In this article we present the Bayesian estimation of spatial probit models in R and provide an implementation in the package spatialprobit. We show that large probit models can be estimated with sparse matrix representations and Gibbs sampling of a truncated multivariate normal distribution with the precision matrix. We present three examples and point to ways to achieve further performance gains through parallelization of the Markov Chain Monte Carlo approach.

We study how people react to small probability events with large negative consequences using the outbreak of the COVID-19 epidemic as a natural experiment. Our analysis is based on a unique administrative data set with anonymized monthly expenditures at the individual level. We find that older consumers reduced their spending by more than younger consumers in a way that mirrors the age dependency in COVID-19 case-fatality rates. This differential expenditure reduction is much more prominent for high-contact goods than for low-contact goods and more pronounced in periods with high COVID-19 cases. Our results are consistent with the hypothesis that people react to the risk of contracting COVID-19 in a way that is consistent with a canonical model of risk taking.

This article presents a 3D reconstruction technique for real world environments based on a traditional 2D laser range finder modified to implement a 3D laser scanner. The article describes the mechanical and control issues addressed to physically achieve the 3D sensor used to acquire the data. It also presents the techniques used to process and merge range and intensity data to create textured polygonal models and illustrates the potential of such a unit. The result is a promising system for 3D modeling of real world scenes at a commercial price 10 or 20 times lower than current commercial 3D laser scanners. The use of such a system can simplify measurements of existing buildings and produce easily 3D models and ortophotos of existing structures with minimum effort and at an affordable price.

Domestic environments are particularly challenging for distant speech recognition: reverberation, background noise and interfering sources, as well as the propagation of acoustic events across adjacent rooms, critically degrade the performance of standard speech processing algorithms. In this application scenario, a crucial task is the detection and localization of speech events generated by users within the various rooms. A specific challenge of multi-room environments is the inter-room interference that negatively affects speech activity detectors. In this paper, we present and compare different solutions for the multi-room speech activity detection task. The combination of a model-based room-independent speech activity detection module with a room-dependent inside/outside classification stage, based on specific features, provides satisfactory performance. The proposed methods are evaluated on a multi-room, multi-channel corpus, where spoken commands and other typical acoustic events occur in different rooms.

The general data protection regulation (GDPR) represents a dramatic shift in global privacy regulation. We focus on GDPR’s enhanced consumer consent requirements that aim to provide transparent and active elicitation of data allowances. We evaluate the effect of enhanced consent on consumer opt-in behavior and on firm behavior and outcomes after consent is solicited. Utilizing an experiment at a large telecommunications provider with operations in Europe, we find that opt-in for different data types and uses increased once GDPR-compliant consent was elicited. However, consumers did not uniformly increase data allowances and continued to generally restrict permissions for more sensitive or tangential uses of their personal information. We also find that sales, the efficacy of marketing communications, and contractual lock-in increased after consumers provided new data allowances. Additional analysis suggests that these gains to the firm emerged because new data allowances enabled them to increase their use of targeted marketing for households that were amenable to these marketing efforts. These results have significant implications for firms and policymakers and suggest that enhanced consent provided via GDPR may be effective for increasing consumer privacy protection while also allowing firms reliant on consumers’ personal information to improve outcomes. This paper was accepted by Chris Forman, information systems.