Telling lies in everyday life: Motivational and organizational consequences of sequential preferences

This study of a collection of self-observed lies told in everyday interactions indicated that all informants lied; that lying was, generally, an easy and spontaneous activity; and that our varied informants told lies in much the same manner and for the same reasons. The analysis of the general features of the interactions in which lies were embedded showed that many lies are the consequence of a preference system that promotes acceptance and hides rejection in the sequential organization of interaction. The lies found in pre-acceptance and pre-rejection sequences indicate that both parties contrive for acceptance. The negative cases of lies told in rejection of deprecating assessments suggest a broader theoretical template that encompasses the lies told for acceptance as a subset of the interactional preference for social solidarity. In contrast to the view that telling lies undermines social cohesion by interfering with trust, this study indicates that many lies are told to affirm affiliation.     

Reducing Bushfire Risk by Planning and Design: A Professional Focus

While links between planning and resilience are increasingly touted as fundamental to managing urban settlements, there are limited practical examples. This paper provides an example via regulatory processes, in parallel with the exercise of professional discretion. Using analysis of diverse urban planning proposals in bushfire prone areas of Victoria Australia, a systematic and site-specific approach for bushfire risk assessment is set out. A three-step process is proposed as a basis for delivery of design solutions to manage bushfire risks. The paper concludes by arguing that professional judgement is a fundamental part of bushfire risk reduction within a regulatory framework.     

Simultaneous edit-imputation and disclosure limitation for business establishment data

Business establishment microdata typically are required to satisfy agency-specified edit rules, such as balance equations and linear inequalities. Inevitably some establishments' reported data violate the edit rules. Statistical agencies correct faulty values using a process known as edit-imputation. Business establishment data also must be heavily redacted before being shared with the public; indeed, confidentiality concerns lead many agencies not to share establishment microdata as unrestricted access files. When microdata must be heavily redacted, one approach is to create synthetic data, as done in the U.S. Longitudinal Business Database and the German IAB Establishment Panel. This article presents the first implementation of a fully integrated approach to edit-imputation and data synthesis. We illustrate the approach on data from the U.S. Census of Manufactures and present a variety of evaluations of the utility of the synthetic data. The paper also presents assessments of disclosure risks for several intruder attacks. We find that the synthetic data preserve important distributional features from the post-editing confidential microdata, and have low risks for the various attacks.     

Complex Thinking and Computing Organization Facing Contingent Problems

Public Organization Review - Facing the reflexive modernity, social wealth production is systematically correlated with the production of social and technical problems. Due to the complexity...     

Exploring Parameter Relations for Multi‐Stage Models in Stage‐Wise Constant and Time Dependent Hazard Rates

Single cohort stage‐frequency data are considered when assessing the stage reached by individuals through destructive sampling. For this type of data, when all hazard rates are assumed constant and equal, Laplace transform methods have been applied in the past to estimate the parameters in each stage‐duration distribution and the overall hazard rates. If hazard rates are not all equal, estimating stage‐duration parameters using Laplace transform methods becomes complex. In this paper, two new models are proposed to estimate stage‐dependent maturation parameters using Laplace transform methods where non‐trivial hazard rates apply. The first model encompasses hazard rates that are constant within each stage but vary between stages. The second model encompasses time‐dependent hazard rates within stages. Moreover, this paper introduces a method for estimating the hazard rate in each stage for the stage‐wise constant hazard rates model. This work presents methods that could be used in specific types of laboratory studies, but the main motivation is to explore the relationships between stage maturation parameters that, in future work, could be exploited in applying Bayesian approaches. The application of the methodology in each model is evaluated using simulated data in order to illustrate the structure of these models.