To understand the current state of silent failure prevention in medical imaging, we conduct the first comprehensive analysis comparing various CSFs in four biomedical tasks and a diverse range of distribution shifts. Based on the result that none of the benchmarked CSFs can reliably prevent silent failures, we conclude that a deeper understanding of the root causes of failures in the data is required. To facilitate this, we introduce SF-Visuals, an interactive analysis tool that uses latent space clustering to visualize shifts and failures. On the basis of various examples, we demonstrate how this tool can help researchers gain insight into the requirements for safe application of classification systems in the medical domain. The open-source benchmark and tool are at: https://github.com/IML-DKFZ/sf-visuals.Comment: Accepted at MICCAI 2

How ice anisotropy contributes to fold and ice stream in large-scale ice-sheet models

Satellite and airborne sensors have provided detailed data on ice surface flow velocities, englacial structures of ice sheets and bedrock elevations. These data give insight into the flow behaviour of ice sheets and glaciers. One significant phenomenon observed is large-scale folds (over 100 m in amplitude) in the englacial stratigraphy in the Greenland ice sheet. A large population of folds is located at ice streams, where the flow is distinctly faster than in the surroundings, such as the North-East Greenland Ice Stream (NEGIS). While there is no consensus regarding the formation of large-scale folds, unraveling the underlying mechanisms presents significant potential for enhancing our understanding of the formation and dynamics of ice streams. Ice in ice sheets is a ductile material, i.e., it can flow as a thick viscous fluid with a power-law rheology. Furthermore, ice is significantly anisotropic in its flow properties due to its crystallographic preferred orientation (CPO). Here, we use the Full-Stokes code Underworld2 (Mansour et al.,2022) for 3D modelling of the power-law and transversely isotropic ice flow, also in comparison with the isotropic ice models. Our simulated folds with anisotropic ice show complex patterns on a bumpy bedrock, and are classified into three types: large-scale folds (fold amplitudes >100 m), small-scale folds (fold amplitudes <<100 m, wavelength <<km) and recumbent basal-shear folds. Our results indicate that bedrock topography contributes to perturbations in ice layers, and that ice anisotropy due to the CPO amplifies these into large-scale folds in convergent flow by horizontal shortening. As for our ice stream model, we simulate convergent flow as initial condition, which subsequently initiates the development of shear margins due to the rotation of the ice crystal basal planes. As soon as the shear margins develop, the ice stream starts to propagate upstream in a short time and narrows in the upstream part. Our modeling shows that the anisotropic rheology of ice and CPO change play a significant role for large-scale folding and for the initiation of ice streams with distinct shear margins. Hence, we promote the implementation of ice anisotropy in large-scale ice-sheet evolution models as it holds the potential to introduce novel perspectives to the glaciological community on the dynamics of ice flow