Bohmian quantum gravity and cosmology

Quantum gravity aims to describe gravity in quantum mechanical terms. How exactly this needs to be done remains an open question. Various proposals have been put on the table, such as canonical quantum gravity, loop quantum gravity, string theory, etc. These proposals often encounter technical and conceptual problems. In this chapter, we focus on canonical quantum gravity and discuss how many conceptual problems, such as the measurement problem and the problem of time, can be overcome by adopting a Bohmian point of view. In a Bohmian theory (also called pilot-wave theory or de Broglie-Bohm theory, after its originators de Broglie and Bohm), a system is described by certain variables in space-time such as particles or fields or something else, whose dynamics depends on the wave function. In the context of quantum gravity, these variables are a space-time metric and suitable variable for the matter fields (e.g., particles or fields). In addition to solving the conceptual problems, the Bohmian approach yields new applications and predictions in quantum cosmology. These include space-time singularity resolution, new types of semi-classical approximations to quantum gravity, and approximations for quantum perturbations moving in a quantum background.Comment: 45 pages, 6 figures, PDFLaTeX; written for "Applied Bohmian Mechanics: From Nanoscale Systems to Cosmology", edited by Xavier Oriols Pladevall and Jordi Mompart; v2 typos correcte

CORRECTING FOR SPATIAL EFFECTS IN LIMITED DEPENDENT VARIABLE REGRESSION: ASSESSING THE VALUE OF "AD-HOC" TECHNIQUES

A common test for spatial dependence in regression analysis with continuous dependent variables is the Moran's I. For limited dependent variable models, the standard definition of a residual breaks down because yi is qualitative. Efforts to correct for potential spatial effects in limited dependent variable models have relied on ad-hoc methods such as including a spatial lag variable or using a regular sample that omits neighboring observations. Kelejian and Prucha have recently developed a version of Moran's I for limited dependent variable models. We present the statistic in a more accessible way and use it to test the value of previously-used ad-hoc techniques with a specific data set. Keywords: Morans I, Spatial Autocorrelation, Limited Dependent Variable Models, Land-Use Change, Geographical Information Systems (GIS),Moran's I, Spatial Autocorrelation, Limited Dependent Variable Models, Land-Use Change, Geographical Information Systems (GIS), Research Methods/ Statistical Methods,

Modeling Deforestation and Land Use Change: Sparse Data Environments

Land use change in developing countries is of great interest to policymakers and researchers from many backgrounds. Concerns about consequences of deforestation for global climate change and biodiversity have received the most publicity, but loss of wetlands, declining land productivity, and watershed management are also problems facing developing countries. In developing countries, analysis is especially constrained by lack of data. This paper reviews modeling approaches for data-constrained environments that involve methods such as neural nets and dynamic programming and research results that link individual household survey data with satellite images using geographic positioning systems.Land Economics/Use, Q15, Q23, R14,

Assessing the Robustness of Predictions in Spatially Explicit Models of Land Use

We propose an information-theoretic approach to assess the performance of a discrete choice model used to analyze land use and land use change. We show that our disaggregated measure can be used to compare robustness of predictions across land use categories and across models. Furthermore, a proper reformulation of the problem shows that a disaggregated (observation by observation) log-likelihood lends itself to an information theoretic interpretation, which allows comparisons performance across models.Land Economics/Use,