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.     

Radical Distrust: Are Economic Policy Attitudes Tempered by Social Trust?

Social Indicators Research - Debates about the appropriate role of markets and governments are often shaped by sharply contrasting opinions. Based on individual data from the World Values Survey...     

A vector of quarters representation for bivariate time series

In this paper it is shown that several models for a bivariate nonstationary quarterly time series are nested in a vector autoregression with cointegration restrictions for the eight annual series of quarterly observations. Or, the Granger Representation Theorem is extended to incorporate, e.g., seasonal and periodic cointegration.     

Economics, sociobiology and behavioral psychology on preferences

Economists have become increasingly interested in hypotheses from sociobiology as a source of inspiration for filling gaps in the economic model of behavior. To avoid borrowing eclectically and arbitrarily from neighboring disciplines, this paper attempts to outline in a systematic way the similarities and differences between the approaches taken in economics and sociobiology. In doing so, special attention is given to an empirical theory of preferences that is lacking in economics. Here, inspiration from sociobiology would seem to be particularly useful. The considerations in the paper suggest that sociobiological arguments may indeed be helpful, albeit at a very elementary level only. A more comprehensive theory cannot ignore the influences of innate learning mechanisms in higher living beings. An elaborated theory of preferences in economics will have to acknowledge and incorporate insights from behavioral psychology.     

Book reviews

Statistical Papers -     

Detecting seasonal unit roots in a structural time series model

In this paper, we propose to detect seasonal unit roots within the context of a structural time series model. Such a model is often found to be useful in practice. Using Monte Carlo simulations, we show that our method works well. We illustrate our approach for several quarterly macroeconomic time series variables.     

Ecocultural Attributes: Evaluating Ecological Degradation in Terms of Ecological Goods and Services Versus Subsistence and Tribal Values

It is becoming increasingly clear that scientists, managers, lawyers, public policymakers, and the public must decide how to value what is provided by, and is a consequence of, natural resources. While “Western” scientists have clear definitions for the goods and services that ecosystems provide, we contend that these categories do not encompass the full totality of the values provided by natural resources. Partly the confusion results from a limited view of natural resources derived from the need to monetize the value of ecosystems and their component parts. Partly it derives from the “Western” way of separating natural resources from cultural resources or values, and partly it derives from the false dichotomy of assuming that ecosystems are natural, and anything constructed by man is not natural. In this article, we explore the previous assumptions, and suggest that because cultural resources often derive from, and indeed require, intact and unspoiled natural ecosystems or settings, that these values are rightly part of natural resources. The distinction is not trivial because of the current emphasis on cleaning up chemically and radiologically contaminated sites, on restoration of damaged ecosystems, on natural resource damage assessments, and on long‐term stewardship goals. All of these processes depend upon defining natural resources appropriately. Several laws, regulations, and protocols depend upon natural resource trustees to protect natural resources on trust lands, which could lead to the circular definition that natural resources are those resources that the trustees feel they are responsible for. Where subsistence or tribal peoples are involved, the definition of natural resources should be broadened to include those ecocultural attributes that are dependent upon, and have incorporated, natural resources. For example, a traditional hunting and fishing ground is less valued by subsistence peoples if it is despoiled by contamination or physical ecosystem degradation; an Indian sacred ground is tarnished if the surrounding natural environment is degraded; a traditional homeland is less valued if the land itself is contaminated. Our argument is that intact natural resources are essential elements of many cultural resources, and this aspect requires and demands adequate consideration (and may therefore require compensation).