Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

Sexual agreements and intimate-partner violence among male couples

Objectives: In the United States, HIV continues to disproportionately affect men who have sex with men. One promising area of research that may inform the development of behavioral interventions among male–male couples is within the realm of sexual agreements. Methods: The purpose of our analysis was to determine whether respondents who report having an open agreement or an agreement breakage also report a higher incidence of recent (within the previous 12 months) intimate-partner violence (IPV) compared to respondents who report having a monogamous agreement or no agreement breakage after controlling for demographic variables. Results: Results showed that men who have an open agreement are less likely to report recent physical IPV. Conclusions: The results highlight the need to develop dyadic behavior interventions that address sexual agreements and stress management.     

REVIEWS

Bailey, C. E. (Ed.) (2000). Children in therapy: Using the family as a resource. Doherty, W. J. (2001). Take back your marriage: Sticking together in a world that pulls us apart. New York: Guilford. Larson, J. H. (2000). Should we stay together? A scientically proven method for evaluating your relationship and improving its chances for long‐term success. Weiner‐Davis, M. (2001). The divorce remedy: The proven 7‐step program for saving your marriage.     

Relations Between Measures of Irritability and Contingency Detection at 6 Months

The hypothesis that irritability and contingency detection are negatively correlated was examined in thirty‐one 6‐month‐old infants. Observation and maternal report‐based assessments of irritability were correlated with both a criterion score and a continuous score of contingency detection. Results indicated that greater irritability in infants was associated with lower contingency detection scores. Discussion focuses on identifying processes by which the 2 constructs may be associated.     

Spatial variation in soil inorganic nitrogen across an arid urban ecosystem

We explored variations in inorganic soil nitrogen (N) concentrations across metropolitan Phoenix, Arizona, and the surrounding desert using a probability-based synoptic survey. Data were examined using spatial statistics on the entire region, as well as for the desert and urban sites separately. Concentrations of both NO₃-N and NH₄-N were markedly higher and more heterogeneous amongst urban compared to desert soils. Regional variation in soil NO₃-N concentration was best explained by latitude, land use history, population density, along with percent cover of impervious surfaces and lawn, whereas soil NH₄-N concentrations were related to only latitude and population density. Within the urban area, patterns in both soil NO₃-N and NH₄-N were best predicted by elevation, population density and type of irrigation in the surrounding neighborhood. Spatial autocorrelation of soil NO₃-N concentrations explained 49% of variation among desert sites but was absent between urban sites. We suggest that inorganic soil N concentrations are controlled by a number of ‘local’ or ‘neighborhood’ human-related drivers in the city, rather than factors related to an urban-rural gradient.     

Quantifying uncertainty in the biospheric carbon flux for England and Wales

Summary.  A crucial issue in the current global warming debate is the effect of vegetation and soils on carbon dioxide (CO₂) concentrations in the atmosphere. Vegetation can extract CO₂ through photosynthesis, but respiration, decay of soil organic matter and disturbance effects such as fire return it to the atmosphere. The balance of these processes is the net carbon flux. To estimate the biospheric carbon flux for England and Wales, we address the statistical problem of inference for the sum of multiple outputs from a complex deterministic computer code whose input parameters are uncertain. The code is a process model which simulates the carbon dynamics of vegetation and soils, including the amount of carbon that is stored as a result of photosynthesis and the amount that is returned to the atmosphere through respiration. The aggregation of outputs corresponding to multiple sites and types of vegetation in a region gives an estimate of the total carbon flux for that region over a period of time. Expert prior opinions are elicited for marginal uncertainty about the relevant input parameters and for correlations of inputs between sites. A Gaussian process model is used to build emulators of the multiple code outputs and Bayesian uncertainty analysis is then used to propagate uncertainty in the input parameters through to uncertainty on the aggregated output. Numerical results are presented for England and Wales in the year 2000. It is estimated that vegetation and soils in England and Wales constituted a net sink of 7.55 Mt C (1 Mt C = 10¹² g of carbon) in 2000, with standard deviation 0.56 Mt C resulting from the sources of uncertainty that are considered.     

Bankrupt Rhetoric: How Misleading Information Affects Knowledge about Social Security

Most citizens know little about politics. Scholars often attributepolitical ignorance to individual-level factors, but we concentrateon the quality of the information environment. Employing a combinationof experimental methods and content analysis, we code statementsfrom the 1998–99 debate over Social Security reform aseither misleading or not misleading. Then, using surveys conductedduring the debate, we examine the impact of individual- andenvironmental-level variables on political knowledge about theprogram’s future. We show that misleading statements aboutSocial Security’s future cause some citizens to get animportant fact about the program wrong. More precisely, manycitizens mistakenly believe that Social Security will run outof money because political elites occasionally use words thatlead to overly pessimistic assessments of the program’sfinancial future. Our findings have important implications forpolicymakers who are attempting to remake America’s largestfederal program, scholars who study citizen competence, andcitizens in a representative democracy.